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16 pages, 3388 KiB  
Article
Bile Imprint on Parietal Peritoneum of Gilthead Seabream and Red Seabream: Effects of Fasting Duration, Stress, and Ice Storage
by Sofia Brinkmann Bougali, Nafsika Karakatsouli, Christos Balaskas, Konstantinos Petropoulos, Despoina Trampouli, Alkisti Batzina and Pinelopi-Paraskevi Laskari
Fishes 2025, 10(1), 32; https://doi.org/10.3390/fishes10010032 - 15 Jan 2025
Viewed by 304
Abstract
The Mediterranean aquaculture industry has recently been confronted with the appearance of a bile imprint on fish filets, which to-date remains of unknown etiology. This study investigates the involvement of common procedures applied before (fasting), during (confinement), and after (ice storage) fish harvesting. [...] Read more.
The Mediterranean aquaculture industry has recently been confronted with the appearance of a bile imprint on fish filets, which to-date remains of unknown etiology. This study investigates the involvement of common procedures applied before (fasting), during (confinement), and after (ice storage) fish harvesting. Two experiments were designed, one for gilthead seabream (Sparus aurata) and one for red seabream (Pagrus major). The fish were grouped according to fasting duration (1, 2, 3 days), harvesting method (stressed, unstressed), and ice storage (0 h, 48 h). In both species, the imprint appeared in all ice-stored fish for 48 h but not in fresh fish (0 h), the color of the imprint became darker as Days of Fasting increased, stressed fish had darker imprints than unstressed fish, and plasma and bile osmolality and cholesterol were significantly affected by treatments. The histological examination of the gallbladder in red seabream showed great variability in the muscularis thickness and appearance, regardless of treatment. These results are not conclusive as to the cause of the bile imprint appearance. However, they offer a first insight into an issue that bears significant impact in the marketing of aquaculture products and may foster further investigation in the search of the underlying causes of this reoccurring issue. Full article
(This article belongs to the Section Physiology and Biochemistry)
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Figure 1

Figure 1
<p>Experimental design for each trial. DoF: Days of Fasting; S: stressed fish (confinement for 20 min), U: unstressed fish.</p>
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<p>Bile imprint on parietal peritoneum of gilthead seabream (<b>left</b>) and red seabream (<b>right</b>). 0 h: fish sampled immediately after slaughtering; 48 h: fish sampled after 48 h of ice storage in 3 °C; DoF: Days of Fasting.</p>
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<p>Plasma and bile osmolality in gilthead seabream. 0 h: fish sampled immediately after slaughtering; 48 h: fish sampled after 48 h of ice storage in 3 °C; DoF: Days of Fasting; S: stressed fish (confinement for 20 min); U: unstressed fish. &gt; and &lt; denote significantly higher or lower values, respectively; ns: no significance; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001. See <a href="#fishes-10-00032-t002" class="html-table">Table 2</a> for detailed significance of treatments.</p>
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<p>Plasma and bile cholesterol in gilthead seabream. 0 h: fish sampled immediately after slaughtering; 48 h: fish sampled after 48 h of ice storage in 3 °C; DoF: Days of Fasting; S: stressed fish (confinement for 20 min); U: unstressed fish. &gt; denotes significantly higher values, respectively; =, ns: no significance; *** <span class="html-italic">p</span> &lt; 0.001. See <a href="#fishes-10-00032-t002" class="html-table">Table 2</a> for detailed significance of treatments.</p>
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<p>Plasma and bile osmolality in red seabream. 0 h: fish sampled immediately after slaughtering; 48 h: fish sampled after 48 h of ice storage in 3 °C; DoF: Days of Fasting; S: stressed fish (confinement for 20 min); U: unstressed fish. &gt; and &lt; denote significantly higher or lower values, respectively; ns: no significance; *** <span class="html-italic">p</span> &lt; 0.001. See <a href="#fishes-10-00032-t002" class="html-table">Table 2</a> for detailed significance of treatments.</p>
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<p>Plasma and bile cholesterol in red seabream. 0 h: fish sampled immediately after death; 48 h: fish sampled after 48 h of ice storage in 3 °C; DoF: Days of Fasting; S: stressed fish (confinement for 20 min); U: unstressed fish. &gt; denotes significantly higher values, respectively; ≥, ns: no significance; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001. See <a href="#fishes-10-00032-t002" class="html-table">Table 2</a> for detailed significance of treatments.</p>
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<p>A red seabream gallbladder cryostat section stained with hematoxylin and eosin (<b>A</b>) and red seabream cryostat section stained with paraldehyde fuchsin (<b>B</b>). Both sampled fish belong to the same treatment (unstressed, 2 DoF, 0 h). The intestinal wall layers are demarcated by line segments: serosa (blue), smooth muscle layer (orange), and mucosa (green). The muscularis is rather uniform in appearance in A, but connective tissue septa (black drawn lines) dividing the smooth muscle cells in distinct groups are evident in B. Note the difference in the thickness of the gallbladder wall layers by comparing the width of the smooth muscle layer, which may be partly attributed to variable distention; yet, the appearance of the mucosal folds (red block arrows) indicates that this distention is not so extensive as to account for the great difference in the width of the muscularis; the mucosal folds are of comparable size.</p>
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12 pages, 4465 KiB  
Article
Phase Transition and Controlled Zirconia Implant Patterning Using Laser-Induced Shockwaves
by Inomjon Majidov, Yaran Allamyradov, Salizhan Kylychbekov, Zikrulloh Khuzhakulov and Ali Oguz Er
Appl. Sci. 2025, 15(1), 362; https://doi.org/10.3390/app15010362 - 2 Jan 2025
Viewed by 461
Abstract
Zirconia is increasingly favored for dental implants owing to its corrosion resistance, hypoallergenic properties, and superior esthetics, but its biocompatibility remains a challenge. This study explores laser-assisted surface modification to enhance zirconia bioactivity. Zirconia transitions from the monoclinic to the tetragonal phase during [...] Read more.
Zirconia is increasingly favored for dental implants owing to its corrosion resistance, hypoallergenic properties, and superior esthetics, but its biocompatibility remains a challenge. This study explores laser-assisted surface modification to enhance zirconia bioactivity. Zirconia transitions from the monoclinic to the tetragonal phase during sintering, with mixed phases observed in the pre-sintered stage. These transitions are critical for understanding its structural stability and malleability. Grid patterns were imprinted on the green body implant surface using a 1064 nm Nd-YAG laser (Continuum Surelite II, San Jose, CA, USA), with mesh sizes ranging from 7 to 50 µm and depths up to 2 µm, controlled by varying laser fluence, irradiation time, and templates. SEM, AFM, and XRD analyses were used to characterize the surface morphology and crystallography. Protein adsorption studies compared two patterned samples with different surface coverage—the first sample had a patterned area of 0.212 cm2 (27%), while the second sample had a patterned area of 0.283 cm2 (36%)—to a control sample. Protein adsorption increased by 92% in the first and 169% in the second sample, demonstrating a direct correlation between increased pattern area and bioactivity. Enhanced protein adsorption facilitates cell attachment and growth, which are crucial for improving osseointegration. These results underscore the potential of laser-assisted surface modification to optimize zirconia’s performance as a medical implant material. Full article
(This article belongs to the Special Issue Advances of Laser Technologies and Their Applications)
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Figure 1
<p>XRD pattern of zirconia. Sintered (red), pre-sintered (green), and green body (blue) ZrO<sub>2</sub> XRD profiles.</p>
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<p>ZrO<sub>2</sub> patterned via the “graphite method”. Cu (400) mesh TEM grid template at F = 1 J/cm<sup>2</sup>, t = 2 s.</p>
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<p>Zirconia surface patterned via the “aluminum method”. First row: Cu (400) square mesh TEM template at F = 2 J/cm<sup>2</sup>, t = 1 s. Second row: Cu (400) hexagonal mesh grid TEM template at F = 2 J/cm<sup>2</sup>, t = 1 s.</p>
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<p>AFM image, 3D image, and depth profile plot of a patterned zirconia via the aluminum method. Cu(400) hexagonal mesh grid TEM template at F = 2 J/cm<sup>2</sup>, t = ₋1 s. −.</p>
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<p>BSA absorbance as a function of time at 562 nm.</p>
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19 pages, 4678 KiB  
Article
Ionic Crosslinking of Linear Polyethyleneimine Hydrogels with Tripolyphosphate
by Luis M. Araque, Antonia Infantes-Molina, Enrique Rodríguez-Castellón, Yamila Garro-Linck, Belén Franzoni, Claudio J. Pérez, Guillermo J. Copello and Juan M. Lázaro-Martínez
Gels 2024, 10(12), 790; https://doi.org/10.3390/gels10120790 - 3 Dec 2024
Viewed by 891
Abstract
In this work, the mechanical properties of hydrogels based on linear polyethyleneimine (PEI) chemically crosslinked with ethyleneglycoldiglycidyl ether (EGDE) were improved by the ionic crosslinking with sodium tripolyphosphate (TPP). To this end, the quaternization of the nitrogen atoms present in the PEI structure [...] Read more.
