KR20180040579A - Graphene reinforced polyethylene terephthalate - Google Patents

Abstract

Compositions and methods for graphene reinforced polyethylene terephthalate (PET) are provided. Graphene nano plastic (GNP), including multilayer graphenes, is used to reinforce PET, improving the properties of PET for a variety of new applications. Master-batches comprising dispersed graphene nano platelets and polyethylene terephthalate are obtained by blending. The master-batch is used to form PET-GNP nanocomposites in a weight fraction ranging from 0.5% to 15%. In some embodiments, PET and GNP are melt blended by biaxial-screw extrusion. In some embodiments, ultrasonic waves are coupled with the biaxial-screw extruder to aid in melt compounding. In some embodiments, the PET-GNP nanocomposite is produced by high speed injection molding. PET-GNP nanocomposites are compared by their mechanical, thermal and rheological properties to contrast different compounding processes.

Description

Graphene reinforced polyethylene terephthalate

preference

This application claims the benefit of U.S. Patent Application No. 15 / 203,668, filed July 6, 2016, and U.S. Patent Application Serial No. 15 / 073,477, filed March 17, 2016, US Provisional Serial No. 62 / 134,482 entitled " Injected Molded Poly (Ethylene Terephthalate) -Graphene Nanocomposites ", filed July 8, 2015, US Provisional Application Serial No. 62 / 190,193 entitled " Graphene Reinforced Polyethylene Terephthalate ".

Field

The field of the present disclosure generally relates to polymer composites. More particularly, the field of the invention relates to compositions and methods for graphene reinforced polyethylene terephthalate.

Composites are defined as multiphasic materials, which can be found in nature or synthesized. The synthesized composites are typically compounded using one or more materials to achieve properties that are not individually available. The composite can be classified based on the type of dispersed phase and continuous matrix, such as stiffeners. On the constituent, a composite material in which one of the predominantly dispersed phases has at least one dimension on the order of 1-100 nanometers is referred to as "nanocomposite ". Nanocomposites can be further classified based on the category (eg, organic or inorganic) as well as the geometry of nanoscale reinforcements. A few well-known examples of natural nanocomposites include human bones, shells, webs, and armored fish. As will be appreciated, each of these materials includes a structural layer (a structure of a multi-length scale), thereby making it exceptionally well functioning as compared to other materials of similar chemistry.

The material properties of the composite are known to be dependent on the interaction between the matrix and the dispersed stiffener. Because of the large surface area per unit volume of nanoscale, nanomaterials generally function differently from their bulk counterparts. Due to the increased interaction between the matrix and the dispersed phase, nanocomposites are considered to be relatively better than conventional composites and advantageously provide new properties without sacrificing existing beneficial properties, such as strength or durability.

Polyethylene terephthalate (PET) is an aromatic semi-crystalline thermoplastic polyester synthesized in the early 1940s. PET is well known for its strength and toughness, high glass transition temperature and melting point, chemical resistance, and optical properties. PET is also commonly used for commodity and engineering applications because it is also relatively inexpensive. PET is characterized by microstructures that form strong fibers with high molecular chain orientations when stretched in the longitudinal direction and also form strong films upon biaxial elongation. Linear PET is semicrystalline in nature. The thermal and mechanical history, such as cooling and elongation rates, can lead to PET to be amorphous or more crystalline, respectively, and affect its mechanical properties. PET is used in industries such as the textile, packaging, filtration and thermoforming industries, but limits its use due to its slow crystallization rate and limited barrier performance as compared to other polyesters such as PBT, PTN and the like.

As has been appreciated, since we have long felt the need to develop lightweight materials for use in a wide range of industries, such as packaging, automotive and aerospace, we are encouraging attempts to improve material properties through better control of the addition of reinforcements and material processing . For example, increasing the crystallinity of PET improves its mechanical and barrier properties. However, attempts have been made to improve the material properties of PET, due to stretching processes, and limitations due to industrial processes in maximizing crystallinity, such as cooling rate, cycle time, and by materials such as crystallization rate. However, advances in the nanomaterials field have led to the development of PET nanocomposites that improve the physical properties of PET, making it more effective for applications in the automotive, aerospace and protective apparel industries. Different types of nano-reinforcing materials (clay, CNF, CNT, graphene, SiO _2, etc.) can be made of PET materials such as PET's mechanical properties, thermal properties, barrier properties, electrical properties, flame retardancy, optical properties, surface properties, Lt; / RTI >

The release of nanostructures into individual entities and their uniform dispersion into the polymer matrix is essential for the success of polymeric nanocomposites. The uniform dispersion of the nanostructures in the polymer can be achieved by a variety of approaches including, but not limited to, melt-blending, in-situ polymerization, surface treatment, etc. of the nanostructures. Carbon nanomaterials such as carbon nanofibers, carbon nanotubes (CNTs) and graphenes are generally advantageous due to their excellent material properties and simple chemistry. Various characterization improvements can be achieved through dispersion of the carbon nanomaterial into the polymer.

Graphene is a relatively new nanomaterial that contains a single layer of carbon atoms similar to non-jiped single-walled carbon nanotubes. Single layer graphenes are generally twice as effective as CNTs in reinforcing polymers because graphene has two surfaces for polymer interaction whereas CNT contains only one outer surface for polymer interaction Because. It will be appreciated that graphene has become commercially viable due to the development of graphene synthesis methods with the introduction of novel graphene-based nanomaterials such as graphene oxide, expanded graphite, and graphene nano-sopan. However, due to limited information on the effectiveness of graphene-based nanomaterials, its application in producing polymer nanocomposites is limited. Therefore, it is necessary to investigate the influence of the graphene nanomaterial on reinforcing the polymer.

Melting-compounding and in-situ polymerization were the most studied techniques in the production of PET-graphene nanocomposites. Intramolecular polymerization is effective for dispersing graphenes, but limits the use of intramolecular polymerization due to the difficulty of achieving the desired molecular weight and the need for expensive reactors. Melting-blending has not been found to be a direct approach involving shear mixing, but alone it is effective in dispersing graphene in various polymer systems tested. As will be appreciated, achieving homogeneous dispersion of nanosplanin in PET is important for improving bulk properties. However, it is not simple to disperse graphene in PET since PET is generally highly viscous (500 - 1000 Pas) with a melting temperature of 260 ° C to 280 ° C. Therefore, it is necessary to select a process that can enable work at high temperatures for high viscosity materials.

Another important aspect for the implementation of polymeric nanocomposite applications is the ability to predict their material properties in order to provide flexibility in manufacturing process design and to reduce development costs. Conventional composite models are not accurate in predicting the properties of nanocomposites. Although continuum theory-based micromechanical models have been found to be effective in estimating short fiber composites, few studies have reported the applicability of these models to nanocomposites.

Therefore, there is a need for an effective way to uniformly disperse graphene nanofibre (GNP) in PET to provide reinforced bulk PET, and a micromechanical model that can predict the material properties of reinforced bulk PET.

In the drawings, reference is made to the embodiments of the present disclosure:
1 is a chemical formula illustrating the molecular structure of polyethylene terephthalate in accordance with the present disclosure;
Figure 2 is a graph illustrating the relationship between particle interface and size in accordance with the present disclosure;
Figure 3 is a table listing properties of graphene obtained through different methods in accordance with the present disclosure;
Figure 4 illustrates the unique structure of a carbon allotrope according to this disclosure;
Figure 5 is a micrograph illustrating carbon black nanoparticles used to reheat PET performance in accordance with the present disclosure;
Figure 6a is a micrograph of graphene nanosplane in accordance with the present disclosure;
Figure 6b is a micrograph illustrating the presence of multiple nano-platelet aggregates in accordance with the present disclosure;
Figure 7 is a chemical formula illustrating the molecular structure of nanosplan (xGnP) according to the present disclosure;
Figure 8 is a table listing properties of PET and master-batch pellets in accordance with the present disclosure;
9 is a schematic diagram illustrating an ultrasonic assisted biaxial-screw extrusion system in accordance with the present disclosure;
10 is a schematic diagram illustrating a process for the preparation of an ethylene glycol-graphene nano platelet according to the present disclosure;
11 is a schematic diagram illustrating a reactor apparatus for an ester interchange step in accordance with the present disclosure;
12 is a chemical formula illustrating the ester interchange reaction between dimethyl terephthalate (DMT) and ethylene glycol (EG) to form a PET monomer in accordance with the present disclosure;
13 is a schematic diagram illustrating a reactor apparatus for a polycondensation step in accordance with the present disclosure;
14 is a chemical formula illustrating the formation of a PET polymer chain from a monomer according to the disclosure;
14A is a table listing reaction time and methanol yield for each polymerization batch in accordance with the present disclosure;
15 is a cross-sectional view illustrating an injection molded compatible tensile specimen in accordance with the present disclosure;
Figure 16A illustrates PET and master-batch pellet blend from a feed throat 0.6% load from set B machining according to this disclosure;
Figure 16B illustrates a PET and master-batch pellet mix for a 0 USM sonicated batch in accordance with the present disclosure;
Figure 16c is a table listing details of PET nanocomposite samples obtained by injection molding in accordance with the present disclosure;
16D is a table illustrating a comparison of process pressures between PET and nanocomposites from an ultrasound master-batch in accordance with the present disclosure;
Figures 17A and B illustrate visual signals of poor mixing observed for 0.5% GNP nanocomposites in accordance with the present disclosure;
Figure 18A illustrates a micro-composer with a co-rotating biaxial screw according to the present disclosure;
Figure 18b illustrates a micro-injection molding system and delivery device in accordance with the present disclosure;
Figure 19A illustrates a double dog bone mold used for the manufacture of a tensile sample in accordance with the present disclosure;
Figure 19b illustrates a PET tensile rod molded according to the present disclosure;
Figure 19c is a table listing process parameters for a tensile rod made by a micro-injection molding system in accordance with the present disclosure;
20 is a schematic diagram illustrating a capillary viscometer in accordance with the present disclosure;
Figure 21A illustrates testing of nanocomposite tensile rods in accordance with the present disclosure;
Figure 21b illustrates a tube test fixture in accordance with the present disclosure;
Figure 21c illustrates a tube test in accordance with the present disclosure;
Figure 21d illustrates testing of a tension bar from a micro-injection molding system in accordance with the present disclosure;
Figure 22 is a schematic diagram illustrating a parallel plate geometry and polymer melt in accordance with the present disclosure;
Figure 23 is a schematic diagram illustrating a sample geometry associated with a device geometry involving a 2D X-ray diffraction frame in accordance with the present disclosure;
Figure 24 illustrates the location of samples collected for nano-tomography and diffraction analysis in accordance with the present disclosure;
25 is a schematic diagram illustrating a CT scanner and process for X-ray computed tomography in accordance with the present disclosure;
26 is a graph illustrating the weight average molecular weight of PET and PET nanocomposite pellets in accordance with the present disclosure;
Figure 27 is a graph illustrating intrinsic viscosity measured for PET and ultrasound treated PET in accordance with the present disclosure;
28 is a graph illustrating a comparison of intrinsic viscosities for PET and PET nanocomposites in accordance with the present disclosure;
29 is a graph illustrating the viscosity of a pellet obtained by in situ polymerization according to the present disclosure;
Figure 30 is a graph illustrating engineering stress-strain curves for PET and PET-GNP nanocomposites in accordance with the present disclosure;
31 is a graph illustrating the zero modulus and tensile strength of the nanocomposite tensile rods in accordance with the present disclosure;
Figure 32A illustrates a PET tensile rod in accordance with the present disclosure;
Figure 32b illustrates a PET-15% GNP tensile bar after testing in accordance with the present disclosure;
Figure 32C illustrates PET-GNP tensile tube elongation and brittle fracture in accordance with the present disclosure;
33 is a graph illustrating the modulus and tensile strength of PET and nanocomposite tensile tubes in accordance with the present disclosure;
34 is a graph illustrating an engineering stress-strain curve of a nanocomposite tensile tube compared to a tensile rod in accordance with the present disclosure;
35 is a graph (horizontal axis-ultrasound amplitude) illustrating the Young's modulus and tensile strength of the sonicated PET compared to the PET control in accordance with the present disclosure;
Figure 36 is a graph (horizontal axis-ultrasonic amplitude) illustrating the ultimate tensile strength of the sonicated PET compared to a PET control in accordance with the present disclosure;
37 is a graph illustrating the modulus and strength of an ultrasonicated nanocomposite with 2% GNP in accordance with the present disclosure;
Figure 38 is a graph illustrating the modulus and strength of an ultrasonicated nanocomposite with 5% GNP compared to a PET control and biaxial-screw-blended nanocomposite according to the present disclosure;
Figure 39 is a graph illustrating zero-modulus and intensity data for in situ polymerized PET and nanocomposites in accordance with the present disclosure;
39A is a table listing tensile strength and non-tensile strength for nanocomposite tensile rods in accordance with the present disclosure;
Figure 39b is a table listing the tensile and non-tensile strengths for nanocomposite tensile tubes in accordance with the present disclosure;
39c is a table listing the tensile strength and the intrinsic strength of a nanocomposite tube from an ultrasonic master-batch in accordance with the present disclosure;
40A is a micrograph illustrating voids on the rupture surface of a nanocomposite tensile rod with a 5% GNP weight fraction in accordance with the present disclosure;
40B is a micrograph illustrating voids and crack initiation sites on the burst surface of a nanocomposite tensile rod with a 10% GNP weight fraction in accordance with the present disclosure;
41A is a micrograph illustrating a burst surface of a 2% nanocomposite tensile tube having an enhancement region presenting a signal of "soft burst" in accordance with the present disclosure;
FIG. 41B is a micrograph illustrating the breakage of a microfibril formed from the in-emphasis stretch of FIG. 41A in accordance with the present disclosure; FIG.
Figure 42A is a micrograph illustrating a nanocomposite tensile bar failure surface at 2% weight fraction in accordance with the present disclosure;
Figure 42b is a micrograph illustrating nanocomposite tensile bar failure surfaces at 5% weight fraction according to this disclosure;
42c is a micrograph illustrating a nanocomposite tensile bar failure surface at 10% weight fraction in accordance with the present disclosure;
42D is a micrograph illustrating a nanocomposite tensile bar failure surface at 15% weight fraction according to the present disclosure;
43 illustrates an ultrasonic micrograph of a PET and PET nanocomposite tensile rod in accordance with the present disclosure, wherein the arrows indicate the direction of injection flow;
Figure 44 is a graph illustrating the GNP weight fraction to glass transition temperature (T _g ), melting temperature (T _m ) and crystallization temperature (T _c ) under an error for temperature measurement of 0.5 ° C in accordance with the present disclosure;
Figure 45 includes a left graph illustrating the crystallization half-life of a PET nanocomposite measured in 0.05 min in accordance with the present disclosure, and a right graph illustrating percent crystallinity of a PET nanocomposite;
46 is a graph illustrating crystallization exotherms for PET and biaxially-screw-blended PET nanocomposite pellets in accordance with the present disclosure;
Figure 47 includes a graph illustrating glass transition and melting temperatures for ultrasound treated PET and PET nanocomposite pellets in accordance with the present disclosure;
Figure 48 is a graph illustrating the melting curve (second column) of ultrasonically treated PET in accordance with the present disclosure;
Figure 49 shows the left graph illustrating the crystallization half-life (measured in 0.05 min) for sonicated PET and PET + 5% GNP pellets in accordance with the present disclosure, and for the sonicated PET and PET + 5% GNP pellets A right graph illustrating crystallinity;
Figure 50 includes a graph illustrating the crystallization temperature and percent crystallinity for a sample polymerized in situ in accordance with the present disclosure;
51 is a graph illustrating the storage modulus of PET and PET nanocomposites for each frequency in accordance with the present disclosure;
52 is a graph illustrating shear modulus versus GNP weight fraction and proposed percolation threshold in accordance with the present disclosure;
53 is a graph illustrating the storage modulus of an ultrasonic nanocomposite as compared to PET and biaxial-screw nanocomposites in accordance with the present disclosure;
54 is a graph illustrating dynamic sweep of storage modulus for different PET samples in accordance with the present disclosure;
55A illustrates a transmission micrograph of a 15% nanocomposite in accordance with the present disclosure;
Figure 55b illustrates a transmission microscope photograph of a 15% nanocomposite in accordance with the present disclosure;
Figure 56A illustrates a transmission microscope photograph of a 5% nanocomposite presenting minority layer graphene in accordance with the present disclosure;
Figure 56b illustrates a transmission microscope photograph of a 5% nanocomposite presenting minority layer graphene in accordance with the present disclosure;
Figure 57a illustrates a transmission electron micrograph of a 15% PET-GNP nanocomposite in accordance with the present disclosure;
57B is a photograph of a binarized microscope suitable for analysis of interparticle distance in accordance with the present disclosure;
58 is a graph illustrating intergranular distance versus GNP weight fraction with dashed lines representing a comparison of theoretical trends and experimental data in accordance with the present disclosure;
Figure 59 is a graph illustrating an X-ray diffraction pattern for GNP, PET and nanocomposite tensile rods according to the present disclosure;
60A illustrates an X-ray diffraction scan along a cross-section of a PET tensile rod in accordance with the present disclosure;
FIG. 60B is a graph illustrating the X-ray diffraction pattern of the line diffraction scan of FIG. 60A according to the present disclosure; FIG.
61 is a graph illustrating an X-ray diffraction pattern at multiple depths in a 3-mm thick 15% nanocomposite tensile rod in accordance with the present disclosure;
62A illustrates a reconstructed 3D volume of a 15% nanocomposite with a boundary size of 240 μm × 240 μm × 163 μm according to the present disclosure;
62B illustrates a nano-platelet in the nanocomposite of FIG. 62A indicating the orientation of the platelet along the direction of injection (Z-axis) in accordance with the present disclosure;
63A illustrates a sample mounted on a rotating pin in accordance with the present disclosure, wherein the cross mark indicates an injection flow direction;
63B illustrates the distribution of nanosplan from within the edge of a 2% nanocomposite tensile tube in accordance with the present disclosure;
Figure 64 is a graph illustrating Raman bands corresponding to CC stretching for PET and PET nanocomposites in accordance with the present disclosure;
65 is a graph illustrating movement in the Raman band corresponding to CC stretch with increasing GNP weight fraction in accordance with the present disclosure;
66 is a graph illustrating the predictive modulus of a PET-graphene nanocomposite as compared to experimental results in accordance with the present disclosure;
66A is a table listing characteristics of GNP and PET for micromechanical model based prediction in accordance with the present disclosure;
67 is a graph illustrating a comparison of nanocomposite experimental behavior and theoretical predictions in accordance with the present disclosure, where E _m is a matrix modulus, E _r is a GNP modulus, A _f is an aspect ratio (diameter / thickness);
68 is a graph illustrating the load extension curve for ultrasound PET compared to a PET control in accordance with the present disclosure;
Figures 69a and b are schematic diagrams illustrating doubling of the same size nano-platelet affecting the polymer matrix in accordance with the present disclosure;
Figure 70 is a graph illustrating the increase in elastic tensile modulus versus GNP weight fraction in accordance with the present disclosure;
71 is a graph illustrating the elastic region of the stress-strain curve for a nanocomposite tensile rod in accordance with the present disclosure;
72 is a graph illustrating a comparison of the Young's modulus for PET-GNP nanocomposites with and without ultrasound treatment in accordance with the present disclosure;
While various changes and alternative forms are known to the teachings herein, specific embodiments thereof have been shown by way of example in the drawings and will be described in detail herein. It is intended that the invention not be limited to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the disclosure.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be apparent to one of ordinary skill in the art that the invention disclosed herein may be practiced without these specific details. In some cases, specific values such as "first process" may be mentioned. It should be understood, however, that specific numerical references should not be interpreted as a literal sequential order, but rather that the "first process" is different from the "second process ". Accordingly, the specific details described are merely exemplary. The specific details may vary and still be considered within the spirit and scope of this disclosure. The term "coupled" is defined as meaning that it is connected directly to the component or indirectly through another component to the component. Additionally, as used herein, the terms " about, "" about, " or "substantially ", to any numerical value or range, refer to a portion of a component functioning for its intended purpose, Indicates an appropriate dimensional tolerance to allow aggregation.