In this work, the mechanical properties of hydrogels based on linear polyethyleneimine (PEI) chemically crosslinked with ethyleneglycoldiglycidyl ether (EGDE) were improved by the ionic crosslinking with sodium tripolyphosphate (TPP). To this end, the quaternization of the nitrogen atoms present in the PEI structure was conducted to render a network with a permanent positive charge to interact with the negative charges of TPP. The co-crosslinking process was studied by 1H high-resolution magic angle spinning (1H HRMAS) NMR and X-ray photoelectron spectroscopy (XPS) in combination with organic elemental analysis and inductively coupled plasma mass spectrometry (ICP-MS). In addition, the mobility and confinement of water molecules within the co-crosslinked hydrogels were studied by low-field 1H NMR. The addition of small amounts of TPP, 0.03 to 0.26 mmoles of TPP per gram of material, to the PEI-EGDE hydrogel resulted in an increase in the deformation resistance from 320 to 1080%, respectively. Moreover, the adsorption capacity of the hydrogels towards various emerging contaminants remained high after the TPP crosslinking, with maximum loading capacities (qmax) of 77, 512, and 55 mg g−1 at pH = 4 for penicillin V (antibiotic), methyl orange (azo-dye) and copper(II) ions (metal ion), respectively. A significant decrease in the adsorption capacity was observed at pH = 7 or 10, with qmax of 356 or 64 and 23 or 0.8 mg g−1 for methyl orange and penicillin V, respectively. Full article
(This article belongs to the Special Issue Functionalized Gels for Environmental Applications (2nd Edition))
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Graphical abstract

Graphical abstract
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<p>ATR-FTIR spectra for TPP, PEI-EGDE hydrogel, quaternized PEI-EGDE hydrogel, and P1T0.01, P1T0.05, and P1T0.1 co-crosslinked hydrogels.</p>
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<p>C 1<span class="html-italic">s</span>, O 1<span class="html-italic">s</span>, N 1<span class="html-italic">s,</span> and P 2<span class="html-italic">p</span> high-resolution XPS spectra for the PEI-EGE hydrogel, and P1T0.01, P1T0.05, and P1T0.1 co-crosslinked hydrogels.</p>
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<p>Full (<b>A</b>) and partial magnification (<b>B</b>) of the <sup>1</sup>H HRMAS NMR for the PEI-EGDE, quaternized PEI-EGDE, P1T0.01, P1T0.05, and P1T0.1 hydrogels swelled in D<sub>2</sub>O (MAS rate = 4 kHz).</p>
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<p><sup>31</sup>P direct-polarization <span class="html-italic">ss</span>-NMR spectrum for P1T0.05 hydrogel (MAS rate = 15 kHz). Rotational bands are indicated with an asterisk.</p>
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<p>T<sub>1</sub>–T<sub>2</sub> maps (<b>A</b>) and T<sub>2</sub> projections for the indicated hydrogels (<b>B</b>).</p>
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<p>Swelling capacities (<b>A</b>) and TGA curves (<b>B</b>) for the PEI-EGDE, quaternized PEI-EGDE, P1T0.01, P1T0.05, and P1T0.1 hydrogels.</p>
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<p>Rheological properties of PEI-EGDE, P1T0.01, P1T0.05, and P1T0.1 hydrogels: storage and loss moduli (<b>A</b>) and complex viscosity (<b>B</b>).</p>
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<p>Adsorption kinetics of MO (<b>A</b>), Cu<sup>2+</sup> ions (<b>B</b>) and PEN (<b>C</b>) by the PEI-EGDE and P1T0.01 hydrogels together with the kinetic model fittings.</p>
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<p>Adsorption isotherms of MO (<b>A</b>), Cu<sup>2+</sup> ions (<b>B</b>) and PEN (<b>C</b>) by the PEI-EGDE and P1T0.01 hydrogels together with the fittings to the Langmuir model.</p>
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<p>Synthetic pathways for the co-crosslinking of PEI-EGDE hydrogels (PEI-EGDE-TPP).</p>
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78 pages, 12129 KiB  
Review
Polymers in Physics, Chemistry and Biology: Behavior of Linear Polymers in Fractal Structures
by Hector Eduardo Roman
Polymers 2024, 16(23), 3400; https://doi.org/10.3390/polym16233400 - 2 Dec 2024
Viewed by 1073
Abstract
We start presenting an overview on recent applications of linear polymers and networks in condensed matter physics, chemistry and biology by briefly discussing selected papers (published within 2022–2024) in some detail. They are organized into three main subsections: polymers in physics (further subdivided [...] Read more.
We start presenting an overview on recent applications of linear polymers and networks in condensed matter physics, chemistry and biology by briefly discussing selected papers (published within 2022–2024) in some detail. They are organized into three main subsections: polymers in physics (further subdivided into simulations of coarse-grained models and structural properties of materials), chemistry (quantum mechanical calculations, environmental issues and rheological properties of viscoelastic composites) and biology (macromolecules, proteins and biomedical applications). The core of the work is devoted to a review of theoretical aspects of linear polymers, with emphasis on self-avoiding walk (SAW) chains, in regular lattices and in both deterministic and random fractal structures. Values of critical exponents describing the structure of SAWs in different environments are updated whenever available. The case of random fractal structures is modeled by percolation clusters at criticality, and the issue of multifractality, which is typical of these complex systems, is illustrated. Applications of these models are suggested, and references to known results in the literature are provided. A detailed discussion of the reptation method and its many interesting applications are provided. The problem of protein folding and protein evolution are also considered, and the key issues and open questions are highlighted. We include an experimental section on polymers which introduces the most relevant aspects of linear polymers relevant to this work. The last two sections are dedicated to applications, one in materials science, such as fractal features of plasma-treated polymeric materials surfaces and the growth of polymer thin films, and a second one in biology, by considering among others long linear polymers, such as DNA, confined within a finite domain. Full article
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Figure 1
<p>Molecular dynamics simulations of the quenching at temperature <span class="html-italic">T</span> of a melt of <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <msup> <mn>10</mn> <mn>3</mn> </msup> </mrow> </semantics></math> linear polymer chains with <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> monomers each. Along the chain, nearest-neighbor monomers interact via a harmonic potential, and the remaining intra- and inter-chain monomers interact via a Lennard–Jones potential. Here, <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>≃</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>/</mo> <mn>4</mn> <mo>)</mo> </mrow> <mspace width="0.166667em"/> <msub> <mi>T</mi> <mi>g</mi> </msub> </mrow> </semantics></math> of the glass transition temperature <math display="inline"><semantics> <msub> <mi>T</mi> <mi>g</mi> </msub> </semantics></math>, and time increases from left to right (1, <math display="inline"><semantics> <msup> <mn>10</mn> <mn>2</mn> </msup> </semantics></math>, <math display="inline"><semantics> <msup> <mn>10</mn> <mn>4</mn> </msup> </semantics></math>) a.u. (adapted from [<a href="#B13-polymers-16-03400" class="html-bibr">13</a>]).</p>
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<p>Mesoscale modeling of agglomeration of molecular bottlebrushes. (<b>Upper panel</b>) Bottlebrush architecture displaying the backbone beads (green circles) and the side chain ones (blue circles). In this example, the former are 62 and the side chain length is 6. On the right side, a snapshot of the equilibrated system of 77 bottlebrushes in the case of the lowest repulsion parameter between side chain beads and solvent molecules, the latter considered to be a good solvent. (<b>Lower panel</b>) Bottlebrushes structures at increasing repulsion parameter (poorer solvents) at four simulations time steps (from left to right): T = (<math display="inline"><semantics> <msup> <mn>10</mn> <mn>5</mn> </msup> </semantics></math>, <math display="inline"><semantics> <msup> <mn>10</mn> <mn>6</mn> </msup> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>2</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>6</mn> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>3</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>6</mn> </msup> </mrow> </semantics></math>) (adapted from [<a href="#B22-polymers-16-03400" class="html-bibr">22</a>]).</p>
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<p>Simulations of a surfactant at the water–oil interface with 50% of water and oil between two solid walls under Poiseuille flow. As is apparent, all the surfactant molecules, representing sodium dodecylsulfate and octaethylene glycol monododecyl ether, remained at the water/oil interface with the hydrophilic head (green) beads facing toward the aqueous phase, whereas the hydrophobic tail (purple) beads tended to face the oil phase (adapted from [<a href="#B33-polymers-16-03400" class="html-bibr">33</a>]).</p>
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<p>Simulations of fibrous materials. Shown are three cases with increasing (from left to right) tortuosity, using von Mises–Fisher directional probability distribution functions of random walks, aimed at modeling: Collagen, sintered metal fiber, and fiberboard, respectively (adapted from [<a href="#B47-polymers-16-03400" class="html-bibr">47</a>]).</p>
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<p>A linear polymer attempts to cross a membrane (from left to right) through a small pore, typically of nanometer size (adapted from [<a href="#B56-polymers-16-03400" class="html-bibr">56</a>]).</p>
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<p>Polymerization of 1,6-hexanediol dimethacrylate (HDDMA) aimed at studying the gel-point transition. (<b>Left panel</b>) Structural formula of HDDMA (top image), molecular structure (middle image), and polymerization rule (bottom image). In the latter, a new bond is created between the atoms (A1, A2) if a reaction occurs, and two angle potentials, (B1-A1-A2) and (A1-A2-B2), are added to the chain, where the reactive atom is drawn in red and the potentially reactive one in white, while the inactive one is drawn in black. (<b>Right panel</b>) Snapshot of the polymeric structure for the initial radical concentration of 3% (adapted from [<a href="#B57-polymers-16-03400" class="html-bibr">57</a>]).</p>
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<p>Molecular dynamics simulations of the formation of rubber. (<b>left</b>) The two molecular units, butadiene (<b>left</b>) (C brown sphere, H white sphere), and acrylonitrile (<b>right</b>) (N blue sphere), which are allowed to polymerize to yield a rubber chain. (<b>right</b>) A full optimization of a system of 5 chains, containing 65% of butadiene molecules of 50 units each, modified with hydroxyl groups (OH-) (O red sphere). The cubic system obeys periodic boundary conditions (PBC) and has a cell size of about 30 Å (adapted from [<a href="#B65-polymers-16-03400" class="html-bibr">65</a>]).</p>
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<p>Snapshot of the simulations of poly-m-phenyleneisophthalamide (PMIA) network (C brown dots, H white dots, O blue dots), containing nano-silica particles (<math display="inline"><semantics> <msub> <mi>SiO</mi> <mn>2</mn> </msub> </semantics></math>) (Si yellow spheres). The inset shows a local zoom of hydrogen bonding in the <math display="inline"><semantics> <msub> <mi>SiO</mi> <mn>2</mn> </msub> </semantics></math>/PMIA model (adapted from [<a href="#B74-polymers-16-03400" class="html-bibr">74</a>]).</p>
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<p>Mixtures of linear positively charged (red circles) polymers (linear segments) and mobile negative counterions (blue circles). The <b>left panel</b> illustrates the case of randomly charged polymers, while in the case depicted on the <b>right panel</b>, the polymers are charged only at their ends. The gray thin lines represent similar linear polymers present in the mixture (adapted from [<a href="#B79-polymers-16-03400" class="html-bibr">79</a>]).</p>
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<p>(<b>a</b>–<b>d</b>) The four types of monomers constituting a branched polyetherimide (PEI), used as models in the MC simulations, include: phthalic anhydride (PA), 4,4<math display="inline"><semantics> <mo>′</mo> </semantics></math>-bisphenol A dianhydride (BPADA), m-phenylenediamine (MPD), and tris[4-(4-aminophenoxy)phenyl] ethane (TAPE) (the latter containing three <math display="inline"><semantics> <msub> <mi>NH</mi> <mn>2</mn> </msub> </semantics></math> terminals). The B bead represents a full functional group containing one amine, and the A bead represents a functional group containing a carboxylic anhydride. (<b>e</b>) The condensation reaction between an amine group and a carboxylic anhydride one, taking place in the polymerization of PEIs, is represented by the formation of a bond between beads A and B. (<b>f</b>–<b>i</b>) The polymerization of branched PEIs is represented by the following four reactions: (<b>f</b>) BPADA + MPD (see (<b>b</b>,<b>c</b>) above); (<b>g</b>) BPADA + TAPE (see (<b>b</b>,<b>d</b>) above); (<b>h</b>) PA + MDP (see (<b>a</b>,<b>c</b>) above); (<b>i</b>) PA + TAPE (see (<b>a</b>,<b>d</b>) above) (adapted from [<a href="#B89-polymers-16-03400" class="html-bibr">89</a>]).</p>
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<p>Illustration of the polymerization of aniline. Aniline molecules (green dots) are located inside a porous hydrogel, and by application of an oxidant (e.g., APS [<a href="#B90-polymers-16-03400" class="html-bibr">90</a>]), they start aggregating (larger green particles) by the process known as in situ polymerization (ISP), yielding a nanocomposite (adapted from [<a href="#B90-polymers-16-03400" class="html-bibr">90</a>]).</p>
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<p>Complex polymer chains interacting with graphene (brown network). The colors in the polymer chains represent: N (Blue), O (red), C (green), Zn (brown) (adapted from [<a href="#B101-polymers-16-03400" class="html-bibr">101</a>]).</p>
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<p>Snapshot of an ensemble of PEI chains (green dots) near a functionalized graphene sheet (dark green) containing -OH dangling bonds (red dots) fixed at random locations on the carbon structure (adapted from [<a href="#B103-polymers-16-03400" class="html-bibr">103</a>]).</p>
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<p>Images of polytetrafluoroethylene (PTFE) surfaces obtained using atomic force microscopy (AFM). The PTFE surfaces have been treated with a radio frequency plasma torch for different exposure times as indicated in the figure: 0 min (original surface), 2 min, 26 min, and 50 min. The resulting root-mean-square roughnesses are 22 nm, 33 nm, 68 nm, and 150 nm, respectively (adapted from [<a href="#B135-polymers-16-03400" class="html-bibr">135</a>]).</p>
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<p>(<b>a</b>) Illustration of a divergent synthesis (first two steps) of dendrimer <math display="inline"><semantics> <msub> <mi>G</mi> <mn>3</mn> </msub> </semantics></math> from dendrimers <math display="inline"><semantics> <msub> <mi>G</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>G</mi> <mn>2</mn> </msub> </semantics></math> (D-sequence) and a convergent path via dendrons formation (C-sequence) (adapted from [<a href="#B150-polymers-16-03400" class="html-bibr">150</a>]). (<b>b</b>) The main parameters determining the structural properties of dendrimers [<a href="#B151-polymers-16-03400" class="html-bibr">151</a>].</p>
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<p>Molecular dynamics simulations of pyrolysis. (<b>Upper panel</b>) PI neat: (<b>a</b>) 2000 K, (<b>b</b>) 2200 K, (<b>c</b>) 3000 K. (<b>Lower panel</b>) PI/BNNS: (<b>d</b>) 2000 K, (<b>e</b>) 2200 K, (<b>f</b>) 3000 K (adapted from [<a href="#B164-polymers-16-03400" class="html-bibr">164</a>]).</p>
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<p>Fillers, represented by the full blue circles, dispersed in MMMs. (<b>a</b>) Very low filler contents may not achieve the desired selection effects. (<b>b</b>) At intermediate filler concentrations, one expects to find an optimal filler behavior. In many cases, very low filler contents are actually sufficient to sharply modify the polymer matrix characteristics. This aspect is rather encouraging for large-scale separations at still competitive costs. (<b>c</b>) At too high filler concentrations, one reaches a threshold value leading to particle agglomeration, where the MMMs display a reduced permselectivity [<a href="#B185-polymers-16-03400" class="html-bibr">185</a>] (adapted from [<a href="#B183-polymers-16-03400" class="html-bibr">183</a>]).</p>
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<p>A multi-chain simulation based on the primitive chain network model to study the elongational rheology of polymers, such as polypropylene carbonate. A typical snapshot of a chain with <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>88</mn> </mrow> </semantics></math> units (green lines) is shown, while the thin black lines are the other chains. The thick green lines are segments entangled to the test chain. Periodic B.C. are used (adapted from [<a href="#B223-polymers-16-03400" class="html-bibr">223</a>]).</p>
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<p>Three-dimensional structures of GLP-2 after MD simulations of 100 ns duration. Structures obtained by application of a uniform electric along the <span class="html-italic">z</span>-direction of intensity: (0, 0.4, 0.5, 0.6) V/nm, while for larger fields, the linear structure of the <math display="inline"><semantics> <mi>α</mi> </semantics></math>-helix is restored (adapted from [<a href="#B250-polymers-16-03400" class="html-bibr">250</a>]).</p>
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<p>Interaction of a linear polymer chain (blue beads) with a vesicle, represented here as a polymer ring (red beads) in two dimensions, for different values of the bending stiffness (adapted from [<a href="#B254-polymers-16-03400" class="html-bibr">254</a>]).</p>
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<p>Schematic illustrations of (<b>left panel</b>) an entangled semiflexible polymer network of filaments (black lines) with an embedded tracer filament (red line). (<b>right panel</b>) In this case, the filaments (black lines) are either intertwined or connected by a crosslinker (blue circles). The dashed tubular structure indicates the space available to the tracer (red) filament, which is generally denoted as the reptation tube (adapted from [<a href="#B269-polymers-16-03400" class="html-bibr">269</a>]).</p>
Full article ">Figure 22
<p>Illustration of a translocation process through a bilayer membrane (<math display="inline"><semantics> <mrow> <mn>5</mn> <mi>σ</mi> </mrow> </semantics></math> wall thickness, where <math display="inline"><semantics> <mi>σ</mi> </semantics></math> is the bead diameter), separating the cis-side from the trans-side of the membrane. (<b>a</b>) The negatively charged polymer is driven by the applied electric field from the cis- to the trans-region. Dark-colored beads represent negatively charged monomers and are considered as hydrophilic (polar), while the white ones carry no charge, representing the hydrophobic ones. Different solvent conditions can be described using different values of the attractive LJ potential strength, <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>≥</mo> <mn>0</mn> </mrow> </semantics></math> (i.e., <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> for a good solvent), among hydrophobic sites. (<b>b</b>) Examples showing the translocation from the cis to the trans side for a polymer of <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>90</mn> </mrow> </semantics></math> monomers and two different LJ potential strengths <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>. The colors range from blue (head) to red (tail) of the chains (adapted from [<a href="#B270-polymers-16-03400" class="html-bibr">270</a>]).</p>
Full article ">Figure 23
<p>(<b>a</b>) Myoglobin protein: (a-left image) Native structure showing the <math display="inline"><semantics> <mi>α</mi> </semantics></math>-helices. (a-right image) Coarse-grained model consisting of 151 amino acids, each one represented by a sphere. Polar amino acids are in red color, while hydrophobic (non-polar) ones are in green color. (<b>b</b>) Coarse-grained model of S25 protein consisting of 40 polar amino acids and 25 hydrophobic ones: (b-left image) Starting elongated configuration. (b-right image) Collapsed structure, where the hydrophobic sites have been brought inside the structure, leaving essentially all polar ones on its surface. (<b>c</b>) Planar polymer ‘mushroom’ in the case of 24 S25 proteins: (c-left image) Top view perpendicular to the surface. (c-right image) Side view (adapted from [<a href="#B277-polymers-16-03400" class="html-bibr">277</a>]).</p>
Full article ">Figure 24
<p>Schematic structure of chromatin showing the nucleosome units, inside which DNA pairs are wrapped around histone proteins. Close chromatin (called heterochromatin) is densely packed (<b>left</b> side of the figure) and transcription cannot occur. For transcription to occur, chromatin must be open, yielding the so-called euchromatin (<b>right</b> part of the figure). Condensing DNA into chromosomes (<b>top right</b> part of the figure) prevents DNA tangling and damage during cell division. Courtesy: National Human Genome Research Institute (<a href="https://www.genome.gov/genetics-glossary/Nucleosome" target="_blank">https://www.genome.gov/genetics-glossary/Nucleosome</a> (accessed on 27 November 2024)).</p>
Full article ">Figure 25
<p>Finite elements (FE) mesh, boundary conditions, and load directions for four crucial wrist movements: (<b>a</b>) flexion, (<b>b</b>) extension, (<b>c</b>) radial and (<b>d</b>) ulnar. The Abaqus software (from Dassault Systems [<a href="#B307-polymers-16-03400" class="html-bibr">307</a>]) was employed for the FE analysis of orthoses, where the splint exhibited 11,481 node points and 37,225 linear four-node tetrahedral elements (adapted from [<a href="#B307-polymers-16-03400" class="html-bibr">307</a>]).</p>
Full article ">Figure 26