In general, the present disclosure provides compositions and methods for graphene reinforced polyethylene terephthalate (PET). Graphene nano plastic (GNP), including multilayer graphene, is used to reinforce PET, improving the properties of PET for a variety of new applications. Master-batches comprising dispersed graphene nano platelets and polyethylene terephthalate are obtained by blending. The master-batch is used to form PET-GNP nanocomposites in a weight fraction ranging from 0.5% to 15%. In some embodiments, PET and GNP are melt blended by biaxial-screw extrusion. In some embodiments, ultrasonic waves are coupled with the biaxial-screw extruder to aid in melt compounding. In some embodiments, the PET-GNP nanocomposite is produced by high speed injection molding. PET-GNP nanocomposites are compared by their mechanical, thermal and rheological properties to contrast different compounding processes.

Polyethylene terephthalate (PET) is an aromatic semi-crystalline polyester. PET is synthesized through condensation polymerization using dimethyl terephthalate (DMT), ethylene glycol (EG), or terephthalic acid (TPA) and ethylene glycol (EG) as raw materials. In order to achieve the desired molecular weight and minimize the formation of by-products (e.g., acetaldehyde), a multi-step polymerization process is used in the production of PET. The molecular structure of PET is shown in Fig. As will be appreciated, the presence of a rigid aromatic ring in the molecular chain not only causes a high melting temperature and a glass transition temperature, but also stiffens the polymer. In addition, the rigid aromatic rings also provide a nearly planar arrangement in the molecules in the crystal structure. The combination of physical properties and chemical inertness makes PET suitable for applications such as fiber, packaging, and engineering molding.

PET is limited in terms of crystallization speed and barrier performance, but due to the relatively low price of PET, it is of interest to improve the material properties of PET by the addition of fillers and reinforcing materials. The nanomaterial minimizes changes in the density of the resulting composite material while providing the advantage of reinforcing the PET.

Nano stiffener

Nano-stiffeners are generally classified into three different groups based on their geometry: nanoparticles, nanotubes, and nano-small plates. Nano-stiffeners are advantageous over larger stiffeners. It will be appreciated that the smaller the particle, the stronger and more effective the particle is in reinforcing the matrix as compared to the larger counterpart. Another advantage is the effective surface area for the unit volume. In the case of spherical particles, for example, the ratio of surface area to volume is inversely proportional to the particle radius. Figure 2 illustrates the increase in interface for different types of stiffeners whose size ranges from micro-scale to nanoscale. As shown in Fig. 2, the effective surface energy per unit area will be high for nanoparticles, so they are chemically active.

It will be appreciated that the choice of nanostructure depends on many factors such as the polymer used, the intended application, the desired properties, the desired form interaction with the polymer, the material handling problem, the processing method as well as the cost. In addition, it will be appreciated that the shape of the nanosubstrate affects the characteristics of the polymer nanocomposite.

Nanoparticles can be classified as organic or inorganic based on their chemistry. Metal nanoparticles (e.g., Al and Ag), metal oxides (e.g. Al ₂ O ₃ , ZnO and silica), cellulose nanocrystals, and carbon derivatives (CNTs, fullerenes, graphite oxides , And graphene) have been used in polymeric nanocomposites.

Carbon nano stiffeners and graphene

Carbon is an interesting periodic table element that possesses the manipulability and inherent hybridization properties of its structure. Carbon is commonly found in various industrial and process applications in the form of graphite, amorphous carbon, and diamond. At the nanoscale, carbon materials such as those shown in Fig. 4, such as fullerenes, carbon nanotubes (CNTs), and graphenes, are interesting and suggest unique properties and structures.

Graphene is defined as a monolayer of carbon atoms with a two-dimensional structure (sp ² hybridization, plane hexagonal arrangement with a CC coupling distance of 0.142 nm). The thickness of the single graphene sheet is estimated to be substantially 0.335 nm. Graphene, one of the first two-dimensional materials available, has the potential to replace many of the modern materials used for different applications. During graphene research, researchers have found that different graphene-based materials such as single-layer graphene sheets (SLGS), fraction-layer graphene (FLG), multilayer graphene (MLG), and exfoliated graphene platelets .

Graphene is superior to other carbon-based nano-reinforcing materials such as CNT, CNF, and expanded graphite (EG) in terms of aspect ratio, flexibility, transparency, thermal conductivity and low thermal expansion coefficient (CTE). The density of the single layer graphene was calculated to be 0.77 mg m ^-2 . Graphene is regarded as the strongest material at the perceptible size. (0.02 TPa for 4130 steel) and a strength of 130 10 GPa (0.7 GPa for 4130 steel) of 1.02 0.03 TPa for an atomic force microscope (AFM) nano It was measured by indentation technique. Graphene exhibits a very high thermal conductivity (K) of 3000 W mK ^-1 comparable to CNT and a negative thermal expansion coefficient α = -4.8 ± 1.0 × 10 ^-6 K ^-1 at a temperature range of 0-300 K It turned out. In addition, the graphene sheet was found to be hydrophobic and have a surface energy of 46.7 mJ m < ^-2 > at room temperature.

The above-mentioned properties are for high quality single-layer graphene sheets. The properties of multi-layer graphenes are different from those of single-layer graphenes. Thus, the number of layers containing graphene ("n") affects the properties of graphene. The single layer graphene sheet exhibits a maximum of 97.7% transparency (2.3% absorbency), which decreases linearly as the number of layers increases. The thermal conductivity of graphene has been shown to fall by more than 50% as the number of layers increases from 2 to 4 and to be comparable to bulk graphite when the number of layers is greater than 8. In addition, the modulus of the graphene sheet decreased with increasing temperature and with increasing ¹³ C isotope density, but increased with increasing number of layers. However, it will be appreciated that the structural dynamics-based atomic modeling of the multi-layer graphene structure, the molecular simulation of the sharing between layers and van der Waals interactions, and experimental measurements represent a decrease in modulus with a decrease in the number of layers. The mechanical properties such as stiffness and Poisson's ratio of graphene nanofabrication have been shown to decrease with increasing number of layers, based on molecular dynamics simulations. The stiffness of the nanosplane containing five layers was estimated to be reduced by 15% when compared to the single layer graphene, and the properties of the graphenes are different based on the orientation. The effective Young's modulus of multi-layer graphenes comprising ten layers is shown to be substantially 380 GPa, which is less than the effective Young's modulus of the graphite crystal. The effective Young's modulus is determined based on the stress transfer efficiency between the layers in the multi-layer graphene. Effective Young's modulus deviates from the modulus of the single layer graphene when the multi-layer graphene has more than three layers, in which case the core layer (s) will not contact the polymer.

The single layer graphene can be obtained by a "top-down" or "bottom-up" approach. The separation of graphene sheets from graphite through mechanical cutting is a "top-down" approach. The graphenes obtained from this process are clean and useful for testing purposes, but are not practical for obtaining significant amounts of graphene. Alternatively, graphene can be produced by chemical methods such as chemical vapor deposition (CVD), epitaxial growth as well as a "bottom-up" approach where synthesis via a colloidal suspension is used. In addition, graphene can also be produced from CNTs by flash reduction of graphite oxides and by chemical etching. The approach used to obtain graphene affects the physical properties of graphene as shown in Figure 3, allowing graphene to target different applications.

Processing of nanocomposites

Composite fabrication has been extensively explored using several processes that are valid based on the application and size of the final product. Nanocomposite processing involves processes for the dispersion of nanostructured materials and for the intended final application. The validity of nanocomposites depends largely on cost, availability of nanoparticles, and suitable manufacturing processes. Manufacturing techniques such as injection and compression molding, layer-by-layer (LBL) fabrication, in-situ micro-emulsion polymerization, and spinning have been used for polymeric nanocomposites. As will be appreciated, the choice of manufacturing process depends on the type of nanoparticles used and the matrix resin. In addition, injection molding will be understood to be the most important of all plastic processing techniques due to its speed, expandability, and tolerances for a wide range of materials. Attempts to achieve uniform dispersion of nanostructures in polymers are discussed in the following section.

Dispersion of nano-stiffeners

Achieving uniform and homogeneous dispersion, or "peeled" states, of nanostructures is essential for the success of polymeric nanocomposites. Nanomaterials have high surface energies per unit area, and they tend to form aggregates to minimize such energy. Due to the tendency to aggregate, it becomes difficult to disperse the nanomaterial into the polymer matrix and maintain the nanoscale of the nanomaterial at an effective dimension. The dispersion of the nanostructured material into the molten polymer depends on such factors as the viscosity of the melt, the wettability of the stiffener, the energy imparted through the mixing process, such as the breaking of the aggregates, and the efficiency of the mixing process. The dispersing methods can be broadly classified as mechanical-based and chemically-based. Several methods of dispersion have been investigated in the mechanical-based category such as melt blending, master-batch processing, ultrasonic-assisted blending, chaos advection blending, solid state shear pulverization (SSSP), solid state ball milling (SSBM) . These dispersion methods can further be classified as "melt mixing" or "solid state mixing ".

Melt blending is the most commonly used technique for dispersing nanostructures in thermoplastic polymers. As described herein, nanostructures were dispersed into the molten polymer by the action of a uniaxial or biaxial-screw extruder. Solid state shear pulverization (SSSP) is another mechanical blending technique developed for the blending of incompatible polymers. However, distortion of the nanosplan during the screw mixing process is a problem that can reduce its effectiveness. Some other techniques described above involving solid state mixing are SSBM and acoustic mixing. In SSBM, the nanoparticles and polymer blend are milled to a fine powder and then used as an input for the secondary process. Acoustic mixing is based on the occurrence of a uniform shear throughout the mixing chamber for high-efficiency mixing.

A chemical approach to preventing aggregation is to modify the surface of nanoparticles and to functionalize the surface, which reduces surface energy and changes its polarity to prevent aggregation. Through functionalization, the surface of the nanoparticles is coated with an ion or molecule (i.e., a surfactant) that is compatible with the particular polymer. Since all polymers have different chemistries and structures, it is important to choose the right functionality.

There are also solvent mixing techniques, such as sol-gel processing, solution mixing, sonication, shear mixing, and high speed mixing. These techniques are mainly useful for work using thermosetting resins and low temperature thermoplastic materials. They are primarily for batch processing and raise handling and consistency issues in large-scale processing.

In a biaxial-screw extruder, the polymer undergoes shear deformation and melts between the two rotating screws and the housing. As the nano platelets are bonded with van der Waals forces, they can be separated by application of shear force during mixing. Shearing and mixing of the stiffener and the polymer melt can be achieved through retention of a large length-to-diameter ratio (L / D) of the mixing screw and by the application of different screw elements. Using this, biaxial-screws have been used in formulations for decades. After its introduction into polymer processing, different types of biaxial-screw extruders have been developed. The basic difference is based on the direction and shape of the screw rotation. There are co-rotating, reversing-rotating, and intermeshing screws. In order to increase the efficiency of the mixing, segmented screws with different interchangeable elements (for example kneading elements) have also been developed. Nanocomposites show similar performance regardless of the screw rotation type, but it has been found that using a reverse-rotating screw results in better dispersion. In addition, the flow rate in the co-rotating screw was found to be higher at the screw tip. This corresponds to a higher shear rate and is considered suitable for mixing.

As will be appreciated, the melt compounding process is the most convenient and industrially promising process for producing polymeric nanocomposites. Master-batch mixing is a multi-stage approach in which already mixed polymer-nanostructure pellets are re-melted and mixed at the same or reduced loading rate. It will be appreciated by those skilled in the relevant art that master-batch mixing is typically used during polymer processing to add specialized additives or dyes during primary processing, such as injection molding and extrusion. Master-batch pellets are prepared using the same or compatible base resin and additives at high loading rates. In addition, nanocomposites from the master-batch process have been found to be superior to those obtained by melt processing. Secondary mixing has helped to improve the performance of nanocomposites through increased dispersion.

In the case of ultrasonic-assisted extrusion with biaxial-screw mixing, additional energy is applied in the form of ultrasonic waves. Ultrasonic energy is used as an initiator for preparing thermodynamically unstable emulsions and for polymerization reactions. As will be appreciated, nanoparticle dispersion can be improved by the combination of ultrasonic and biaxial-screw extrusion. The ultrasonic energy applied to the polymer-nanoparticle mixture will cause cavitation due to the local expansion of the high temperature zone. As the bubbles grow, they help separate and break the nanoparticles into the polymer matrix.

Dispersing single-layer graphene into polymers has been of interest to researchers for quite some time. Graphene is generally difficult to wet and exhibits lower adhesion energy than graphite and graphene oxide. To improve the adhesion and reactivity of graphene for specific applications, graphene sheets can be functionalized on both surfaces. The functionalized graphene is particularly useful for bio-sensing applications. In some studies, the effect of fluorination on graphene sheets has been studied. The resulting fluorographene was found to be an insulator with similar thermal and mechanical behavior to graphene.

Solvent dispersion of graphene received much attention through the successful dispersion of graphene in organic solvent N-methyl-2-pyrrolidone (NMP). In some studies, the effectiveness of different solvents in the degradation of graphene through sonication has been studied. Graphene has been shown to be dispersible in water at high concentrations (0.7 mg / ml) by using a combination of low power and high power ultrasound treatment and a surfactant (Triton X-100). Since graphene is hydrophobic, application of dispersants having hydrophobic and hydrophilic ends will help stabilize dispersion in aqueous solvents. The strong π-π interaction between the aromatic structure of the graphene sheet and the benzene ring in the surfactant (Triton X-100) supports dispersion. Aqueous dispersed graphene obtained through a size-selective approach (i.e., selecting graphene of uniform diameter through centrifugation) appears to be a promising direction for the preparation of polymeric nanocomposites. Nonetheless, the complexity and cost of the approach can limit the above path for commercial applications.

The adhesion and wettability of graphene were found to be higher for ethylene glycol (EG) compared to water. In addition, the reduced graphene oxide can be well dispersed in ethylene glycol due to the presence of oxygen-containing functional groups. Due to ethylene glycol, which is one of the raw materials for the polymerization of PET, solution dispersion becomes a suitable route for the development of nanocomposites.

PET nanocomposite

As described at the beginning, PET nanocomposites are pursued for the purpose of improving its properties and expanding to new applications. Currently, other nanomaterials are already in use and are dispersed upon polymerization of PET. For example, as shown in FIG. 5, carbon black nanoparticles having an average diameter of 400 nm are used at 6 ppm or 0.0006% for improving the heat absorption capacity of PET. Carbon black dispersion achieved through in-situ polymerization provides energy savings even at such a low 6 ppm load. Investigating the production of nanocomposites through an in situ approach at a more significant weight fraction can help to understand the effectiveness of such an approach.

Due to the high melting temperature and melt viscosity of PET, melt-blending is a relevant technique for the production of nanocomposites. As described herein, the addition of graphene to PET has been found to improve the mechanical, barrier, thermal and conduction properties of PET. However, improvement in dispersion of graphene and understanding of the strengthening mechanism at high load is envisaged as a non-limiting example, leading to new applications such as strain monitoring, electromagnetic shielding, weakened impact protection, reduced water absorption, and the like.

Experiment details

In some embodiments, commercially available PET of molecular weight M _w 84,100 g / mol (0.81 dl / g intrinsic viscosity (IV)) can be obtained in the form of pellets. As provided, the PET pellets are semi-crystalline and can be identified by differential scanning calorimetry (DSC). As will be appreciated, PET is hygroscopic and the presence of water in the polymer melt will lead to loss of molecular weight through chain segmentation (hydrolysis of the ester bond). Thus, PET can advantageously be dried at 170 占 폚 for 4-6 hours before each step to minimize polymer degradation.

In some embodiments, commercially available graphene can be obtained in the form of graphene nano platelet (GNP) having two different average surface areas. In some embodiments, graphene nanosophene (GNP) (xGnP <> -M-5 grade) having an average diameter of 5 μm, a thickness of about 6 to 8 nm and an average surface area of 120-150 m ² / Can be used for manufacturing. In some embodiments, nanosplanes (xGnP®-C-750 grades) having an average diameter of 2 μm and an average surface area of 750 m ² / g can be used for in-situ polymerization. In some embodiments, the nanosplane is first in the form of a dry agglomerated powder, and each agglomerated platelet contains a plurality of nanosplants, as shown in Figures 6A-6B. As will be appreciated, nano-platelet is generally not uniform over its length and includes jig-jag edges. Figure 7 illustrates the chemical structure of the nano-platelet. The nano-platelet consists of 99.5% carbon, where very low oxygen and hydrogen are present in the form of carboxyl and hydroxyl groups on the edge. It will be appreciated that the carboxyl and hydroxyl groups are formed due to the exposure of the raw carbon during rupture of the platelets. In some embodiments, the nanosplane is prepared by reacting acid-implanted graphite flakes with microwave < RTI ID = 0.0 > (I) < / RTI > as described in the Doctor of Philosophy Dissertation, entitled "Graphite Nanoreinforcements in Polymer Nanocomposites, For example by the process of expanding by processing, the entire contents of which are incorporated herein by reference.

Manufacture of PET-GNP nanocomposites

In some embodiments, graphene nanosplan may be dispersed into the PET matrix without forming aggregates by blending the PET-graphene master-batch through a biaxial-screw and an ultrasonic-assisted biaxial-screw process. In one embodiment, graphene nano platelet (GNP) and PET resin were blended into PET-xGnP master-batch pellets using a Krauss-Mafi ZE-25 UTX laboratory extruder with a co-rotating screw. Two different master-batch pellet sets of 2%, 5%, 10% and 15% weight fractions were formulated using this process. In each set, a master batch of 5.4 kg (12 lb) was prepared for each weight fraction.

In some embodiments, ultrasonic waves may be used to aid in biaxial-screw mixing. In one embodiment, the PET-graphene nano platelet was processed using an ultrasonic-assisted biaxial-screw extrusion system. The PET pellets were dried overnight in an oven at 80 DEG C to remove water and then blended with a graphene nanofot plate in a 5% weight fraction. PET and graphene nano platelet were formulated using a co-rotating twin screw-screw micro-composer with an ultrasonic horn operating at 40 kHz as shown in FIG. The ultrasonic horn was located within the barrel region at a distance of 14.5 cm from the die inlet. The vertical position of the horn tip was adjusted to contact the polymer melt. With a set screw speed of 200 RPM, a flow rate of 0.9 kg / hr (2 lb / hr) was maintained throughout the process, resulting in a residence time of 9.2 seconds in the ultrasonic treatment zone.

A total of four master-batch sets were prepared, combined with a reference complex master-batch including different sonic amplitudes: Ultrasound (0 USM), 3.5 μm (3.5 USM), 5 μm (5 USM) and 7.5 μm 7.5 USM). In addition, to understand the effect of sonication on PET alone, pure PET (no reinforcement) was also treated under the same conditions. Figure 8 illustrates the size and dimensions of several exemplary embodiments of blended PET-graphene in pelletized form.

Polymerization in situ

In some embodiments, intramolecular polymerization can be used to prepare polymer nanocomposites. As will be appreciated, intramuscular polymerization generally involves two steps. The first step involves inserting nanoscale reinforcements onto the solution using an compatible polymer precursor or solvent. In the second step, the polymerization is carried out using the nanosplane-embedded solution. Dispersing the nanosplast into a chemically compatible low viscosity material is considered to be more efficient than direct mixing with the high viscosity polymer melt.

As will be understood, since ethylene glycol (EG) is one of the raw materials for the polymerization of PET, EG can advantageously be used as a solvent for the dispersion of graphene nanofibers. In one embodiment, reagent grade EG with 99% purity was used as the solvent for the dispersion of the graphene nanosplan. The graphene nanofabrication was added to the EG at a concentration of 1 mg / ml (i.e., 0.1% weight fraction) and ultrasonicated using a 40 kHz bass ultrasonic generator. The EG-GNP solution was sonicated for 106 hours to ensure homogeneous dispersion as shown in FIG. During the ultrasonic treatment process, the solution beaker was covered with aluminum foil to prevent exposure to atmospheric oxygen. The dispersions were prepared using both low (120 m ² / g) and high (750 m ² / g) surface area graphene nano plastic plates.

In one embodiment, in-situ polymerization of graphene nanofibers dispersed in ethylene glycol and dimethyl terephthalate was attempted using a 1 kg polymerization reactor. The PET polymerization was carried out through a two-step reaction. The first step is the ester interchange reaction (EI), in which the monomers are formed. In the second step, a polymer is formed through a polycondensation reaction (PC). An experimental apparatus used in conjunction with the reactions experienced in each step is described below.

11 is a schematic diagram illustrating an exemplary embodiment of a reactor and a methanol collection device for performing an ester interchange reaction. In the embodiment illustrated in Figure 11, powdered dimethyl terephthalate (DMT) was used for the polymerization. The EG, the dispersed GNP and the powdered DMT were charged into the reactor at a 2.3: 1 molar ratio with excess EG under nitrogen purge. The catalytic acetic acid manganese (Mn (CH ₃ COO) ₂ ) for the ester interchange reaction and the catalyst antimony trioxide (Sb ₂ O ₃ ) for the polycondensation reaction were added to the batch at 82 ppm and 300 ppm, respectively, 0.0 > 175 C. < / RTI > Methanol collection began when the batch temperature approached about 170 ° C, indicating that the ester interchange reaction was initiated and the nitrogen purge was finished. The batch temperature continuously increased to 15 ° C until the temperature reached 235 ° C. As the reaction progressed, the temperature in the gooseneck condenser increased from room temperature to over 60 ° C. If the methanol collection reaches the theoretical yield (300 ml in this case) and the gooseneck condenser temperature drops below 60 ° C, the ester interchange is considered complete. The gooseneck condenser was removed and polyphosphoric acid (H ₃ PO ₄ ) was added to the batch at 38 ppm to terminate the ester interchange reaction. Figure 12 illustrates the formation of ester interchanges via ester interchange between DMT and EG. The entire ester interchange reaction took about 3 hours to complete.