<p>Illustrations of the Sierpinski fractal carpet in 2D and the fractal sponge in 3D [<a href="#B308-polymers-16-03400" class="html-bibr">308</a>]. Both fractals are generated according to the rule (<math display="inline"><semantics> <mrow> <mi>n</mi> <mo>,</mo> <mi>k</mi> </mrow> </semantics></math>), where <span class="html-italic">n</span> is the length of the initiator and <span class="html-italic">k</span> refers to the number of subunits deleted from the generator (c.f. the red square (‘seed’) shown at the bottom-left part of the figure). The seed is made of <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>×</mo> <mi>n</mi> </mrow> </semantics></math> (here <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>) squares (cubes), of which the central square (<math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>) is deleted for the 2D case, and <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>7</mn> </mrow> </semantics></math> cubes are deleted for the 3D fractal. The process is continued deterministically afterwards. The 2D fractal is shown up to the third iteration, and the 3D sponge up to the fourth one. The sites available to the SAW are shown as white squares in 2D and white cube faces in 3D. The fractal dimensions, <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi mathvariant="normal">f</mi> </msub> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, can be calculated exactly, <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi mathvariant="normal">f</mi> </msub> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>=</mo> <mo form="prefix">ln</mo> <mrow> <mo>(</mo> <msup> <mi>n</mi> <mi>d</mi> </msup> <mo>−</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>/</mo> <mo form="prefix">ln</mo> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, yielding <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi mathvariant="normal">f</mi> </msub> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> <mo>≃</mo> <mn>1.89278</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi mathvariant="normal">f</mi> </msub> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> <mo>≃</mo> <mn>2.72683</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 27
<p>A large percolation cluster at the critical site concentration <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>≃</mo> <mn>0.3116</mn> </mrow> </semantics></math> in 3D on a simple cubic lattice. The cluster has been grown using standard techniques [<a href="#B315-polymers-16-03400" class="html-bibr">315</a>,<a href="#B316-polymers-16-03400" class="html-bibr">316</a>] (see also [<a href="#B310-polymers-16-03400" class="html-bibr">310</a>,<a href="#B317-polymers-16-03400" class="html-bibr">317</a>] for a generalization of the growth rules). The fractal dimension of percolation clusters is <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi mathvariant="normal">f</mi> </msub> <mo>≃</mo> <mn>2.53</mn> <mo>±</mo> <mn>0.02</mn> </mrow> </semantics></math> (see, e.g., [<a href="#B318-polymers-16-03400" class="html-bibr">318</a>]). The different colors represent the length of the shortest (linear) path connecting a site to the seed of the cluster, the latter is located within the red sites and the blue sites have the longest paths (or chemical distances [<a href="#B310-polymers-16-03400" class="html-bibr">310</a>]) from the seed. The cluster contains over a million sites, and the longest chemical distance is 2000 in lattice units (courtesy of Francesco Marini). The fractal dimension of the shortest path is <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>min</mi> </msub> <mo>=</mo> <mn>1.13077</mn> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </semantics></math> in 2D and <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>min</mi> </msub> <mo>=</mo> <mn>1.3756</mn> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow> </semantics></math> in 3D [<a href="#B319-polymers-16-03400" class="html-bibr">319</a>]. The shortest paths are essential when dealing with the transport of matter along the connected sites of the cluster.</p>
Full article ">Figure 28
<p>A linear polymer in a good solvent, modelled by a 2D SAW of 1000 monomers on the square lattice, generated using the reptation method discussed in <a href="#sec3dot2dot4-polymers-16-03400" class="html-sec">Section 3.2.4</a>. SAWs are linear fractals with a fractal dimension <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi mathvariant="normal">f</mi> </msub> <mo>=</mo> <mn>4</mn> <mo>/</mo> <mn>3</mn> </mrow> </semantics></math> in 2D (see e.g., [<a href="#B324-polymers-16-03400" class="html-bibr">324</a>]). Interestingly, the shortest paths in percolation clusters are less compact than SAWs in both 2D and 3D. In the figure, we have highlighted the ends points of the trace of the SAW, denoting them with the letters A and B. The dashed line has a length <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>AB</mi> </msub> <mrow> <mo>(</mo> <mi>N</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, which upon a configurational average depends only on the number of SAW steps, <span class="html-italic">N</span>, between the endpoints (here <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>). Self-trail crossing are forbidden for SAWs.</p>
Full article ">Figure 29
<p>The internal structure of SAWs: The metric <span class="html-italic">ℓ</span>, yielding the distance along the chain between two monomers, compared with the Euclidean distance <span class="html-italic">r</span> in space between them. In the figure, a site is taken as the origin (black–red circle at <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>), from which one can determine the chemical distances <span class="html-italic">ℓ</span> (see the inset for the indicative values of <span class="html-italic">ℓ</span>) of those monomers located on the dashed circle at distance <span class="html-italic">r</span> from the origin. Then, <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">Φ</mi> <mi>SAW</mi> </msub> <mrow> <mo>(</mo> <mi>r</mi> <mo>,</mo> <mo>ℓ</mo> <mo>)</mo> </mrow> </mrow> </semantics></math> is obtained by averaging over all pairs (<span class="html-italic">r</span>,<span class="html-italic">ℓ</span>).</p>
Full article ">Figure 30
<p>The end-to-end topological distance, <math display="inline"><semantics> <mrow> <mo>ℓ</mo> <mo>(</mo> <mi>N</mi> <mo>)</mo> </mrow> </semantics></math> (dashed line), and Euclidean one, <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>(</mo> <mi>N</mi> <mo>)</mo> </mrow> </semantics></math> (red line), for an SAW with <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>9</mn> </mrow> </semantics></math>. Here, <math display="inline"><semantics> <mrow> <mo>ℓ</mo> <mo>(</mo> <mn>9</mn> <mo>)</mo> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>r</mi> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> <mo>=</mo> <msqrt> <mn>20</mn> </msqrt> <mo>≃</mo> <mn>4.472</mn> </mrow> </semantics></math>, in lattice units, which are consistent with the bounds, <math display="inline"><semantics> <mrow> <mn>4.47</mn> <mo>≲</mo> <mo>ℓ</mo> <mo>≲</mo> <mn>6.32</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 31
<p>The scaling function for the end-to-end chemical distance (see Equations (<a href="#FD8-polymers-16-03400" class="html-disp-formula">8</a>) and (<a href="#FD9-polymers-16-03400" class="html-disp-formula">9</a>)), <math display="inline"><semantics> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>/</mo> <msup> <mi>x</mi> <mi>d</mi> </msup> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mo>ℓ</mo> <mo>/</mo> <msup> <mi>N</mi> <mi>ν</mi> </msup> </mrow> </semantics></math>, for SAWs on regular lattices: (<b>a</b>) square lattice and (<b>b</b>) simple cubic lattice, which were obtained using the reptation method. Here, we have used <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>=</mo> <mn>3</mn> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math> in 2D and <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>=</mo> <mn>0.5876</mn> </mrow> </semantics></math> in 3D. The circles correspond to the case <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> steps, and the triangles to <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>400</mn> </mrow> </semantics></math> steps. The red straight lines have slopes, <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> <mo>=</mo> <mn>0.458</mn> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> <mo>=</mo> <mn>0.268</mn> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </semantics></math>, which are consistent with the results, <math display="inline"><semantics> <mrow> <mn>11</mn> <mo>/</mo> <mn>24</mn> <mo>≃</mo> <mn>0.45833</mn> </mrow> </semantics></math> and 0.26711(2), respectively, as reported in <a href="#polymers-16-03400-t001" class="html-table">Table 1</a> (adapted from [<a href="#B344-polymers-16-03400" class="html-bibr">344</a>]).</p>
Full article ">Figure 32
<p>The reptation method on a square lattice. A chain of 9 monomers is shown, where the polymer ends are indicated with the letters A and B. The thick lines between the circles represent the chain links. The left panel illustrates the initial configuration, which can be changed by selecting any of the six possible jumps of the chain starting at any of its ends (red arrows). The right panel depicts the new configuration after end B has moved downward carrying the whole chain with it.</p>
Full article ">Figure 33
<p>(<b>a</b>) Mean exit time steps, <math display="inline"><semantics> <mrow> <mo>〈</mo> <msub> <mi>T</mi> <mi>Exit</mi> </msub> <mo>〉</mo> </mrow> </semantics></math>, to generate a reptation SAW configuration vs. <span class="html-italic">N</span>. The continuous line is a guide to the eyes, and the vertical lines are included for illustration, representing a standard deviation. The numerical data can be well fitted with the form, <math display="inline"><semantics> <mrow> <mover> <mi>T</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>0.7</mn> <mo form="prefix">exp</mo> <mrow> <mo>(</mo> <mn>1.92</mn> <mspace width="0.166667em"/> <msup> <mi>N</mi> <mrow> <mn>1</mn> <mo>/</mo> <mn>4</mn> </mrow> </msup> <mo>)</mo> </mrow> </mrow> </semantics></math>, for <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>&gt;</mo> <mn>20</mn> </mrow> </semantics></math>. Averages over <math display="inline"><semantics> <msup> <mn>10</mn> <mn>4</mn> </msup> </semantics></math> SAW configurations have been performed for the case <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>max</mi> </msub> <mo>=</mo> <msup> <mn>10</mn> <mn>5</mn> </msup> </mrow> </semantics></math> steps and lattice size <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>2000</mn> </mrow> </semantics></math>. (<b>b</b>) Reptating SAWs on a square lattice illustrated in the cases: <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> monomers.</p>
Full article ">Figure 34
<p>Root mean square of the end-to-end distance, <math display="inline"><semantics> <mrow> <mi>R</mi> <mrow> <mo>(</mo> <mi>N</mi> <mo>)</mo> </mrow> <mo>=</mo> <msqrt> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <msup> <mi>R</mi> <mn>2</mn> </msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mi>N</mi> <mo>)</mo> </mrow> </mfenced> </msqrt> </mrow> </semantics></math>, vs. number of monomers, <span class="html-italic">N</span>, on the square lattice (full circles), for chains generated with the reptation method fulfilling the ‘exit’ condition discussed in the text. The straight line has the slope <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>=</mo> <mn>3</mn> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math>, which is in excellent agreement with the expected exact value. The square lattice has size <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>2000</mn> </mrow> </semantics></math>, and averages over <math display="inline"><semantics> <msup> <mn>10</mn> <mn>4</mn> </msup> </semantics></math> SAW configurations have been perfomed.</p>
Full article ">Figure 35
<p>Examples of bridges, <span class="html-italic">B</span> (virtual non-local links between two monomers in space indicated by the red dots), for SAWs generated with the reptation method on a square lattice. (<b>Upper panel</b>) <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> in the cases: (Upper a) Typical configuration with a low number <math display="inline"><semantics> <mrow> <mi>B</mi> <mo>=</mo> <mn>26</mn> </mrow> </semantics></math> bridges, and (Upper b) compact one with <math display="inline"><semantics> <mrow> <mi>B</mi> <mo>=</mo> <mn>67</mn> </mrow> </semantics></math> bridges. (<b>Lower panel</b>) Same as in the upper panel in the case <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>200</mn> </mrow> </semantics></math> for: (Lower a) Typical configuration with <math display="inline"><semantics> <mrow> <mi>B</mi> <mo>=</mo> <mn>83</mn> </mrow> </semantics></math> bridges, and (Lower b) compact one with <math display="inline"><semantics> <mrow> <mi>B</mi> <mo>=</mo> <mn>123</mn> </mrow> </semantics></math> bridges. A bridge is a virtual link connecting two monomers <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </semantics></math> which are nearest neighbors (one lattice unit distance) in space, but they are separated by at least three links along the SAW chain, i.e., <math display="inline"><semantics> <mrow> <mo>|</mo> <mi>i</mi> <mo>−</mo> <mi>j</mi> <mo>|</mo> <mo>≥</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 36