Figure 13 is a schematic diagram illustrating an exemplary embodiment of a reactor and an excess EG collection condenser apparatus for performing a polycondensation reaction. During the polycondensation reaction, the reactor temperature was increased to 285 DEG C and held at vacuum (30 in Hg) until PET of the desired viscosity was obtained. Isophthalic acid (C ₆ H ₄ (COH) ₂ ) and stabilized cobalt were added to the batch at the start of the polycondensation reaction at 20 grams and 65 ppm, respectively. It will be appreciated that the isophthalic acid limits the crystallinity of the PET, making it easier to process the PET. The stabilized cobalt was added to control the final color of the PET. As illustrated in FIG. 14, during the polycondensation reaction, the molecular weight of PET increases and EG is released. During the polycondensation reaction, the released EG was collected in a round flask and solidified using dry ice to prevent the EG from entering the vacuum pump. As will be appreciated, a change in the viscosity of the batch with increasing PET chain length will affect the stirring current. Thus, as the reaction progressed, the current sent to the agitator was monitored for change at 15 minute intervals. If no change in current sent to the agitator was detected in two consecutive readings, the vacuum was cut to stop the reaction. The resulting polymer melt was then extruded from an opening in the bottom of the reactor into an ice-water bath and pelletized using a strand chopper. Figure 14a illustrates reaction time and yield for three batch polymerizations, including one control batch without graphene nanofibres, which was performed by the apparatus illustrated in Figures 11 and 13.

Injection molding of nanocomposites

In some embodiments, the nanocomposite master-batch can be injection-molded with different final nano platelet loading fractions to facilitate investigation of its microstructural, mechanical, and thermal properties. In one embodiment, three different injection molding presses were used, including an oil-cooled molding machine, a water-cooled molding machine, and a micro-injection molding machine. The PET-graphene nanosoprene master-batch obtained from the compounding process described above was used for the formation of nanocomposites of different loading fractions. An oil-cooled injection molding unit was used to form nanocomposites of 2%, 5%, 10% and 15% GNP weight fractions from the master-batch (the blended pellets were blended with pure PET without dilution of the graphene concentration Injection molded). Tensile bars were formed at barrel temperatures ranging from 260 ° C to 280 ° C. A standard tension bar mold followed by ASTM D 638 Type I specification.

Due to the slow cooling rate, crystallization signals appearing as opaque cores in the injection molded PET were observed. In another embodiment, a HyPET 90 RS45 / 38 injection molding system designed for PET was used. Injection molding was performed remotely at a Niagara Bottling LLC facility in Ontario, CA. The HyPET 90 RS45 / 38 injection molding system has a clamping force of 90 tons and includes 38 mm screw diameter and cooling water cooled molds. This makes it possible to process the PET at a higher cooling rate to retain the amorphous microstructure.

In addition, an outer mold was developed to maintain the injection molding of the nanocomposite similar to the industry standard for processing PET. The tube specimen produced using the outer mold shown in Fig. 15 is designed for ease of mechanical testing. As shown in FIG. 15, the tube specimen includes a large gauge length with a uniform cross-section. Thus, the outsourcing mold manufactures parts that include typical associated sizes and processing windows (i.e., injection pressure and cycle time) for industrial scale components.

Using the nanocomposite pellets obtained from the above method, samples for mechanical testing were injection molded at different GNP concentrations. For testing of nanocomposites with low GNP weight fractions, the master-batch was diluted by mixing with PET and injection molded into a nanocomposite with a weight fraction as low as 0.5%. The final weight fraction of the nanocomposite was confirmed by measuring the percentage of pellets in the images collected from the feed throat, as shown in Figures 16a-16b. When using the dimensions of the pellets as listed in Figure 8, the actual weight fractions were calculated. The nanocomposites from each process run were collected for characterization studies after the process was stabilized. Stabilization occurs when the injection pressure and cycle time are fixed for more than 10 minutes. Figure 16c shows the injection molded nanocomposite weight fractions associated with each master-batch.

Process Optimization

Polymer processing through injection molding depends on a number of variables, such as barrel temperature, injection pressure, holding and back pressure, charge time, cooling time, and the like. As will be appreciated, balancing of all such variables is necessary to have components such as voids and no crystallinity. At the start of each process run, the barrel was flushed with a reference material to remove any residual material from the previous test. It will be appreciated that flushing in the reference material allows processing to be started at known conditions and allows it to be optimized when the PET-master-batch mixture occupies the barrel.

The addition of graphene nanofibers affects the melt viscosity of the PET, which will reflect the filling pressure. The maximum filling pressure was found to be reduced when machining the ultrasonic master-batch as shown in Figure 16d, while the holding pressure was observed to be the same. As will be appreciated, the retention pressure is important to keep the mold closed while the material is solidified. Another important process parameter is backpressure, which helps to homogenize the material and remove voids from the melt. The effectiveness of the process, the mixing of the PET, and the master-batch inside the barrel can be checked by visual inspection. For samples with lower GNP weight fractions, the visual signal of poor mixing includes dark spots, marks, and flowstreaks as shown in FIG.

Micro-injection molding

In some embodiments, the tensile sample can be prepared using a micro-injection system to assess the improvement from graphene dispersion via ultrasound without dilution and to examine the effect of ultrasonic treatment on PET mechanical properties. In one embodiment, a tensile sample was prepared using a 5.5 cc micro-injection molding unit with a 5 cc microformulation unit as shown in Figures 18a-18b. The micro-injection molding unit of Figs. 18A-18B including the mold shown in Fig. 19A was used for the manufacture of the tension rods shown in Fig. 19B. A micro-composer unit equipped with a co-rotating twin screw was used to melt the pellets and provide a homogenous molten mixture as described herein. The transfer device shown in Fig. 18B was used to transfer the polymer or nanocomposite melt from the composer to the injection molding machine. The injection molding machine injected the polymer material into the conical mold by means of a plunger connected to high pressure air (13.8 bar). As will be appreciated, the micro-injection system provides control of mold temperature, injection pressure, holding pressure, injection time and hold time.

In one embodiment, the double dog mold shown in Figure 19a was designed according to the ASTM D 638 Type V specimen L / D ratio for the gage compartment with a filling volume of 2.1 cc. During the compounding process, the material was heated to 270 DEG C and homogenized by opening the recirculation valve for 1 minute, after which the melt was collected into the delivery device. Tensile bars were prepared using aluminum molds at room temperature. A relatively large volume of aluminum mold served as a heat sink and allowed cooling of the polymer melt during injection. Figure 19c lists the injection process parameters used for the preparation of PET nanocomposite tensile rods.

A total of five different sets of materials, including materials from polymerization, nanocomposite pellets with a 5% GNP weight fraction from PET control, ultrasonicated PET, biaxial-screw mixing, ultrasonic assisted biaxial-screw mixing, System, and tensile bars were obtained for mechanical testing. For nanocomposites, different mixing times were also investigated to understand the effect of mixing time on nanocomposite properties. All materials were dried in a small amount (30 grams) at 170 占 폚 for 2 hours in an oven before processing to avoid over-drying due to viscosity drop, or decomposition due to the presence of moisture.

Characterization of nanocomposites

A comparison of the density between injection molded nanocomposites will help to identify differences in the sample due to process defects (e.g., voids). The relative density can be determined based on the Archimedes principle using the following equation:

은 액체 매질 중의 샘플의 질량이며, ρ₀는 사용된 매질 (즉, 물)의 밀도이다.Where m is the mass of the sample in air,

Is the mass of the sample in the liquid medium, and rho ₀ is the density of the medium (i.e., water) used.

Amorphous PET has a density of 1335 kg / m ³ . PET semi-crystalline polymers exhibit a wide density based on their crystallinity. The theoretical density of amorphous nanocomposites can be calculated using the relative density of PET (1335 kg / m ³ ) and GNP (2200 kg / m ³ ). The crystallinity of the control (PET) and nanocomposite samples can be evaluated using the equations given below.

Here, X _c is the crystallinity of the sample, ρ _a is the density for amorphous PET, ρ _c is the density for crystalline PET (1455 kg / m ³ ), and ρ _sample is the density of the composite.

PET is known to cause chain splitting under high shear at the melting temperature. In addition, the effect of ultrasound treatment on PET has not been previously investigated. Therefore, gel permeation chromatography (GPC) was performed to evaluate the change in the molecular weight of the ultrasound treated PET and PET nanocomposite. Hexafluoroisopropanol (HFIP) was used as a solvent for dissolving PET at room temperature. For the composite pellets, the polymer was dissolved and the nanosplan was filtered. GPC measurements were performed by Auriga Polymers. The polymer dissolved in the solvent (5 mg / ml) was pumped through the GPC column with a certain pore size at a constant flow rate. The time (residence time) taken for the swollen polymer molecules to pass through the column is based on the molecular size. As the polymer solution passed through the column, the elution volumes for different fractions (same molecular weight) identified using refractive index detectors were recorded. This elution volume was compared against polystyrene standards of known molecular weights to obtain an average molecular weight for the PET sample.

The intrinsic viscosity (IV) of the PET and ultrasonic treated PET pellets was measured in a polymer plant using a proprietary solvent that was calibrated against the solvents recommended in the ASTM D4603 standard. The polymer pellets were dissolved in the solvent and the flow time for the solution was noted when the solution was passed through a glass capillary viscometer and descended from top to bottom calibration mark (as shown in Figure 20). The ratio of the mean flow time to solution versus solvent provided the relative viscosity ( _r ) of the polymer. The intrinsic viscosity of the polymer was calculated using the following equations:

Here, η _r is relative viscosity, t is the average solution Flow time (s), t ₀ is the average solvent flow time (s) and, η is intrinsic viscosity (dL / g), C is the polymer solution concentration (g / dL).

Using the weight average molecular weight data from the GPC technique and the intrinsic viscosity (IV) data obtained by the procedure mentioned above, the mark-after-wink parameters for the relevant PET IV to M _w were adjusted and the ultrasonic treated nanocomposite Was used to calculate the viscosity for:

Here, η is the intrinsic viscosity of the polymer (dL / g), M is the average molecular weight (g / mol), and 'K' and 'a' are the mark-after-wink constants. Weight average molecular weight was used, and 'K' and 'a' were taken as 0.00047 and 0.68, respectively.

From the injection molding process, nanocomposite samples having two different geometries, a tensile rod and a tensile tube, were obtained. Both geometric tension rods and tubes were tested using a universal material tester at a cross-head speed of 5 mm / min in accordance with ASTM D 638 standards. A non-contact laser stretcher system was used to record the strain. The laser stretcher records the displacement based on the reflection from the self-reflecting tape used to mark the gage length on the test sample, as shown in Figure 21A. The load from the load cell and the strain value from the laser stretching system were simultaneously recorded at intervals of 100 ms. For testing of nanocomposite tubes, the outer fixture shown in Figures 21b-21d was used. A minimum of five samples were tested for each process condition.

Differential scanning calorimetry (DSC) of PET and nanocomposite samples was performed to understand the effect of graphene on the thermal properties of PET (glass transition, crystallization and melting temperature). The temperature recorder of the nanocomposite was obtained using differential scanning calorimetry. The nanocomposite sample was heated from ambient temperature to 300 占 폚 at 10 占 폚 / min under a nitrogen atmosphere and maintained at 300 占 폚 for 1 minute (first heating cycle), then cooled to 25 占 폚 at 10 占 폚 / min, min (first cooling cycle), and then reheated (second heat cycle) to 10O0C / min to 300 < 0 > C. The sonicated PET pellets were also analyzed for changes in thermal properties.

From the first heating cycle, the melting parameters (temperature, heat of fusion) and the heat of crystallization were obtained to determine the degree of crystallization (X _c ). To determine the crystallization half-life (t _1/2 ), melt crystallization temperature (T _c ) and initiation temperature (T _on ) were obtained from the first cooling cycle. The crystallinity can be calculated using the following equation:

Where ΔH _f is the heat of fusion, ΔH _cc is the crystallization heat (low temperature crystallization), ΔH _c ⁰ is the heat of fusion for the 100% crystalline polymer (140.1 J / g for PET), w _f is the weight of the stiffener in the nanocomposite Fraction.

The crystallization half-life was determined using the following equation:

Here, T _on is a crystallization start temperature, T _c is a crystallization temperature, and X is a cooling rate (here, 10 ° C / min).

It is known that polymer flow behavior is affected by the addition of stiffeners (micro or nano). Studying the flow characteristics of nanocomposites is useful for their processing. Melting rheology was studied to understand the effect of graphene on the flow characteristics of PET. The rheographs for the nanocomposite pellets were obtained using a rotary rheometer equipped with electronically controlled heating and a 25 mm diameter parallel plate geometry. The samples were dried in an oven at 170 DEG C for 12 hours to remove moisture. A parallel PET pellets and nanocomposite sandwiched between the plates N was molten in a thickness of 1 mm ₂ at 260 ℃ under atmosphere pressure (as shown in FIG. 22). The linear viscoelastic region of the sample (region where the material response is independent of the deformation amplitude) was determined by running a strain sweep at a frequency of 1 Hz. A dynamic frequency sweep from 100 rad / s to 0.1 rad / s was obtained for all samples with 1% strain within the linear viscoelastic region for PET.

Dispersion of the nanoparticles into the polymer matrix increases polymer chain entanglement through polymer-polymer and polymer-reinforcement interactions. The increase in entanglement stiffens the polymer and exhibits a solid-like (rigid) strain behavior, which is independent of the test frequency. Transition to hard behavior of nanocomposites occurs at the critical weight fraction (percolation threshold) when the backbone network of the stiffener is formed. The dynamic frequency sweep of the modulus provides information from both the polymer and the stiffener material. When the high frequency modulus is dominated by the polymer matrix, the low frequency response of the material is dominated by the stiffener. Thus, the percolation volume fraction can be obtained based on the low frequency modulus of the nanocomposite. The average aspect ratio of the stiffener in the percolation volume fraction can be determined using the following equation.

Where the phi _sphere is the percolation volume fraction for the randomly packed superimposed sphere (here assumed to be 0.30), and [phi] _per is the percolation volume fraction for the nanocomposite.

Raman spectroscopy is the most widely used technology to characterize graphene quality. In the case of nanocomposites, several studies have reported the application of Raman spectroscopy to characterize the interaction between the quality of graphene and the polymer-graphene system. Yes single layer will have the characteristic Raman spectrum of the peak pin (G- band) near 1580 cm ^-1 corresponding to the CC height of sp ² carbon material, 2680 cm ^-1, near (G'- band) corresponds to high-order Mode. The presence of defects in graphene can cause different Raman peaks (D-band) near 1350 cm ^-1 , which is useful for analyzing the quality of graphene. In the case of multilayer graphene, the number of layers up to n = 7 for multilayer graphene can be estimated based on the intensity of the G-band (~ 1580 cm ^-1 ) and the 2D-band or G'-band 2680 cm ^-1 ) can be used to identify layers up to n = 4. In the present study, Raman spectroscopy was used to evaluate the dispersion of graphene nanoparticles and to determine the π-π interaction between the phenyl ring in the PET molecular chain and the graphene layer. The interaction of the PET phenyl ring with graphene was found to indicate the migration of the Raman band associated with the CC stretch of the phenyl ring (1617 cm ^-1 ).

Raman spectra for PET and PET-GNP nanocomposites were collected using a 532 nm (green light) laser excitation at 2 mW laser power with a 20 μm spot size. The change in CC (1617 cm ^-1 ) band position was assessed by performing individual peak pits (Gaussian pits) on the collected spectra for each GNP weight fraction.

Microstructure analysis

As will be appreciated, imaging of nanocomposites requires understanding of the role of nanoparticles in improving polymer properties. Nano stiffeners are considered to be advantageous because of the broadest possible interaction with the polymer matrix. Thus, it is necessary to visualize the degree of interaction, which depends on the level of dispersion. In addition, actual microstructure information is useful for modeling the behavior of nanocomposites and is helpful for engineering materials. Electron microscopy and X-ray diffraction are the most common techniques used to study dispersion. Both of these techniques are often used with each other.

The graphene nano platelet inside the PET matrix was imaged by scanning electron microscopy (SEM). SEM micrographs of the ruptured surface of PET and PET-GNP nanocomposites were obtained. A PET control and a nanocomposite with a lower graphene content (2% or less) were Au / Pt coated using a Balzers Union MED 010 coater.

Nanocomposite tensile rods were imaged with ultrasound to assess the presence of process defects (e.g., voids). Ultrasonic 'bulk scan' for nanocomposites was obtained using an acoustic microscope. The scan was performed at an ultrasonic frequency of 30 MHz with a spot size of 122 μm and a 0.5 "focal length. To maximize ultrasonic transmission, a liquid medium such as water was used between the probe and the sample during the injection process. Ultrasonic micrographs were recorded at pixel pitch.

Transmission electron microscopy (TEM) was performed to analyze the degradation of graphene nanofabrication. Nanocomposite flakes (70 nm thick) on 5% and 15% GNP weight fraction tension rods were microtomed and imaged under a transmission electron microscope at an operating voltage of 200 kV. The difference in electron density between PET and GNP provided contrast in the transmission electron microscope photographs. Due to the higher density of graphene nano-platelets compared to PET, they can be perceived as darker areas in the micrograph. Nanoplatelet parameters, thickness and length (diameter) were obtained by measuring the number of pixels after correction of the transmission electron micrograph.

Transmission electron micrographs provide 2D dimensions of the nanosplane. However, this information alone is not sufficient to quantify its distribution in the polymer matrix. The 'inter-particle distance (λ _d )' parameter can be used to quantify the dissolution of platelets based on information from TEM micrographs. The interparticle distance developed based on the steric relationship to associate information from 2D slices to 3D is the average distance measured linearly between particles. Using a binarized TEM micrograph, the inter-particle distance (lambda _d ) was determined based on the equation (9) given below. The interfacial area per unit volume (S _V ) _PG can be obtained by measuring the combined perimeter of the nano platelets present per unit area of the micrograph.

Where V _V is the volume fraction of nano-platelet, (S _V ) _PG is the polymer-nanosepoxide interface area per unit volume of the specimen, and L _A is the total circumference of the platelets per unit area of the 2D micrograph.

Considering that the nanosplane having a known thickness t and an aspect ratio A _f dispersed in a polymer is disc-shaped, the theoretical inter-particle distance can be estimated using the following equation, ): ≪ RTI ID = 0.0 >

Where V _v is the volume fraction of the nanosplan, A _f is the nanoseparator aspect ratio, t is the nanosubstance thickness, and λ _d is the inter-particle distance.

X-ray diffraction helps to understand the dispersion state of nano-platelet in the polymer matrix by measuring the spacing between nanoparticles. The single-layer graphene has a two-dimensional (2D) hexagonal lattice. Graphene nano platelet having a 3D structure similar to graphite exhibits "graphene-2H" characteristic reflections corresponding to (002) and (004) planes (26.6 ° and 54.7 ° 2θ for Cu K _α X-rays). PET is mainly, having a three circumstances crystal structure (010), (110), (100) and _{(105) (Cu K α 17.5} ° on X-ray, 22.5 °, 25.6 ° and 42.6 ° 2θ) surface [48] ≪ / RTI > Amorphous PET shows broad halo at about 20 ° 2θ.

The diffraction pattern of the nanocomposite was collected using a 2D detector and microdiffraction and a 0.5 mm collimator as the reflectance for crystallinity measurement. Cu K _α X-ray radiation (lambda = 1.54184 A) was used with an injection time of 60 sec. Percent crystallinity can be determined based on amorphous and crystalline fractions using equation (11): < RTI ID = 0.0 >

Where A _c is the crystalline contribution and A _a is the amorphous contribution.

The sample geometry associated with the instrument geometry (I - injection flow direction, T ₁ - longer dimension of section, and T ₂ - thickness) is shown in FIG. A 2-D diffraction frame is shown in FIG. 23 which presents partial diffraction rings for PET and graphene, indicating the presence of selective orientation. The location of the nanocomposite samples used for diffraction and tomography is shown in Fig.

As will be appreciated, electron microscopy provides only two-dimensional microstructure information of the sample from a small area. For TEM, the sample size is only 500 μm × 500 μm in area and 70 nm in thickness. Electron microscopy combined with a focused ion beam (FIB) may be useful for obtaining microstructure information along the third direction. Nevertheless, X-rays have certain advantages over electrons in imaging materials. The simplicity of the sample preparation, the choice of ambient or in-situ environments, and less induced damage to the material are the main advantages. X-ray tomography is a non-destructive imaging technique that enables the reconstruction of 3D structural details of a material.

Tomographic imaging is the process of collecting cross-sectional information from an illuminated object in a transmissive or reflective mode. The material and geometry information are recorded (radiographed) based on the transmission intensity of the X-rays as illustrated in Fig. This transmission intensity can be related to material information based on the X-ray absorption coefficient and density of the material according to the following equation:

Here, I is the transmitted X-ray intensity, I ₀ is the initial X-ray intensity, and μ _m is the mass attenuation coefficient of the material, ρ is the material density, x is the material thickness. Radiographs are reconstructed with cross-sectional slices (tomography) using Fourier transform-based algorithms. The development of X-ray and detector optics has enabled the beam to be focused on much smaller areas to obtain nanoscale resolution.

In the present study, two different samples (nanocomposite tensile rods and tensile tubes) were attempted with X-ray nano tomography for the purpose of understanding the distribution of the nanoparticles in three dimensions. Nano-tomography of samples collected from a 15% tensile bar was performed at 272 nm / pixel resolution on a SkyScan 2011 Nano-CT instrument. For the 2% weight fraction of the tensile tube sample (ultrasonicated), the wedge-shaped compartments from the inner and outer surfaces were injected at 150 nm / pixel resolution on an XRadia 800 Ultra 3D X-ray microscope. The reconstructed tomography photographs were visualized using 3D visualization software.

Micromechanical Modeling of Nanocomposites

Continuous fiber composites are often designed or evaluated based on simple empirical formulas (referred to as "mixing rules"). For nano-stiffeners, the mixing law incorrectly predicts the final properties. Along with the fact that they are not continuous fiber reinforcements, the differences are influenced by the low volume fraction, the significant characteristic difference between the matrix and the reinforcement, and the aspect ratio. In nanocomposites, the spatial interaction between the nanosplane and the matrix is important in determining its elastic behavior. The high aspect ratio of the nanosplane combined with the interaction at the matrix-stiffener interface complicates the characterization of nanocomposite properties. Therefore, the conventional micromechanical model was changed to estimate the mechanical properties for nanoparticles.

For the purpose of understanding the effectiveness of graphene nanosophen as a reinforcing material, micromechanical models such as halftone-difference and Hui-shea models were used to determine the theoretical elastic mechanical performance of PET-GNP nanocomposites. These models were a simplified micromechanical relationship of the continuum-based Mori-Tanaka and Hill methods for predicting composite properties, both of which are designed for unidirectional composites. The aspect ratio of the nanosplane dispersed in the polymer can be determined from a transmission electron microscope photograph. In the halftone-difference model, the longitudinal modulus (E ₁₁ ) of the composite is predicted using the following equation:

Where A _f is the aspect ratio of the nano-stiffener (D / t), φ is the volume fraction of the stiffener and E _r is the ratio of the stiffener modulus to the matrix modulus (E _m ).

In the case of the Hui-Xia model, the modulus prediction is made using the following equation:

Here, φ is the volume fraction of the reinforcing material, α is the inverse aspect ratio (t / D), E _m is the Young's modulus of the matrix (PET), E _f is the Young's modulus of the reinforcing material (graphene nano-platelets).

result

For the purpose of improving the PET properties, graphene nanotubes were blended with PET and injection molded with a nanocomposite having a specific load ratio. The nanocomposites obtained from this process were evaluated for their mechanical, thermal and rheological properties in order to understand the effectiveness of graphene nanosophene.

Average molecular weight

The average molecular weight was obtained from gel permeation chromatography (GPC) for the following samples: control PET, sonicated PET, ultrasonicated nanocomposite master-batch (5% GNP), and biaxial- Screwed master-batch. Comparing master-batches with similar GNP weight fractions can help to understand the changes caused by the presence of graphene.

Based on the weight average molecular weight (M _w ) presented in Figure 26, the following observations were made. First, the average molecular weight changes with biaxial-screw machining regardless of ultrasonic treatment. The reduction in molecular weight through a single sonication is less significant than the drop from the biaxial-screw formulation.

In addition to this observation, it was also recognized that the drop in molecular weight increased with the presence of graphene. From molecular weight measurements, the sonicated samples showed a polydispersity (ratio of weight to number average molecular weight) of 1.8 and 1.9 for nanocomposites with 5% GNP.

Intrinsic viscosity

The intrinsic viscosity (I.V.) is the most commonly expressed number in connection with the discussion of comparing the properties of polyethylene terephthalate. Thus, the intrinsic viscosity of the PET and sonicated PET sample presented in Figure 27 was obtained by capillary viscometers using polymer dissolved solvents.

By the correlation between the experimentally obtained viscosity and the viscosity calculated by the weight average molecular weight using the equation 5, the mark-after-winking parameters 'K' and 'a' were optimized to values 0.00047 and 0.658, respectively. Using the new constants, the intrinsic viscosity for the nanocomposite sample was obtained. The calculated viscosity values for both PET and PET nanocomposite samples are shown in Fig.

The intrinsic viscosity for the experimentally collected, in-situ polymerized PET and nanocomposite pellets is shown in Fig. 29, both of which show a viscosity of the order of 0.6 dL / g.

Mechanical behavior

The stress-strain curve for the tensile rod sample is shown in Fig. Young's modulus was obtained from the initial region of the stress-strain curve. The zero modulus and intensity data (set-A) for the nanocomposite tensile rod are shown in Fig. A decrease in the strength of the nanocomposite was observed compared to control PET. In addition, the nanocomposites had brittle fracture with loss of elongation compared to control PET samples.

Using the outer fixture, the PET and nanocomposite tensile tubes and rods shown in Figures 32a-32b were tested for their mechanical properties. The Young's modulus and tensile strength of PET and nanocomposites are shown in FIG. The PET modulus from the tensile tube was found to be smaller than the tensile bar sample (0.2 GPa difference), because the tensile bar exhibits thermal crystallinity due to slower cooling (19%). The modulus of nanocomposites increased with increasing GNP content. However, the strength of the nanocomposite remained the same as that of PET, except for the 2% sample. The stress-strain curves for nanocomposite tensile tubes of PET tensile rods (2% GNP) and low GNP weight fractions (0.6% and 1.2% GNP) are compared in FIG. Figure 34 shows that the nanocomposite is stiffer (less stress-strain curve area) than PET. The Young's modulus for the 2% nanocomposite was the same as for the two different injection molding processes used (3.1 GPa). However, nanocomposite tubes with a 2% GNP load deviate from the type of breakdown associated with lower weight fractions. At 2% GNP loading, the nanocomposites exhibited only 1% strain, which is significantly lower than the breakage strain (400%) for a 1.2% GNP load.

The PET obtained from the micro-injection molding process and the tensile rods of the ultrasonicated PET were tested for their mechanical properties. Figure 35 compares zero-modulus and intensity data for ultrasound treated PET with control PET. Ultrasonic processing of PET was observed to have no significant effect on its modulus and strength. However, the ultimate tensile strength (break tensile strength) for the ultrasound treated PET was significantly increased (by 24%) as shown in Fig.

Using sonicated master-batch pellets, nanocomposite tensile tubes with 2% GNP loading were prepared and tested for comparison with nanocomposites from biaxial-screw formulations. Nanocomposites prepared from blended pellets treated with different ultrasonic amplitudes suggest improved Young's modulus and tensile strength. The improvement in modulus for nanocomposites by 3.5 μm ultrasonic amplitude was higher (2.7 GPa - 12% improvement) as compared to other ultrasonic treatments, as shown in FIG. Nonetheless, the increase in modulus for the ultrasonically treated 2% nanocomposite is lower than for the 2% GNP biaxial-screw-blended nanocomposite (3.1 GPa - 24% improvement). Ultrasonic nanocomposites exhibit similar yield behavior to PET, but the maximum improvement in strength is only 3%.