<p>The distribution of the number of bridges, <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>(</mo> <mi>B</mi> <mo>)</mo> </mrow> </semantics></math> (bars), vs. number <span class="html-italic">B</span>, for reptating SAWs in the cases: <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>200</mn> </mrow> </semantics></math>, obtained by generating 5 <math display="inline"><semantics> <msup> <mn>10</mn> <mn>4</mn> </msup> </semantics></math> configurations each. The continuous lines represent Gaussian fits, <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>(</mo> <mi>B</mi> <mo>)</mo> <mo>=</mo> <mi>A</mi> <mspace width="0.166667em"/> <mo form="prefix">exp</mo> <mo>[</mo> </mrow> </semantics></math>−(1/2)((<span class="html-italic">B</span> − <math display="inline"><semantics> <mrow> <msub> <mi>B</mi> <mn>0</mn> </msub> <msup> <mrow> <mo>)</mo> <mo>/</mo> <mi>σ</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>, yielding (<math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>B</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>32.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mn>7.84</mn> </mrow> </semantics></math>) for <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, and (<math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>0.034</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>B</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>68.5</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mn>11.84</mn> </mrow> </semantics></math>) for <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>200</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 37
<p>A cartoon of the type of collapsed DNA chain observed in the experiments. The thick lines represent fluorescently labeled DNA molecules, while the thin lines the remaining ones in the sample studied. The former are the actually observed DNA chains (adapted from [<a href="#B360-polymers-16-03400" class="html-bibr">360</a>]).</p>
Full article ">Figure 38
<p>(<b>a</b>) The chemical structure of an amino acid. It consists of a central carbon atom (red circle), denoted as <math display="inline"><semantics> <msub> <mi mathvariant="normal">C</mi> <mi>α</mi> </msub> </semantics></math>, to which a hydrogen atom and a small molecule, or side-chain residue, are attached. The two remaining <math display="inline"><semantics> <msub> <mi mathvariant="normal">C</mi> <mi>α</mi> </msub> </semantics></math> bonds are occupied by an amino group and a carboxyl one. Amino acids therefore just differ from each other by the type of side chain considered. (<b>b</b>) The formation of a dipeptide: the case of glycylglycine. A peptide bond arises when the O-H subgroup of one amino acid (in this case glycine) joins one H unit of the amino group of the second amino acid (the second glycine here), yielding a water molecule. The newly created C-N link is the peptide bond (red line). Note that there are (<math display="inline"><semantics> <mrow> <mi>N</mi> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>) peptide bonds within a polypeptide chain of <span class="html-italic">N</span> amino acids. Notice that in the case of 20 different amino acids, the size of the sequence space would be <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">N</mi> <mi>s</mi> </msub> <mrow> <mo>(</mo> <mi>N</mi> <mo>)</mo> </mrow> <mo>=</mo> <msup> <mn>20</mn> <mi>N</mi> </msup> <mo>≃</mo> <msup> <mn>10</mn> <mrow> <mn>1.30</mn> <mi>N</mi> </mrow> </msup> </mrow> </semantics></math>, yielding <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">N</mi> <mi>s</mi> </msub> <mrow> <mo>(</mo> <mn>100</mn> <mo>)</mo> </mrow> <mo>≃</mo> <msup> <mn>10</mn> <mn>130</mn> </msup> </mrow> </semantics></math>.</p>
Full article ">Figure 39
<p>(<b>Left panel</b>) A native structure of a lattice protein in a simple cubic lattice having <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>36</mn> </mrow> </semantics></math> amino acids (red circles). The structure is chosen to be maximally compact, where the numbers indicate the index of the <math display="inline"><semantics> <msub> <mi mathvariant="normal">C</mi> <mi>α</mi> </msub> </semantics></math> along the chain. This structure has <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>40</mn> </mrow> </semantics></math> non-local contacts (or bridges) [<a href="#B363-polymers-16-03400" class="html-bibr">363</a>]. The sites (6, 27, 30) (connected by thin dashed lines) represent a sort of ‘hydrophobic core’ of the protein (see text below). (<b>Right panel</b>) The red sites (6,27,30) are denoted as hot sites, and the yellow ones (3,5,11,14,16,28) as warm sites [<a href="#B364-polymers-16-03400" class="html-bibr">364</a>]. Hot and warm sites happen to be first neighbors in the native structure either along the chain or as non-local contacts (see text below).</p>
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<p>Local energy variations for single mutations at a site <span class="html-italic">i</span> in the native structure of <math display="inline"><semantics> <msub> <mi>S</mi> <mn>36</mn> </msub> </semantics></math> as a function of site index <span class="html-italic">i</span>. The colors indicate sites belonging to the same subset, denoted here as hot sites (6,27,30) (red bars) with <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>E</mi> <mrow> <msup> <mi>m</mi> <mo>′</mo> </msup> <mo>,</mo> <mi>m</mi> </mrow> </msub> <mo>&gt;</mo> <mn>2</mn> </mrow> </semantics></math>, warm sites (3,5,11,14,16,28) (yellow bars) with <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>&lt;</mo> <mo>Δ</mo> <msub> <mi>E</mi> <mrow> <msup> <mi>m</mi> <mo>′</mo> </msup> <mo>,</mo> <mi>m</mi> </mrow> </msub> <mo>&lt;</mo> <mn>2</mn> </mrow> </semantics></math>, and cold sites (1,2,4,7,8,9,10…) (green bars) with <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>E</mi> <mrow> <msup> <mi>m</mi> <mo>′</mo> </msup> <mo>,</mo> <mi>m</mi> </mrow> </msub> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
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<p>Off-lattice simulation of the reconstruction of Rubredoxin native structure using holonomic constraints for the intrachain bonds. (<b>Left panel</b>) The evolution of the number of emerging native contacts as a function of time. For comparison, also the number of new contacts not present in the native structure (denoted as wrong ones) are displayed for illustration. (<b>Right panel</b>) Comparison between Rubredoxin native conformation with a reconstructed one. The colors are to facilitate the location of the sites in space (adapted from [<a href="#B370-polymers-16-03400" class="html-bibr">370</a>]).</p>
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<p>The two main forms of polypropylene of unit [<math display="inline"><semantics> <msub> <mi mathvariant="normal">C</mi> <mn>3</mn> </msub> </semantics></math><math display="inline"><semantics> <msub> <mi mathvariant="normal">H</mi> <mn>6</mn> </msub> </semantics></math>]: (<b>a</b>) isotactic PP in which all the chiral carbons (<math display="inline"><semantics> <msub> <mi>CH</mi> <mn>3</mn> </msub> </semantics></math>) have the same configuration; i.e., they are on the same side of the chain. (<b>b</b>) Atactic PP, where the chiral carbons have random orientations. In syndiotactic PP, the chiral units alternate regularly along the chain (see, e.g., [<a href="#B384-polymers-16-03400" class="html-bibr">384</a>] for more details).</p>
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<p>(<b>a</b>) Mixed polymerization connecting an isotactic PP string (blue lines) followed by an atactic one (red lines). (<b>b</b>) Illustration of a piece of material formed by mixed PP chains shown in (<b>a</b>). The blue straight lines represent isotactic PP crystalline zones, giving some rigidity to the structure, while the chosen proportion of atactic PP pieces provide the newtowrk with a desired elasticity.</p>
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<p>(<b>Upper panel</b>) The PTFE (commonly known as teflon) is built upon the tetrafluoroethylene (TFE) unit [<math display="inline"><semantics> <msub> <mi mathvariant="normal">C</mi> <mn>2</mn> </msub> </semantics></math><math display="inline"><semantics> <msub> <mi mathvariant="normal">F</mi> <mn>4</mn> </msub> </semantics></math>] (dashed square). (<b>Lower panel</b>) The typical PTFE helix configuration.</p>
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<p>Illustration of non-treated (NT) (green color) and plasma-treated (PT) (red color) samples of polymeric surfaces, depicted roughly on scale. (<b>a</b>) PCL, the vertical scale for the NT sample is 60 nm, while for the PT one, it is 120 nm. (<b>b</b>) PTFE, the NT scale is 70 nm, and the PT one 790 nm.</p>
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<p>AFM images of PET surfaces after oxygen plasma treatment of different time durations, <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> <mo>)</mo> </mrow> </semantics></math> [min] (adapted from [<a href="#B439-polymers-16-03400" class="html-bibr">439</a>]).</p>
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<p>(<b>a</b>) The treated PET surfaces shown in <a href="#polymers-16-03400-f046" class="html-fig">Figure 46</a> illustrating the hydrophilicity effects on a virtual water droplet positioned on top of the material for different treatment times <span class="html-italic">T</span> [min]. The original surface is at <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, and the vertical scales are: (200, 200, 350, 500) nm. (<b>b</b>) The variation of the contact angle with treatment time expected theoretically (lines) and compared with experimental results (symbols). The images correspond to films of size 10<math display="inline"><semantics> <mrow> <mspace width="0.166667em"/> <mi mathvariant="sans-serif">μ</mi> </mrow> </semantics></math>m × 10<math display="inline"><semantics> <mrow> <mspace width="0.166667em"/> <mi mathvariant="sans-serif">μ</mi> </mrow> </semantics></math>m. In the case of the PET film, the fractal parameters are <math display="inline"><semantics> <mrow> <mi>H</mi> <mo>=</mo> <mn>0.82</mn> <mo>±</mo> <mn>0.02</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi mathvariant="normal">S</mi> </msub> <mo>=</mo> <mn>2.18</mn> <mo>±</mo> <mn>0.02</mn> </mrow> </semantics></math>, while for the PET tissue, they become <math display="inline"><semantics> <mrow> <mi>H</mi> <mo>=</mo> <mn>0.46</mn> <mo>±</mo> <mn>0.02</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi mathvariant="normal">S</mi> </msub> <mo>=</mo> <mn>2.54</mn> <mo>±</mo> <mn>0.02</mn> </mrow> </semantics></math> (see Equation (<a href="#FD31-polymers-16-03400" class="html-disp-formula">31</a>)) (adapted from [<a href="#B439-polymers-16-03400" class="html-bibr">439</a>]).</p>