Nanocomposites prepared through dilution of the sonicated master-batch did not provide sufficient evidence to understand the change in mechanical properties. Thus, an ultrasonic treated master-batch pellet was used to obtain a tensile bar with a 5% GNP weight fraction from the micro-injection molding system (without dilution of the GNP weight fraction). The Young's modulus and tensile strength of 5% GNP nanocomposite tensile rods were compared to tensile rods prepared using 5% pellets from control PET and biaxial-screw blending process, as shown in FIG. Strength data indicate the recovery of tensile strength with increasing ultrasonic amplitude, but the modulus data suggest that the improvement from ultrasonic treatment is not significant compared to regular biaxial-screw mixing.

The tensile bars of the PET control and the nanocomposite with 0.1% GNP weight fraction obtained from the polymerization process were tested for their mechanical properties. Figure 39 compares zero modulus and intensity data for nanocomposites with PET and 0.1% GNP at different surface areas. There was no significant difference in the modulus of the nanocomposite, but the strength showed two different tendencies. The ultimate strength of the nanocomposites suggests a significant (at least 16%) improvement over the PET control. On the other hand, the tensile strength of the nanocomposite was slightly (5%) lower than that of PET.

Density measurement

The density of the nanocomposites was measured using Archimedes' principle. The density of the nanocomposite was different from the theoretical value calculated based on amorphous PET and graphene. A comparison of the density between the molded PET tensile rod and the tensile tube suggests that the tensile rod is semi-crystalline (19% crystallinity) based on Equation 2. Density measurements from nanocomposite samples deviate from the theoretical values based on the mixing law. To better compare the strength of the nanocomposite, the density was collected for the pre-test sample and used to calculate its specific strength as shown in Figures 39a-39c. The non-intensities values shown in Figures 39a-39c do not suggest a significant loss or improvement in the strength of the PET with GNP, except for the nanocomposite tensile tube with a 2% GNP weight fraction.

Scanning electron microscopy

A comparison of the stress-strain curves of PET and nanocomposites suggests that the tensile strain (elongation) of nanocomposite tensile bars decreases. To understand the type and reason for the breakage of nanocomposites, scanning electron microscope photographs were collected. Ruptured surface micrographs present the presence of micropores as shown in Figures 40a and 40b. The water present in the pellets can cause voids during its processing. Thus, the increase in stress concentration near the voids was responsible for the strength degradation of the nanocomposite tensile bars. Rupture Surface Microscope Photograph The initiation of cracking from voids, as shown in Figure 40b, demonstrates this observation.

Similar observations were made from a micrograph of the rupture surface of a nanocomposite tensile tube with a 2% GNP weight fraction. The nanocomposite tensile tube presented a signal of "soft burst" as shown in Figures 41a and 41b. The pores observed in this sample set are very small, <10 μm in size, and are indicated by arrows in FIG. 41a. Localized ductile deformation of the polymer matrix through fibril stretching around the microvoids can increase local stress concentration. This increase in stress concentration can initiate cracking, leading to brittle fracture of the nanocomposite.

At higher magnification, the ruptured surface of the graphene nanotubes was observed. As can be seen in the SEM micrographs shown in FIGS. 42A-42D, nano-platelets protrude from the surface indicating that they were exposed during breakage and were part of load sharing. At higher nanoparticle content (15%), the microstructure of nanocomposites differs from others with more localized ruptures. One difficulty of nanocomposites is the ability to prepare transparent amorphous samples. Having a transparent PET tensile bar helps to remove defects caused by poor processing, such as voids. Since these nanocomposites are dense, the voids should generally be awaited to be visually observed by the breakdown method.

Ultrasonic imaging

A non-destructive alternative to imaging voids is ultrasound imaging. An ultrasonic micrograph of the tensile bar from the ultrasound "bulk scan" is shown in FIG. These micrographs show the presence of voids along the length of the tensile bar. Based on the micrograph, the voids were estimated to be the result of processing. In addition, the density of the ultrasound imaged sample was compared to the mechanical test, confirming that the deviation in density was due to the presence of voids.

Thermal analysis

The melting and crystallization behaviors of the nanocomposites were analyzed by DSC measurement. Figure 44 shows the glass transition (T _g ) and melting temperature (T _m ) from the second heating cycle, plotted against the GNP weight fraction for biaxially-screw-blended pellets, and the crystallization temperature (T _c ). The melt temperature shifted to higher values with increasing GNP, but the glass transition showed a decreasing trend except at the 15% weight fraction. The reduction in glass transition temperature as shown in Figure 44 may be due to the agglomeration of the nanosplane within the PET matrix. The agglomerated platelets can act as plasticizers and can affect the glass-transition temperature.

Both crystallization and melting temperature increased with increasing GNP content. The melting temperature of PET is dependent on the crystal shape and size. As shown in Figure 44, the addition of GNP increases the melting temperature. This may be due to the formation of larger and more complete crystals, where the presence of nucleation sites (i.e., nanosplanes) is expected and is manifested by higher crystallization temperatures (10 ° C to 18 ° C). The change of the melting temperature was small, but the crystallization temperature increased with the increase of the GNP content. The increase in crystallization temperature is due to the nucleation effect from the presence of GNP, which is stronger with increasing GNP weight fraction. The shape of the exothermic peak and the change of the crystallization temperature together with the GNP weight fraction are shown in Fig.

Using the crystallization exotherm for the nanocomposite pellets shown in Figure 46, the initiation temperature (T _on ) was determined to determine the crystallization half-life (t _1/2 ) according to equation (7). With increasing GNP content, the crystallization half-life (inverse of the crystallization rate) was observed to increase. The decrease in crystallization rate indicates that PET chain mobility is affected with increasing GNP content. As a result, the crystallinity of the nanocomposite decreased at higher graphene loading, as shown in Fig.

The percentage crystallinity as shown in Figure 45 (right) for the injection-molded tensile bars as provided was measured by differential scanning calorimetry (DSC). The crystallinity measured for injection molded nanocomposites presents a trend similar to the non-isothermal crystallinity obtained through DSC. This demonstrates the observation that the increase in GNP limits the chain mobility while allowing early nucleation of PET.

Ultrasonicized PET and PET-5% GNP nanocomposite pellets were analyzed for changes in their thermal properties. The glass transition and melting temperature are shown in Fig. The glass transition temperature of PET decreased with the addition of GNP under ultrasonic condition (0 USM). Ultrasonic treatment was observed to affect the glass transition temperature (T _g ) of both PET and PET nanocomposites. For PET, the glass transition followed a decreasing trend except at 7.5 μm amplitude. The change in 'T _g ' for PET refers to polymer softening with increasing ultrasonic amplitude. The glass transition temperature for nanocomposites increased with ultrasonic amplitude. However, the 'T _g ' of the nanocomposite was still lower than that of PET. The crystallization temperatures for PET and PET-5% GNP pellets were kept constant at 194 캜 and 214 캜, respectively, regardless of the ultrasonic amplitude.

As evidenced in Figure 48, multiple melting peaks were observed in the melt endotherm for the sonicated PET. Multiple melting peaks indicate the presence of different crystal sizes, which potentially represent a broader molecular weight distribution.

The crystallization half-life (t _1/2 ) for PET was reduced by ultrasonication. With the addition of GNP, 't _1/2 ' increased for all ultrasonic amplitudes as shown in FIG. The increase in the half-life of crystallization for 5 μm amplitude-conditioned nanocomposites was smaller than for other ultrasonic amplitudes. The non-isothermal crystallinity for ultrasound treated PET increased with increasing amplitude except for the 7.5 μm amplitude. The presence of graphene increased the degree of crystallinity, but the maximum change in crystallinity was observed only at 7.5 μm amplitude.

The tensile bars of the ultrasound treated PET and PET nanocomposites obtained from microinjection molding were evaluated for percent crystallinity. Under similar conditions, the ultrasonicated PET sample has a crystallinity of 8% and the ultrasonicated nanocomposite has a crystallinity of 11-13%.

PET control and nanocomposites obtained from in situ polymerization were evaluated for their crystallization behavior. At 0.1% loading, graphene nano platelets with higher average surface area (750 m ² / g) had a stronger nucleation effect than nano-platelets with lower average surface area (120 m ² / g). The crystallization temperature and the non-isothermal crystallinity are higher in the case of high surface area graphenes as shown in FIG.

Distributed research

In order to understand the dispersion of nano-scatters in PET, we studied the rheology of nanocomposites. The dynamic frequency sweep for the nanocomposite pellets from the biaxial-screw blend with the control PET is shown in FIG. The shear storage modulus (G ') of PET decreased linearly with frequency. The addition of graphene nanosplan to PET improved its modulus (G '). For the PET-2% GNP nanocomposite pellets, the modulus (G ') was transferred from a linear region (dependent) to a plateau (independent of angular frequency) of less than 0.3 rad / s. This transition for the 5% nanocomposite migrated to a frequency of 64 rad / s. Nanocomposites with 10% and 15% GNP weight fractions also exhibited rigid behavior when tested at 320 DEG C with a gap of 1.6 mm (melt thickness between parallel plates). The percolation threshold (φ _per ) for biaxially-screw-blended PET-GNP nanocomposites is based on a linear regression of the G 'value at 0.1 rad / s for the 2% and 5% samples, as illustrated in FIG. 1.75% wt. (1.1% vol.). The nano platelet aspect ratio at percolation threshold was estimated at 40 based on equation (8).

Ultrasonic assisted blending of PET and graphene nano platelets showed a more linear response compared to biaxial-screw blending for the same weight fraction (5%), as demonstrated in FIG. At low frequencies, nanocomposites with lower ultrasonic amplitude exhibited higher storage modulus. This indicates that the increase in ultrasonic amplitude affects the nanoparticle dispersion. As the modulus at higher frequencies is an indicator of polymer behavior, a reduction in modulus as compared to the PET control suggests a change in the polymer structure. Comparing the ultrasonic treated PET to the PET control, it can be seen that the shear modulus of the PET at higher frequencies increases at lower (3.5 and 5 μm) ultrasonic amplitudes, as shown in FIG. Additionally, the modulus at lower frequencies increased in samples with no (0 μm) and 7.5 μm ultrasonic amplitude conditions. Based on the data presented in Figures 53 and 54, ultrasonic amplitude of 5 [mu] m has been found to have less effect on PET, which also indicates an improvement in the dispersion of graphene nanofiber.

Transmission micrographs were collected on nanocomposite tensile rods of 5% and 15% weight fractions. 56a and b, it can be seen from the transmission micrographs of the nanocomposite that the graphene nanosophene is not completely peeled off in the PET matrix. From the micrographs shown in Figs. 55A and 55B, it can be seen that the nano-platelet is distributed in the matrix in a region of high concentration.

The average dimensions (thickness and length) of nanosplanets obtained from TEM micrographs were used as input parameters to evaluate the micromechanical model. Intergranular distances for graphene nano platelets within the PET matrix were determined using binarized TEM images. By converting the micrograph to a binary image, it was possible to separate the nano-platelets (darker regions) from the polymer matrix, as shown in Fig. The intergranular distances for the 5% and 15% nanocomposites were determined as 2800 nm and 520 nm, respectively, as shown in FIG. Such a change in inter-particle distance may be due to an increase in the concentration of graphene nanofibers, which may affect dispersion. The theoretical inter-particle distance for a graphene nanosplan of the known aspect ratio of 40 (obtained from a rheological measurement) was plotted against the calculated values based on the TEM, as shown in FIG.

The diffraction pattern obtained from the graphene nano platelet, PET, and PET-GNP nanocomposite tensile rods is shown in FIG. The peak wideness observed for graphene peaks at 26.6 [deg.] 2 [theta] indicates the presence of platelets with different d-spacings. The intensity of the graphene peak at 26.6 DEG 2 &amp;thetas; increased with the weight fraction of nano-platelet. However, peak migration was not observed as in the case of the degraded nanocomposite. PET and nanocomposite tensile rods exhibit broad amorphous halo around 19.2 ° 2θ.

From the diffraction scan, it can be seen that the PET tensile bar is amorphous. However, density measurements and visual observations were the opposite. Thus, diffraction scans were collected across the cross-section of the PET tensile rod to confirm the presence of a crystalline core having an amorphous outer layer. The slower cooling rates during the oil-cooled injection molding process lead to the formation of significantly different epidermis and core microstructures. Data from diffractive line scanning according to thickness are shown in Figures 60A-60B. To better understand the microstructure of the nanocomposite, a similar line scan as shown in Figure 61 was performed along the thickness of the 15% tensile bar. The intensity of the graphene peak was observed to vary along the thickness of the sample, with a higher intensity at the center. In addition, the crystallization of the PET was also observed at the core side of the sample as indicated by the arrow labeled "PET"

From the diffraction analysis on the nanocomposite tensile tube, it can be seen that the addition of the fully amorphous microstructure and GNP does not increase the crystallization of the PET. From the 2D diffraction frame, it can be seen that GNP is oriented at the surface due to injection flow stress.

Using reconstructed tomography photographs of the samples collected from the 15% nanocomposite tensile rods, the distribution of nano-platelet inside the PET matrix was visualized as shown in Figures 62a-62b. Based on the observation from the reconstructed volume, it was found that the nanosplatons were oriented along the flow direction (along the Y-axis direction) about 200 μm deep from the surface. From this data, nano-plates with random orientation and curved platelets were also observed.

3D X-ray microscopy of samples (wedge shaped) collected from nanocomposite tensile tubes suggested that the degree of nanoplatelet orientation is smaller than in tensile bars. 63A-63B show the 3D distribution of nano-platelets on the inner surface of a tensile tube. As can be seen from the photograph, the nanosplane is oriented in the direction of flow parallel to the surface to a thickness of only 15.6 占 퐉. Outside the surface, the alignment by flow was limited to a thickness of 7.5 μm.