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<p>Monte Carlo simulations of SAW chains deposition on a flat plasma-activated substrate. (<b>Left panel</b>) Number of attached monomer units, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>att</mi> </msub> <mo>=</mo> <mi>N</mi> <mspace width="0.166667em"/> <msub> <mi>N</mi> <mi>chain</mi> </msub> </mrow> </semantics></math>, with <math display="inline"><semantics> <msub> <mi>N</mi> <mi>chain</mi> </msub> </semantics></math> being the numer of attached chains of <span class="html-italic">N</span> monomers each, as a function of MC steps. Here, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>68</mn> </mrow> </semantics></math> (black line), <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>114</mn> </mrow> </semantics></math> (orange line) and <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>200</mn> </mrow> </semantics></math> (red line). The data collapse above <math display="inline"><semantics> <msup> <mn>10</mn> <mn>6</mn> </msup> </semantics></math> steps suggests that <math display="inline"><semantics> <msub> <mi>N</mi> <mi>att</mi> </msub> </semantics></math> becomes independent of <span class="html-italic">N</span> asymptotically. (<b>Right panel</b>) An example of a simulated PEG thin film, on a substrate of 200 × 200 sites and system height 100 sites, with a lattice constant <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.36</mn> </mrow> </semantics></math> nm, and SAW chains of <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>200</mn> </mrow> </semantics></math> monomers. The total number of attached chains is 834 in this example. The color scale is proportional to monomer height (adapted from [<a href="#B444-polymers-16-03400" class="html-bibr">444</a>]).</p>
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<p>Schematic illustration of an i-motif: a piece of DNA with a knot inside its structure. Such openings make the transcription process possible, providing key regulatory functions (adapted from [<a href="#B449-polymers-16-03400" class="html-bibr">449</a>]).</p>
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<p>(<b>a</b>) Transcription process: Simulation of a long DNA (10,000 units) in a cubic lattice of linear size 100 lattice units, using the reptation method. The red circles represent the transcription targets. A single transcriptor executes a 3D random walk inside the free lattice space and can perform a 1D random walk along the blue path. Once moving along the DNA chain, the transcriptor can leave its 1D walk and move to the 3D free lattice if the target has not been found. Its remains along the DNA for a finite number of steps only, in keeping with the faster diffusion in the free space that accelerates the target-finding process. (<b>b</b>) Simulation of 20 SAW chains with <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> monomers each, using the reptation method, within a cubic lattice of linear size <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math> lattice units with PBC, yielding a total chain occupation of 40% inside the confining cube. Courtesy of Francesco Marini.</p>
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<p>The structure of aminocyanine dyes Cy3-<math display="inline"><semantics> <msub> <mi>NH</mi> <mn>2</mn> </msub> </semantics></math>-(1,2). The red circles represent C atoms, while few H atoms are indicated for illustration. (<b>a</b>) Cy3-<math display="inline"><semantics> <msub> <mi>NH</mi> <mn>2</mn> </msub> </semantics></math>-1: The unit <math display="inline"><semantics> <msub> <mi>NH</mi> <mn>2</mn> </msub> </semantics></math> is close to <math display="inline"><semantics> <msup> <mi mathvariant="normal">N</mi> <mo>+</mo> </msup> </semantics></math>. Here, a 3C-polymethine chain is transformed into a 2C-polymethine chain in the center of the molecule, and the third C is attached to the amine group <math display="inline"><semantics> <msub> <mi>NH</mi> <mn>2</mn> </msub> </semantics></math> rather than to a H. (<b>b</b>) Cy3-<math display="inline"><semantics> <msub> <mi>NH</mi> <mn>2</mn> </msub> </semantics></math>-2: The unit <math display="inline"><semantics> <msub> <mi>NH</mi> <mn>2</mn> </msub> </semantics></math> is at the center of the polymethine chain (adapted from [<a href="#B454-polymers-16-03400" class="html-bibr">454</a>]).</p>
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20 pages, 8023 KiB  
Article
Reaction-Engineering Approach for Stable Rotating Glow-to-Arc Plasma—Key Principles of Effective Gas-Conversion Processes
by Samuel Jaro Kaufmann, Haripriya Chinnaraj, Johanna Buschmann, Paul Rößner and Kai Peter Birke
Catalysts 2024, 14(12), 864; https://doi.org/10.3390/catal14120864 - 26 Nov 2024
Viewed by 546
Abstract
This work presents advancements in a rotating glow-to-arc plasma reactor, designed for stable gas conversion of robust molecules like CO2, N2, and CH4. Plasma-based systems play a critical role in Power-to-X research, offering electrified, sustainable pathways for [...] Read more.
This work presents advancements in a rotating glow-to-arc plasma reactor, designed for stable gas conversion of robust molecules like CO2, N2, and CH4. Plasma-based systems play a critical role in Power-to-X research, offering electrified, sustainable pathways for industrial gas conversion. Here, we scaled the reactor’s power from 200 W to 1.2 kW in a CO2 plasma, which introduced instability due to uplift forces and arc behavior. These were mitigated by integrating silicon carbide (SiC) ceramic foam as a mechanical restriction, significantly enhancing stability by reducing arc movement, confining convection, and balancing volumetric flow within the arc. Using high-speed camera analysis and in situ electronic frequency measurements, we identified dominant frequencies tied to operational parameters, supporting potential in operando monitoring and control. Arc-rotation frequencies from 5 to 50 Hz and higher frequencies (500 to 2700 Hz) related to arc chattering reveal the system’s dynamic response to power and flow changes. Furthermore, refining the specific energy input (SEI) to account for plasma residence time allowed for a more precise calculation of effective SEI, optimizing energy delivery to target molecules. Our findings underscore the reactor’s promise for scalable, efficient gas conversion in sustainable energy applications. Full article
(This article belongs to the Special Issue Plasma Catalysis for Environment and Energy Applications)
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Figure 1

Figure 1
<p>Force balance on arc at plasma reactor with stream direction top to bottom.</p>
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<p>Uplift and gas drag force on the plasma arc plotted over the diameter <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>d</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">t</mi> <mi mathvariant="normal">h</mi> <mi mathvariant="normal">o</mi> <mi mathvariant="normal">d</mi> <mi mathvariant="normal">e</mi> </mrow> </msub> </mrow> </semantics></math> of the cathode at various volume flows <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>V</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math>.</p>
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<p>Instabilities of plasma power: Power over time (<b>f</b>); arc phenomena (right): (<b>a</b>) arc chattering; (<b>b</b>) breakdown; (<b>c</b>) shortcut; (<b>d</b>) and (<b>e</b>) spiralizing.</p>
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<p>Timesteps of arc phenomena shortcut and arc chattering at operating conditions of <math display="inline"><semantics> <mrow> <mi>B</mi> <mo>=</mo> <mn>4.5</mn> <mo> </mo> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">T</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>V</mi> </mrow> <mo>˙</mo> </mover> <mo>=</mo> <mn>2</mn> <mo> </mo> <mi>S</mi> <mi>L</mi> <mi>M</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>=</mo> <mn>500</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>.</p>
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<p>Silicon carbide ceramic foam included in the reactor.</p>
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<p>Plasma stability without SiC and with SiC at a magnetic flux density of <math display="inline"><semantics> <mrow> <mi>B</mi> <mo>=</mo> <mn>4.5</mn> <mo> </mo> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">T</mi> </mrow> </semantics></math>, a volume flow of <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>V</mi> </mrow> <mo>˙</mo> </mover> <mo>=</mo> <mn>2</mn> <mo> </mo> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">M</mi> <mo>,</mo> </mrow> </semantics></math> and at input voltages of <math display="inline"><semantics> <mrow> <mi>U</mi> <mo>=</mo> <mn>300</mn> <mo>…</mo> <mn>500</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>.</p>
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<p>Plasma stability without SiC and with SiC for different operating conditions.</p>
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<p>Changes in plasma power and rotational arc frequency at different magnetic flux densities, mass flow, and voltages.</p>
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<p>Arc tracing through SiC at different magnetic fields, voltages, and mass flows. The conditions lead to rotational frequencies of (<b>a</b>) 11 Hz at 7 mT, 6 SLM, and 300 V; (<b>b</b>) 18 Hz at 7 mT, 6 SLM, and 500 V; (<b>c</b>) 36 Hz at 9.5 mT, 6 SLM, and 500 V; and (<b>d</b>) 46 Hz at 9.5 mT, 10 SLM, and 500 V.</p>
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<p>Curve of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mi mathvariant="normal">p</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> with stable operating conditions (9.5 mT, 6 SLM, and 500 V), measured with the current probe and FFT spectrum (right), plotting the signal amplitude over the frequency.</p>
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<p>Curve of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mi mathvariant="normal">p</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> over the first 100 ms of an experiment for different magnetic flux densities <math display="inline"><semantics> <mrow> <mi>B</mi> <mo>=</mo> <mn>4.5</mn> <mo>…</mo> <mn>9.5</mn> <mo> </mo> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">T</mi> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <mi>V</mi> <mo>=</mo> <mn>6</mn> <mo> </mo> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">M</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>=</mo> <mn>500</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>.</p>
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<p>Spectral power density at different variations in magnetic flux density, volume flow, and input voltage.</p>
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<p>Spectral power density at different variations in the humidity (<b>left</b>) and the dominant frequencies over the humidity (<b>right</b>).</p>
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<p>Arc images of experiments with different operating conditions with noted arc diameters.</p>
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<p>Changes in specific energy input and the number of theoretical arc contacts with molecules at different magnetic flux densities, mass flow, and voltages.</p>
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<p>Experimental setup. Electrical components: plasma driver, DC power supply. Reactor components: electrodes, magnet holder, mass flow controller. Analytical components: oscilloscope, current probe, high-speed camera.</p>
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<p>(<b>a</b>) Voltage–current graph with discharge modes; (<b>b</b>) equivalent electrical circuit.</p>
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34 pages, 1382 KiB  
Review
Molecular Characteristics and Processing Technologies of Dairy Products from Non-Traditional Species
by Isabela Pérez Núñez, Rommy Díaz, John Quiñones, Ailín Martínez, Lidiana Velázquez, Rodrigo Huaiquipán, Daniela Tapia, Alex Muñoz, Marcos Valdés, Néstor Sepúlveda and Erwin Paz
Molecules 2024, 29(22), 5427; https://doi.org/10.3390/molecules29225427 - 18 Nov 2024
Viewed by 1708
Abstract
Non-bovine dairy animals, commonly referred to as non-traditional dairy species, include goats, sheep, yaks, buffalo, donkeys, alpacas, llamas, and other less commonly farmed species. These animals have been integral to livestock systems since ancient times, providing milk and other essential products. Despite their [...] Read more.