To analyze the dispersion of graphene nanoparticles, Raman spectra for PET and PET nanocomposites were collected. From Raman spectra, it can be seen that the nanosplanets dispersed in the PET matrix are multi-layered. As described at the beginning, Raman spectroscopy can also be used to determine the presence of a pi-pi interaction between the PET and the graphene layer. Figure 64 shows a Raman band (~ 1617 cm ^-1 ) corresponding to CC stretching for PET and nanocomposites with increasing GNP content. The change in band position determined from the peak pitch can be observed in Fig. Movement of such a Raman band (~ 1617 cm ^-1 ) corresponding to CC stretching in the phenyl ring of PET indicates interaction with graphene. In addition, to understand the effect of graphene on the PET chain mobility, the full width at half maximum for the Raman band (~ 1730 cm ^-1 ) corresponding to C = O stretching was evaluated. The peak width for the 1730 cm ^-1 Raman band (C = O stretch), which is perceived to be an index for amorphous PET chain mobility, was not observed here. This may be due to a highly oriented structure at the tensile rod surface obtained from injection molding. Even if the surface is amorphous in these nanocomposites, the highly oriented structure will limit the band width by reducing the probability of having multibranched rotations. Accordingly, FIG. 66A presents a table listing properties of GNP and PET for micromechanical model based prediction in accordance with the present disclosure.

Micro-machine modeling

Single layer graphenes are known for their high strength and stiffness. Nevertheless, it has not been implemented here that only graphene nanosplanets are dispersed into single-layer graphenes. Part of the mixture may be a single layer, but most were not. Studies on multi-layer graphene have suggested that when the number of layers is less than 10, the characteristics are similar to those of the single layer. In the case of nano-platelets with more than a few layers, their mechanical behavior was found to be similar to graphite flakes. For that reason, the modulus of graphene nanofabrication was considered to be 0.795 TPa, which is similar to highly oriented graphite.

The improvement in nanocomposite properties depends on the nanosplane dispersion. Based on the measurements from TEM micrographs, graphene nano plastic plates having different lengths (diameter of platelets) and thickness were observed. Figure 66 shows the average size of the platelet with minimum and maximum values. The predictive modulus of the nanocomposite from the halftone-difference and Hui-sia micromechanical model was plotted against the experimental results as shown in Fig. To compare with the modulus from the nanocomposite tensile rods, the modulus of the semi-crystalline PET obtained from the PET tensile rods was used as model input characteristics. Using the average platelet properties and its standard deviation, the modulus limits for the nanocomposites were calculated. The upper and lower limits for the prediction modulus are shown in Figure 66 by the error bars. The comparison of the modulus data with the experimental data suggests that the posterior-shear model predictions are close to the experimental values.

The nanocomposite modulus for the platelet aspect ratio can be plotted, as shown in Figure 67, using a Hui-Shia model for nanoplatons loaded along the length of the nanocomposite (i.e., in direction '1 or 2' . Modulus data for nanoplasmin aspect ratio (mean and upper limit) from TEM measurements, molten rheology, and ideal dispersion conditions (single layer graphene) were plotted. Based on the micromechanical model, the predictive properties were observed to be more sensitive to the nanoplaced aspect ratio than their properties. Under ideal dispersion conditions, graphene monolayer modulus 1.02 TPa was used. In the model data shown in Fig. 67, the modulus of amorphous PET obtained from the injection-molded tensile tube was used.

Comparison of experimental modulus with predictive modulus suggests that nanocomposites with lower GNP weight fractions have higher aspect ratios. It has been observed that master-batches with low GNP content induce higher aspect ratios in the nanocomposites (0.5%, 0.6% and 1.2%) produced through dilution of the master-batch (as mentioned in FIG. 16c) . This can be explained by considering the milder machining that occurs in the dilution of the master-batch performed with a single screw rather than a biaxial screw.

Argument

Polyethylene terephthalate - graphene nano platelet nanocomposite was prepared by injection molding. Master-batch pellets from biaxial-screw blending and ultrasonic-assisted biaxial-screw blending were characterized for their mechanical and thermal properties. In this chapter, the effects of ultrasound on PET properties, the type of interaction between graphene and PET, the mechanics behind the change of properties, the effects of blending and injection molding, and the applicability of micromechanical models in nanocomposite evaluation are discussed .

Effect of ultrasonic treatment on PET

In this study, ultrasound - assisted extrusion was used to disperse graphene nanotubes in PET matrix. Because there is no literature available to understand the effect of ultrasound on PET, sonicated PET is also analyzed here. During ultrasonic-assisted extrusion, the energy applied to the polymer (in the form of ultrasonic waves) can increase the local melt temperature as a result of acoustic cavitation. Cavitation not only supports the dissolution of nanoparticles, but can also potentially change the polymer. From the average molecular weight from the GPC measurements for the sonicated PET, it can be seen that the molecular weight decreases with increasing ultrasonic amplitude.

Nonetheless, the molecular weight for un-sonified PET also decreased. Based on the data, it is understood that the decrease in molecular weight is mainly derived from the extrusion process (15%) and the ultrasonic treatment has a slight (5% drop) effect on the molecular weight.

Mechanical testing of ultrasonicated PET did not reveal significant differences in Young's modulus and tensile strength. Nonetheless, the ultrasonically treated specimen suggested an improvement in ultimate strength (breaking strength) and showed a higher toughness than the PET control, as can be seen by Figures 36 and 68.

PET degradation involves three different processes: hydrolysis, thermal degradation and oxidation. During the extrusion process, polymer degradation may be effected through one or more of the processes described above and may cause chain segmentation. Some condensation reactions can also occur, which extends the chain. The increase in toughness of PET from ultrasound treatment indicates that ultrasound actually modifies the PET molecular chain. Thermal analysis (DSC and rheology) of ultrasound treated PET suggests entanglement through the branching of PET. The entanglement of the polymer chains causes an increase in shear modulus (G ') at lower frequencies as observed in FIG. It also accounts for the increase in 'T _g ' for PET treated with 7.5 μm amplitude. Polymers with lower molecular weight (shorter chain length) exhibit lower glass transition temperatures as compared to higher molecular weight grades. Subsequently again, the presence of entanglement (cross-linking or chain branching) limits chain mobility and thus increases the glass transition temperature. The increase in the glass transition temperature may be mainly due to the presence of entanglement in the PET molecular chain, since the drop in molecular weight at 7.5 mm amplitude is less significant than other amplitudes.

Similar observations have been reported with increasing levels of fracture strength for low level branching trimethyl trimellitate (TMT) and polymerized PET. At low levels (> 0.4%) of the branching agent, there is a significant increase (25%) in fracture strength from the GPC measurement without signaling of crosslinking.

Wetness and interaction of PET and graphene

In selection of nano-reinforcing materials, compatibility with polymers is an important factor. The two polymers are considered to be compatible (or miscible to form a homogeneous mixture) when their difference in surface energy is small. Increasing the difference in surface energy can lead to phase separation. Likewise, a similar surface energy between the polymer and its nanosubstance supports dispersion. PET is slightly polar due to the presence of C = O bonds in the molecular chain. The surface energy of PET is 41.1 mJ / m ² . The surface energy of graphene is similar to 46.7 mJ / m ² . Although higher than PET and hydrophobic, graphene is much closer to graphene oxide (62.1 mJ / m ² ) and graphite (54.8 mJ / m ² ). As a result, graphene is placed as a more compatible nano-reinforcing material for PET. Generally, graphenes are considered difficult to disperse as individual sheets into any polymer matrix. It presents a tendency to aggregate to minimize surface energy. Thus, it is necessary to apply external energy through different mixing techniques in order to disintegrate the agglomerates and distribute them into the polymer matrix. As mentioned at the beginning, PET is a high viscosity polymer with a high melting temperature. As a result, biaxial-screw and ultrasonic-assisted biaxial-screw mixing techniques are selected for dispersion of graphene nanofibres.

As described above, PET is chemically inert except for strong alkaline solvents. Thus, PET does not react with native graphene. Nonetheless, graphene (similar to CNT) is known to have non-shared interactions with aromatic compounds due to the pi-pi stacking of graphene and benzene. Graphene sheet of graphite inside a similar aromatic-be lower than the aromatic (π-π) has the interaction, their energy is graphene and energy (~ 8.4 × 1014 eV / cm 2) of the benzene system (~ 8 × 1011 eV / cm &lt; ² &gt;). As one of ordinary skill in the relevant art (s) will appreciate, the magnitude of the pi-pi interaction increases with increasing density of hydrogen atoms (i.e., stronger dipoles) in the graphene-aromatic molecular system. This explains the difference in binding energy for the graphene-benzene interaction compared to the graphene-graphene interaction.

PET is an aromatic polyester having an almost planar molecular chain structure. Thereby making it more preferable for the? -Π interaction with graphene nanofabrication. The presence of a pi-pi interactions between PET and graphene nanofibers is detected in the form of Raman peak shifts corresponding to C-C stretches in the phenyl ring (Figure 65). In addition, the graphene nano platelet used in this work has low polarity functional groups such as hydroxyl, carboxyl and ether on the edge (Fig. 7). Polar groups available for nanoplasma are likely to react with polar groups of PET. The above interaction between PET and graphene is advantageous in influencing the properties of the nanocomposite.

Stress transfer between PET and graphene

The PET-GNP nanocomposites have shown improvement in zero modulus as demonstrated in Fig. The increase in modulus of these nanocomposites is due to effective stress transfer from PET to GNP. In the case of such a stiff stiffener, the load transfer between the polymer and the stiffener depends on the strength of the interface, which is directly proportional to the thermodynamic adhesion (W _a ). The adhesion energy between PET and graphene was determined to be 84.6 mJ / m ² by the following equation (21). The total surface energy for graphene is 46.7 mJ / m ² . The polar and non-polar components of the PET surface energy were found to be 2.7 mJ / m ² and 38.4 mJ / m ² .

Where γ ^LW is the Leitz sheath-van der Waals (non-polar or dispersed) component of the surface energy, γ ^AB is the Lewis acid-base (polar) component of the surface energy, and γ =? ^LW +? ^AB .

As one of ordinary skill in the relevant art will appreciate, the interface shear strength is quantified to be 0.46 to 0.69 MPa for the source monolayer graphene (another surface of graphene in contact with the air) in contact with the PET substrate. Since this value is for contact between primitive surfaces with non-polar group interactions, the interfacial strength for the nanocomposite in this study is likely to be higher than 0.69 MPa. In addition, it was found that nano-platelets are well dispersed in PET, although the graphene nano platelet has less interfacial adhesion to PET than clay.

The increase in the weight fraction of GNP caused a reduction in the interparticle distance, as quantified from the TEM micrograph shown in FIG. For nanocomposite pellets with a 15% load, the interparticle distance was determined to be 520 nm, which is greater than the intergranular distance 200 nm reported for 2% graphene (having a higher surface area of 750 m ² / g).

As the nano platelet becomes closer to each other, the number of polymer chains affected by the presence of nanosplan will increase as shown in FIG. 69. Increasing the volume of nano-platelet affecting the polymer will stiffen the polymer. As can be observed by Figure 70, with increasing GNP weight fraction, the nanocomposite modulus increases exponentially. This behavior clearly shows that GNP load sharing increases with increasing weight fraction.

At a higher weight fraction GNP, the stress-strain curve shows a more complicated yield behavior as shown in FIG. Plagiarism - platelet interaction is likely to be absent in low fractions. Not only is the volume fraction of the platelet is important, but also the platelet surface area at such a volume fraction is important. At higher volume fractions, the low surface area platelet is expected to have a similar gain to the lower surface fraction of the high surface area platelet. However, eventually, the platelet starts interacting over the matrix and such interactions will affect the yield behavior. Plate-plate bonding is much weaker than plate-matrix bonding. In this case, the inventors have begun to find more prominent evidence of such interactions in volume fraction plates above 10%.

Nanocomposite microstructure, and application of micromechanical model

Based on the Hui-Si expression, the micromechanical model predicted nanocomposite characteristics more closely than halpin-difference. Initially they were developed to model the properties of semi-crystalline polymers by considering the crystalline domains as stiffener phases in the amorphous matrix. These micromechanical models were later adapted to model micro-composites. The key premise for the above-mentioned models is the uniform interface between the stiffener and polymer oriented in the direction of the load, and the uniform aspect ratio of the stiffener. Nevertheless, the nano platelets dispersed in the nanocomposite are not fully oriented along the load axis (i.e., the injection direction), as observed from the nanotomography illustrated in Figures 62 and 63b. For example, in a nanocomposite tensile rod, the GNP orientation due to flow stress was observed only to a depth of 200 μm from the surface and much less for a tensile tube (15 μm from the surface). This suggests that the bulk core of the nanocomposite has more randomly oriented nanosplanets. Increased injection molding speed and cooling rate resulted in limited nanopatterning orientation.

During injection molding, the polymer melt flowing through the mold channel experiences a shear force. This shear action is due to the temperature gradient induced by the cold mold wall. As the polymer melt begins to solidify (thicken), an increase in shear force, along with a highly oriented layer on the outer surface, creates a layered structure along the thickness. Some were supposed to be 0.1 mm thick. The cooling rate determines the thickness of the oriented layer. Nano tomography enabled quantification of the thickness of the epidermal layer, which is the first observation of such a layer. As observed with the tensile tube, the difference in the oriented layer thickness is likely to be due to the more effective cooling rate from the mold design and curvature of the surface. A comparison of tensile tube data and tensile bar data suggests that the thickness of the oriented surface layer is greater at the tensile bar. This is consistent with slower cooling and injection rates compared to tensile tubes.

One of the observations from nano-tomography was that the aspect ratio of the platelets was not uniform in the nanocomposite. The aspect ratio of nanoplasma from rheological and transmission electron microscopy was determined to be 40 and 18.75. Taking these as limits for the aspect ratio, the modulus of the nanocomposite is predicted. As shown in Figure 67, when comparing the experimental data with the predicted modulus trend for different aspect ratios, it is emphasized that the nanocomposites have virtually different average aspect ratios and increase with increasing GNP weight fraction.