Non-bovine dairy animals, commonly referred to as non-traditional dairy species, include goats, sheep, yaks, buffalo, donkeys, alpacas, llamas, and other less commonly farmed species. These animals have been integral to livestock systems since ancient times, providing milk and other essential products. Despite their historical significance, dairy production from many of these species remains predominantly confined to rural areas in developing countries, where scientific advancements and technical improvements are often limited. As a consequence of this, the scientific literature and technological developments in the processing and characterization of dairy products from these species have lagged behind those for cow’s milk. This review aims to compile and analyze existing research on dairy products derived from non-traditional animals, focusing on their molecular characteristics, including proteins (alpha, beta, kappa, and total casein), fats (cholesterol and total fat), lactose, albumin, ash, total solids, and somatic cell count, among others, for each of these species. Additionally, we discuss emerging technologies employed in their processing, encompassing both non-thermal methods (such as high-pressure processing, pulsed electric fields, ultrasound processing, UV-C irradiation, gamma radiation, microfiltration, and cold plasma processing) and thermal methods (such as ohmic heating). This review also explores the specific potential applications and challenges of implementing these technologies. By synthesizing recent findings, we aim to stimulate further research into innovative technologies and strategies that can enhance the quality and yield of non-bovine dairy products. Understanding the unique properties of milk from these species may lead to new opportunities for product development, improved processing methods, and increased commercialization in both developing and developed markets. Full article
(This article belongs to the Special Issue Bioproducts for Health III)
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Graphical abstract
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<p>Main benefits of non-traditional animals’ milk.</p>
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<p>Graphs displaying the total protein, fat, and lactose content in the milk of sheep, goats, donkeys, zebus, yaks, buffalo, camels, reindeer, llamas, and alpacas. Total protein from sheep [<a href="#B158-molecules-29-05427" class="html-bibr">158</a>], goat [<a href="#B157-molecules-29-05427" class="html-bibr">157</a>], donkey [<a href="#B159-molecules-29-05427" class="html-bibr">159</a>], zebu [<a href="#B160-molecules-29-05427" class="html-bibr">160</a>], yak [<a href="#B161-molecules-29-05427" class="html-bibr">161</a>], buffalo [<a href="#B162-molecules-29-05427" class="html-bibr">162</a>], camel [<a href="#B161-molecules-29-05427" class="html-bibr">161</a>], reindeer [<a href="#B162-molecules-29-05427" class="html-bibr">162</a>], llama [<a href="#B162-molecules-29-05427" class="html-bibr">162</a>] and alpaca [<a href="#B162-molecules-29-05427" class="html-bibr">162</a>]; total fats from sheep [<a href="#B158-molecules-29-05427" class="html-bibr">158</a>], goat [<a href="#B27-molecules-29-05427" class="html-bibr">27</a>], donkey [<a href="#B159-molecules-29-05427" class="html-bibr">159</a>], zebu [<a href="#B160-molecules-29-05427" class="html-bibr">160</a>], yak [<a href="#B163-molecules-29-05427" class="html-bibr">163</a>], buffalo [<a href="#B162-molecules-29-05427" class="html-bibr">162</a>], camel [<a href="#B161-molecules-29-05427" class="html-bibr">161</a>], reindeer [<a href="#B162-molecules-29-05427" class="html-bibr">162</a>], llama [<a href="#B162-molecules-29-05427" class="html-bibr">162</a>] and alpaca [<a href="#B162-molecules-29-05427" class="html-bibr">162</a>]; and total lactose from sheep [<a href="#B164-molecules-29-05427" class="html-bibr">164</a>], goat [<a href="#B164-molecules-29-05427" class="html-bibr">164</a>], donkey [<a href="#B159-molecules-29-05427" class="html-bibr">159</a>], zebu [<a href="#B160-molecules-29-05427" class="html-bibr">160</a>], yak [<a href="#B161-molecules-29-05427" class="html-bibr">161</a>], buffalo [<a href="#B162-molecules-29-05427" class="html-bibr">162</a>], camel [<a href="#B161-molecules-29-05427" class="html-bibr">161</a>], reindeer [<a href="#B162-molecules-29-05427" class="html-bibr">162</a>], llama [<a href="#B162-molecules-29-05427" class="html-bibr">162</a>] and alpaca [<a href="#B162-molecules-29-05427" class="html-bibr">162</a>] are shown. Data is presented as grams per 100 g of milk, with values sourced from the indicated references.</p>
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10 pages, 2270 KiB  
Article
Study of the Pure Deuterium Fuel Burning Rate in Z-Pinch Devices with Magneto-Inertial Confinement
by Olzhas Bayakhmetov and Assylkhan Azamatov
Energies 2024, 17(21), 5297; https://doi.org/10.3390/en17215297 - 24 Oct 2024
Viewed by 718
Abstract
The burning rate of pure deuterium (D-D) fuel in Z-pinch devices with magneto-inertial confinement was studied in this paper. The system of particle and energy balance equations for D-D fuel burning with a mixed D-T-3He fusion cycle (D-D, D-T, and D- [...] Read more.
The burning rate of pure deuterium (D-D) fuel in Z-pinch devices with magneto-inertial confinement was studied in this paper. The system of particle and energy balance equations for D-D fuel burning with a mixed D-T-3He fusion cycle (D-D, D-T, and D-3He reactions) was solved numerically, taking into account the densities of all reacted and produced ions (protons, deuterium, tritium, helium-3, and alpha-particles). The obtained results indicate that effective D-D fusion in Z-pinch devices can be successfully achieved under conditions of a hot, dense plasma with an initial temperature of 31 keV or higher. The initial ion density of deuterium and electron density were equal due to quasi-neutrality condition of the plasma, with both reaching 1024 m−3. Although the obtained results show that the burning rate of D-D fuel is approximately 2.3 times slower and its power density notably lower than that of D-T fuel, pure deuterium plasma can be considered as a promising alternative to well-studied deuterium–tritium plasma, with potential future applications in magneto-inertial fusion (MIF) facilities. Full article
(This article belongs to the Section I2: Energy and Combustion Science)
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<p>D-D fuel burning scheme in a Z-pinch device with fast laser ignition.</p>
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<p>Change in ion densities and plasma temperature over time for D-D fuel.</p>
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<p>Power density of charged particles, bremsstrahlung radiation, and neutrons depending on time for D-D fuel.</p>
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<p>Energy density of charged ions, neutrons, and bremsstrahlung radiation.</p>
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<p>Ratio between charged ion energy and total fusion energy.</p>
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<p>Fusion power density of charged ions for D-D and D-T fuel in a Z-pinch device.</p>
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11 pages, 3916 KiB  
Article
Nonlinear Optical Microscopic Imaging for Real-Time Gaseous Chemical Sensing
by Gombojav O. Ariunbold, Bryan Semon, Logan Carlson and Thejesh N. Bandi
Photonics 2024, 11(10), 959; https://doi.org/10.3390/photonics11100959 - 13 Oct 2024
Viewed by 846
Abstract
Nonlinear optical microscopic imaging techniques have advanced for chemically sensitive imaging of solid and liquid samples but lack advancements for gaseous samples. In this work, wide-field three-color ultrafast coherent anti-Stokes Raman scattering microscopy is implemented for selectively imaging the ambient nitrogen gas. Our [...] Read more.