The graphene nano platelet used in this study has an average diameter (length) of 5 μm. As can be appreciated, this dimension is much smaller than the size of 30 μm for native graphene, which was estimated to effectively reinforce polymethylmethacrylate (PMMA). In addition, it will be appreciated that graphene with reduced stiffness (modulus drop from 1000 GPa to 100 GPa) has been shown to be less effective when reinforcing glassy polymers (e.g., PET) compared to elastomers. As mentioned at the beginning, graphene having more than ten layers can have reduced stiffness. Due to these factors, the need to have detailed information on the microstructure of nanocomposites in application of micromechanical models to predict properties is enhanced.

Effect of graphene nanosophene on PET properties and molecular chain mobility

The nanocomposite tensile bars from oil-cooled moldings exhibited about 20% crystallinity. Using a high speed injection molding system with a faster cooling rate, nanocomposite tensile tubes were produced with better control over the crystallization of PET during injection molding. Through this process, nanocomposites with a GNP weight fraction of 2% to 0.5% were prepared and tested. Comparison of the modulus of the nanocomposites with 2% GNP as shown in Figures 31 and 33 from both processes (water cooled and oil cooled injection molding) suggests an improvement in modulus with process variation. For tensile tubes with 2% GNP, the modulus increased from 2.5 GPa (amorphous modulus of PET) to 3.1 GPa (7% higher than tensile bars). Another important observation was that the presence of voids in the 2% nanocomposite had little effect on its modulus. On the other hand, the presence of pores (from processing) resulted in reduced strength and premature failure. As can be seen from the SEM micrographs of the nanocomposite rupture surfaces shown in Figs. 40 and 41, the pores acted as stress concentration points, leading to breakage. The strength of the nanocomposite tensile tube with low GNP weight fraction showed a slight increase as shown in FIG.

In general, the addition of GNP to PET did not affect the strength. This is expected to be because the weight fraction is low and the matrix will dominate typical flow behavior for yield. It is also helpful to think that the lack of chemical bonding between PET and GNP reduces GNP impact on yield and toughness. As discussed in the introduction, interfacial interaction between PET and GNP is desirable for initial stress transfer. With increasing strain, interfacial sliding begins between PET and GNP, which prevents GNP from sharing fracture loads. Since the strength of the material depends on the weakest element, the nanocomposite strength remains similar to PET.

From the rheology and thermal analysis data, it is deduced that the presence of a higher weight fraction of graphene nanosophene will affect the mobility of the PET molecular chain. The higher GNP weight fraction will cause the development of the continuous network as shown in Figure 69, which will change the deformation behavior of the PET. As observed from the mechanical tests, up to the 2% GNP weight fraction, the nanocomposites are stiffer than PET with increased breakage strain. Since PET breakdown occurs as a result of thickness propagation of the surface cracks, the presence of graphene nanofibers in the PET matrix can counteract it through crack deflection. As can be seen from the SEM micrographs in FIG. 42, nanosplane extending from the tear surface supports this observation. On the other hand, nanocomposites with a GNP weight fraction of greater than 2% exhibited brittle fracture. The graphene nanofibrate weight fraction observed for such a transition is consistent with the percolation limit from rheological measurements.

Using Raman spectroscopy, it has been shown that increasing the graphene concentration limits the mobility of the PET chain. The Raman spectrum shown in Figure 5 does not show a change in peak width because the nanocomposite from injection molding represents a highly oriented amorphous surface layer. The oriented PET chain will limit the number of possible chain structures and limit the rotation of the C = O isomer causing peak widening.

Thermal analysis of the nanocomposites suggested that the addition of GNP affects PET crystallization. Graphene nanofabrication can act as a nucleation site and can accelerate crystallization with increasing crystallization temperature. Nevertheless, the reduced PET chain mobility (constraint effect) with the increase of the nanofabric fraction counteracts the nucleation effect. The combination of these opposite effects led to an increase in the crystallization half-life and reduced the amount of crystallinity as shown in Fig. Since intergranular distance becomes smaller with increasing GNP weight fraction, chain mobility is more limited. This reveals a change in the breakage type of the PET nanocomposite, as discussed at the beginning for the higher GNP weight fraction (greater than 2%). Similar observations have been reported with high aspect ratio graphene and PET at less than 2% weight fraction, where the crystallization half-life is found to decrease to less than 2% graphene loading and begin to increase at 2%.

Effect of ultrasonic treatment on PET-graphene nanocomposite

Comparison of the properties of the nanocomposites produced from the biaxial-screw and ultrasonic-assisted blend shown in FIG. 38 helps to identify the best mixing approach. Ultrasonic amplitudes of 5 μm and 7.5 μm showed maximum improvement in modulus. However, such improvements in modulus are not significantly different as compared to nanocomposites from biaxially-screw compounded materials. From this, it can be seen that the ultrasonic treatment did not provide an advantage in improving the dispersion of the graphene nanofabrication plate. The molecular weight data for 5% graphene nanocomposite pellets from both processes show a similar drop in the PET average molecular weight as shown in FIG. Additionally, the presence of graphene was observed to increase the drop in molecular weight from the extrusion process. This may be due to the high thermal conductivity of graphene nanoclay, which allows faster heating of the PET and causes chain damage under normal heating conditions.

The rheology of the sonicated nanocomposites suggested similar behavior to that observed in the sonicated PET as illustrated in Figures 53 and 54. The reduction of shear modulus at high frequencies is due to damage of the polymer chains (molecular weight), and the increase in shear modulus at low frequencies can be attributed to the presence of dispersed graphene as well as the increase in entanglement from ultrasonic treatment. Higher ultrasonic amplitudes suggest lower shear modulus; From this it can be seen that there is a better dispersion at higher amplitudes, which is also evident from the mechanical properties. However, for a 7.5 μm ultrasonic amplitude, the drop in molecular weight is higher than for other amplitudes. Ultrasound-assisted dispersion of graphene showed differences in thermal properties of the nanocomposite (for the same graphene weight fraction) as compared to regular biaxial-screw injection. The evaluation of the glass transition temperature, the crystallization half-life, and the percent crystallinity shown in Figures 47 and 49 suggests better graphene dispersion at 7.5 microns of ultrasonic amplitude compared to other amplitudes. The crystallization half-life of PET decreases with decreasing molecular weight, but dispersed graphene may be responsible for the increase in half-life at 7.5 μm amplitude. Such observations, along with the melt rheology data presented in Figures 53 and 54, suggest that a 7.5 um sonic amplitude is likely to improve the dispersion of GNP. However, the improvement in dispersion as observed from the thermal analysis was not reflected in the mechanical data.

The effects of mixing time on mechanical properties were investigated while fabricating tensile rods of ultrasonic treated nanocomposites on a microinjection molding system. The nanocomposite samples were injection molded for 1 min, 2 min and 3 min, respectively. From the modulus data presented in Figure 72, it can be seen that the longer mixing time leads to a decrease in the nanocomposite modulus. This may be due to the damage of the polymer due to the longer residence time.

Effect of Graphene Surface Area on the Properties of PET Nanocomposites

In situ polymerized PET nanocomposites suggest that the surface area of graphene nanofibers can be the basis for the difference in the crystallization behavior of PET. Returning to the mechanical properties of the nanocomposite, the difference is not significant enough to conclude. The dispersed nano platelets through ultrasonic treatment will contain different sizes of platelets, which will result in varying the effective average surface area by inducing a wide distribution of nanoplanet aspect ratio. The application of a size-selective approach through centrifugation can help to understand the effect of nanoparticle surface area.

As mentioned herein, one drawback to in situ polymerization is that similar molecular weight polymers are achieved between different batches. From this, not only is the polymerization process not sufficient to produce nanocomposites; It can be seen that the application of secondary techniques such as solid state polymerization can be helpful in addressing the molecular weight gaps.

Effectiveness of Graphene as a Stiffener

The mechanical behavior of PET depends on the type of crystallization of spherical and elongational crystallization. The modulus of the PET crystal was calculated to be 146 GPa based on the modification of the covalent bond. One approach to improving the properties of PET is to increase its crystallinity from the processing method. The PET film sample obtained through biaxial elongation presents 5.4 GPa at 45% crystallinity. Comparing him to the nanocomposite, the modulus for the 10% GNP weight fraction was 5.3 GPa. Improvement by GNP addition is comparable to self-reinforced (stretch-crystallized) PET, with stiffeners 5.5 times stiffer than PET crystals. For the same biaxially elongated sample, when tested along the maximal molecular orientation, the orientation showed a modulus of about 9.1 GPa. From this, it can be seen that the orientation can be further improved for graphene nanofibers during processing of the nanocomposite.

conclusion

Poly (ethylene terephthalate) -graffin nano-platelet (PET-GNP) nanocomposites were exemplified by injection molding. As described herein, PET-GNP nanocomposites were evaluated for dispersion, mechanical and thermal properties. As a result, the PET-GNP nanocomposite exhibits an exponential improvement in Young's modulus in the range of 8% (for the 0.5% GNP weight fraction) to 224% (for the 15% GNP weight fraction) without affecting the PET strength . The addition of graphene nanofibers in a weight fraction of more than 2% was found to affect the fracture strain of PET. In addition, the particular molding system used plays a significant role in influencing the final properties of the nanocomposite. In particular, nanocomposites produced by high-speed injection molding provide a relatively improved modulus.

As described herein, the master-batch method effectively disperses nano-scrap in the PET, wherein the lower GNP content provides better dispersion than the higher GNP content master-batch. Ultrasonic processing of PET generally provides a slight effect on molecular weight and increases its toughness without affecting the Young modulus. Biaxial-screw blending and ultrasonic-assisted biaxial-screw blending cause similar improvements in the Young modulus. In addition, the PET-GNP nanocomposite obtained by biaxial-screw mixing shows the GNP intergranular distance which decreases with increasing concentration. In particular, the 15% nanocomposite exhibits an average GNP particle distance of substantially 520 nm.

The PET-GNP nanocomposites produced by injection molding exhibit selective orientation of GNP in the flow direction to a depth of substantially 200 μm below the surface of the mold. The depth of selective orientation depends on the cooling rate of the PET-GNP nanocomposite. In addition, the presence of GNP affects the crystallization behavior of PET, where the crystallization temperature increases with additional nucleation from graphene and the crystallization half-life (t _1/2 ) increases with increasing GNP content. The crystallinity of PET is generally influenced by the elongation as well as the cooling rate. Strain-induced crystallization is effective in improving the mechanical properties of PET as compared to thermal-induced crystallization. Thus, it will be appreciated that the graphene reinforcement can be optimized by increasing the orientation of the nanosplane along the flow direction and increasing the nanosplane effective surface area.

While the invention has been described in terms of certain variations and illustrative figures, those skilled in the art will recognize that the invention is not limited to the described variations and drawings. It will also be appreciated by those of ordinary skill in the pertinent art that the order of the specific steps may be varied and those variations are contemplated according to variations of the present invention, when the methods and steps described above refer to specific instances occurring in a particular order. Additionally, certain steps may be performed concurrently, as described above, as well as being performed concurrently, if possible, in a parallel process. To the extent that there are variations of the invention that are within the purview of equivalents or disclosures of the invention found in the claims, the patent is also intended to cover such modifications. Accordingly, it is to be understood that this disclosure is not limited by the specific embodiments described herein, but is only limited by the scope of the appended claims.

Claims (15)

A method for producing graphene reinforced polyethylene terephthalate using injection molding,
The master-batch method is used to form the graphene nano-small pellets in a polyethylene terephthalate with a lower graphene content in the polyethylene terephthalate to obtain one or more master-batch pellets, To effectively disperse into the nano platelet content, thereby providing improved dispersion compared to the master-batch of higher graphene nanosepoxide content,
Wherein the polyethylene terephthalate-graphene nanoporous nanocomposite comprises a weight fraction ranging from 0.5% to 15%, wherein the polyethylene terephthalate-graphene nano plastic nanocomposite comprises a polyethylene terephthalate-
Way. The method according to claim 1, wherein the polyethylene terephthalate graphene nano plastic is melt-blended using biaxial-screw extrusion. The method of claim 1, wherein the polyethylene terephthalate-graphene nanoporous nanocomposite is produced using a high speed injection molding process. 3. The method of claim 2, wherein ultrasonic-assisted extrusion is coupled with biaxial-screw extrusion to aid in melt compounding. 5. The method of claim 4, wherein the ultrasonic-assisted extrusion comprises applying ultrasonic waves to the polyethylene terephthalate-graphene nanofiber plate to locally increase the melt temperature as a result of acoustic cavitation. 6. The method of claim 5, wherein the ultrasonic wave comprises an ultrasonic amplitude of 5 m. 6. The method of claim 5, wherein the ultrasonic waves comprise an ultrasonic amplitude of 7.5 [mu] m. The method of claim 1, wherein the weight fraction causes improvement of the Young's modulus without affecting the strength of the polyethylene terephthalate. 5. The method of claim 4, wherein the ultrasonic-assisted extrusion increases the toughness of the polyethylene terephthalate without affecting the Young's modulus. 3. The method of claim 2, wherein the biaxial-screw extrusion is performed by an extruder comprising a co-rotating screw. 2. The method of claim 1, wherein the polyethylene terephthalate-graphene nanoporous nanocomposite is produced by injection molding to exhibit selective orientation of the graphene nanosplane in the flow direction to a depth of substantially 200 [mu] m below the surface of the mold. 12. The method of claim 11 wherein the depth of selective orientation is dependent on the cooling rate of the polyethylene terephthalate-graphene nano plastic nanocomposite. 13. The method of claim 11, wherein the presence of graphene nanopotassium affects the crystallization behavior of the polyethylene terephthalate, wherein the crystallization temperature increases with additional nucleation from the graphene and the crystallization half-life (t _1/2 ) Wherein the graphen increases with increasing nano platelet content. The method according to claim 11, wherein the crystallinity of the polyethylene terephthalate is affected by the cooling rate as well as the elongation. 12. The method of claim 11, wherein the strain-induced crystallization improves one or more mechanical properties of the polyethylene terephthalate as compared to thermal-induced crystallization.

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