Nonlinear optical microscopic imaging techniques have advanced for chemically sensitive imaging of solid and liquid samples but lack advancements for gaseous samples. In this work, wide-field three-color ultrafast coherent anti-Stokes Raman scattering microscopy is implemented for selectively imaging the ambient nitrogen gas. Our technique operates by capturing a series of spectrally selected images with a rate of 5–10 frames per second. The recorded data are analyzed both qualitatively and quantitatively. This technique has been demonstrated to be sensitive to a variation of approximately 1011 nitrogen molecules in ambient air confined within a microscopic volume of 10 μm by 50 μm by 50 μm. We believe that our approach can potentially be extended toward real-time, in situ chemical imaging of the microscopic dynamics of gases, for example, in ammonia for nitrogen cycle, greenhouse gases for environmental pollution, plant fertilization regulation for precision agriculture, or byproducts produced from lower-temperature plasmas. Full article
(This article belongs to the Special Issue Nonlinear Optics and Hyperspectral Polarization Imaging)
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<p>(Color online) A schematic of the wide-field three-color ultrafast imaging CARS microscopy. (<b>Top</b>) Three beams are produced by the fs laser combined with the NOPA. Timing of the probe at 520 nm (green) and pump at 830 nm (orange) is adjusted by two delay stages. The Stokes beam (maroon) is a residual of 1035 nm laser beam. (<b>Bottom</b>) All three beams are combined by using the two dichromatic mirrors and focused by the optical lens in the air. The CARS signal is collected by the long working distance objective lens by using shortpass and bandpass filters. Combined with its compatible tube lens, they form an image that is magnified 100 times. Data are recorded using a spectrograph with imaging EMCCD. When a grating is used, and the entrance slit is closed, the spectral data are recorded. While the slit is wide open and the mirror is in place, image data are captured. Images are saved either in a single file in accumulation mode, that is, accumulated (added) up all the images, or are saved in separate files in kinetic mode.</p>
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<p>(Color online) (<b>A</b>) The raw spectral data. The peak corresponds to an anti-Stokes Raman shift at −2330 cm<sup>−1</sup> for the N<sub>2</sub> Raman active vibrational mode. (<b>B</b>) The background subtracted the spectrum. (<b>C</b>) The image resulting from 100 accumulations of 0.2 s exposure time per frame when the pump and Stokes beams are blocked and only the probe beam is present, and (<b>D</b>) when the probe beam is blocked and the other two are present. (<b>E</b>) All three beams are present. Image size is 150 μm by 150 μm.</p>
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<p>(Color online) Individual images are captured with an exposure time of 0.1 s without any accumulation: (<b>A</b>) All beams are blocked. (<b>B</b>) The pump and Stokes are blocked, but the probe is present. (<b>C</b>) The pump beam is blocked and the other two are present. (<b>D</b>) The probe is blocked, but the other two are present. (<b>E</b>) All three beams are present. (<b>F</b>) The image in (<b>E</b>) is denoised using an empirical Bayesian method. Image size is 150 μm by 150 μm.</p>
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<p>(Color online) Mosaics montaged with 99 frames (Grayscale images converted from raw data): (<b>Left</b>) Scan 1. All three beams are present with 0.2 s exposure. (<b>Right</b>) Scan 2. All three beams are present with 0.2 s exposure. Image size of each frame is 113 μm by 113 μm. Frames are separated by white lines. The color scale is the same for all scans in each sequence of 99 frames.</p>
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<p>(Color online) Mosaics montaged with 99 frames (Grayscale images converted from raw data): (<b>Left</b>) Scan 3. All three beams are present with 0.2 s exposure. (<b>Right</b>) Scan 4. All three beams are present with 0.1 s exposure. Image size of each frame is 113 μm by 113 μm. Frames are separated by white lines. The color scale is the same for all scans in each sequence of 99 frames.</p>
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<p>(Color online) Mosaics montaged with 99 frames (Grayscale images converted from raw data): (<b>Left</b>) Scan 5. All three beams are present with 0.1 s exposure. (<b>Right</b>) Scan 6. Only the probe beam is present with 0.1 s exposure. Image size of each frame is 113 μm by 113 μm. Frames are separated by white lines.</p>
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<p>(Color online) Mean images after averaging overall 99 raw data frames: Images (<b>A</b>), (<b>B</b>), (<b>C</b>), (<b>D</b>), (<b>E</b>), and (<b>F</b>) correspond to scans 1, 2, 3, 4, 5, and 6, respectively. (<b>G</b>) A histogram of average counts for these six scans. Image size is 150 μm by 150 μm.</p>
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<p>(Color online) The noise fluctuations in counts as percentages for the average counts from <a href="#photonics-11-00959-f007" class="html-fig">Figure 7</a>G.</p>
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<p>(Color online) The normalized two-dimensional cross-correlation calculations between the successive frames in <a href="#photonics-11-00959-f004" class="html-fig">Figure 4</a>, <a href="#photonics-11-00959-f005" class="html-fig">Figure 5</a> and <a href="#photonics-11-00959-f006" class="html-fig">Figure 6</a>. Insets: (<b>a</b>) A 3D view of the cross-correlation plot and (<b>b</b>) the six values at the centers of the 3D plots.</p>
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26 pages, 8426 KiB  
Article
Development and Testing of a Helicon Plasma Thruster Based on a Magnetically Enhanced Inductively Coupled Plasma Reactor Operating in a Multi-Mode Regime
by Anna-Maria Theodora Andreescu, Daniel Eugeniu Crunteanu, Maximilian Vlad Teodorescu, Simona Nicoleta Danescu, Alexandru Cancescu, Adrian Stoicescu and Alexandru Paraschiv
Appl. Sci. 2024, 14(18), 8308; https://doi.org/10.3390/app14188308 - 14 Sep 2024
Viewed by 1217
Abstract
A disruptive Electric Propulsion system is proposed for next-generation Low-Earth-Orbit (LEO) small satellite constellations, utilizing an RF-powered Helicon Plasma Thruster (HPT). This system is built around a Magnetically Enhanced Inductively Coupled Plasma (MEICP) reactor, which enables acceleration of quasi-neutral plasma through a magnetic [...] Read more.
A disruptive Electric Propulsion system is proposed for next-generation Low-Earth-Orbit (LEO) small satellite constellations, utilizing an RF-powered Helicon Plasma Thruster (HPT). This system is built around a Magnetically Enhanced Inductively Coupled Plasma (MEICP) reactor, which enables acceleration of quasi-neutral plasma through a magnetic nozzle. The MEICP reactor features an innovative design with a multi-dipole magnetic confinement system, generated by neodymium iron boron (NdFeB) permanent magnets, combined with an azimuthally asymmetric half-wavelength right (HWRH) antenna and a variable-section ionization chamber. The plasma reactor is followed by a solenoid-free magnetic nozzle (MN), which facilitates the formation of an ambipolar potential drop, enabling the conversion of electron thermal energy into ion beam energy. This study explores the impact of an inhomogeneous magnetic field on the heating mechanism of the HPT and highlights its multi-mode operation within a pulsed power range of 200 to 500 W of RF. The discharge state, characterized by high-energy electron-excited ions and low-energy excited neutral particles in the plasma plume, was analyzed using optical emission spectroscopy (OES). The experimental testing campaign, conducted under pulsed power excitation, reveals that, as RF input power increases, the MEICP reactor transitions from inductive (H-mode) to wave coupling (W-mode) discharge modes. Spectrograms, electron temperature, and plasma density measurements were obtained for the Helicon Plasma Thruster within its operational envelope. Based on OES data, the ideal specific impulse was estimated to exceed 1000 s, highlighting the significant potential of this technology for future LEO/VLEO space missions. Full article
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<p>Block diagram of a HPT based on MEICP reactor and inhomogeneous magnetic field.</p>
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<p>Representation of the electric field pattern of the <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math> azimuthal mode in an MEICP reactor.</p>
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<p>CAD rendering of the Helicon Plasma Thruster Breadboard Model (HPT-BM).</p>
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<p>Cooper-based half-wavelength right helical (HWRH) antenna used to excite helicon waves within the MEICP reactor.</p>
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<p>(<b>a</b>) Relative displacement of the PMs within the multi-cusps magnetic confinement system and (<b>b</b>) a cross-section view depicting the magnetic field flux density profile within the MEICP reactor.</p>
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<p>(<b>a</b>) A schematic representation illustrating the conversion of azimuthal momentum into axial momentum within a magnetic nozzle, where B is the applied magnetic field and (<b>b</b>) the formation of the expanding magnetic field downstream of the MEICP reactor.</p>
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<p>The Helicon Plasma Thruster Breadboard Model during its commissioning procedure.</p>
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<p>Helicon Plasma Thruster experimental system schematic.</p>
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<p>A schematic sketch of the communication connection overseen for the HPT experimental testing campaign.</p>
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<p>RF envelope-auto ignition of the MECP reactor under a pulsed wave modulation (PWM) regime.</p>
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<p>The representative images of the HPT during a testing sequence in Pulse Width Modulation (PWM) mode, operating with a forward power of 200 W (<b>a</b>) 15 ms, (<b>b</b>) 30 ms, (<b>c</b>) 45 ms, and (<b>d</b>) 60 ms after ignition initialization. The sequence features 1 s of RF on-time followed by 0.4 s of RF off-time.</p>
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<p>ICP mode emission spectrum of the Helicon Plasma Thruster with a forward power of 300 W and 1 s of RF on-time followed by 0.4 s of RF off-time.</p>
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<p>The representative images of the HPT during a testing sequence in Pulse Width Modulation (PWM) mode, operating with a forward power of 500 W (<b>a</b>) 15 ms, (<b>b</b>) 30 ms, (<b>c</b>) 45 ms, and (<b>d</b>) 60 ms after ignition initialization. The sequence features 1 sec of RF on-time followed by 0.4 s of RF off-time.</p>
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<p>WHP mode emission spectrum of the Helicon Plasma Thruster with a forward power of 500 W and 1 sec of RF on-time followed by 0.4 s of RF off-time.</p>
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<p>Comparison of the Argon Plasma Spectra for ICP and Helicon modes of operation in 404–493 nm.</p>
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14 pages, 30297 KiB  
Article
Production of Spheroidized Micropowders of W-Ni-Fe Pseudo-Alloy Using Plasma Technology
by Andrey Samokhin, Nikolay Alekseev, Aleksey Dorofeev, Andrey Fadeev and Mikhail Sinaiskiy
Metals 2024, 14(9), 1043; https://doi.org/10.3390/met14091043 - 13 Sep 2024
Cited by 1 | Viewed by 735
Abstract
The process of obtaining powders from the 5–50 μm fraction of a W-Ni-Fe system consisting of particles with predominantly spherical shapes was investigated. Experimental studies on the plasma–chemical synthesis of a nanopowder composed of WNiFe-90 were carried out in a plasma reactor with [...] Read more.
The process of obtaining powders from the 5–50 μm fraction of a W-Ni-Fe system consisting of particles with predominantly spherical shapes was investigated. Experimental studies on the plasma–chemical synthesis of a nanopowder composed of WNiFe-90 were carried out in a plasma reactor with a confined jet flow. A mixture of tungsten trioxide, nickel oxide, and iron oxide powders interacted with a flow of hydrogen-containing plasma generated in an electric-arc plasma torch. The parameters of the spray-drying process and the composition of a suspension consisting of WNiFe-90 nanoparticles were determined, which provided mechanically strong nanopowder microgranules with a rounded shape and a homogeneous internal structure that contained no cavities. The yield of the granule fraction under 50 μm was 60%. The influence of the process parameters of the plasma treatment of the nanopowder microgranules in the thermal plasma flow on the degree of spheroidization and the microstructure of the obtained particles, seen as their bulk density and fluidity, was established. It was shown that the plasma spheroidization of the microgranules of the W-Ni-Fe system promoted the formation of a submicron internal structure in the obtained spherical particles, which were characterized by an average tungsten grain size of 0.7 μm. Full article
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<p>SEM (<b>a</b>) and TEM (<b>b</b>) images of the nanopowder of the W-Ni-Fe system.</p>
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<p>Dependences of the W, Ni, and Fe contents in the W-Ni-Fe-O-H equilibrium system on temperature at different levels of excess hydrogen.</p>
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<p>Particle size distribution of nanopowder of W-Ni-Fe system (I—differential distribution curve, II—integral distribution curve).</p>
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<p>XRD pattern of W-Ni-Fe nanopowder.</p>
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<p>SEM image of W-Ni-Fe nanopowder (<b>a</b>). SEM + EDS images of distribution maps of W (<b>b</b>), Ni (<b>c</b>), and Fe (<b>d</b>) in nanopowder.</p>
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<p>SEM image of the target fraction of the microgranules.</p>
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<p>SEM image of a microgranule after etching with gallium ions (<b>a</b>) and SEM+EDS images of the distribution maps of the W (<b>b</b>), Ni (<b>c</b>), and Fe (<b>d</b>) in its volume.</p>
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<p>Comparison of the particle size distribution results of samples of microgranules of the fraction between 20 and 60 μm with sucrose contents 0.5 wt. % (<b>a</b>), 2.0 wt. % (<b>b</b>) and 5.0 wt. % (<b>c</b>) before and after testing the abrasion resistance when the samples were mixed in a Turbula C 2.0 mixer for 80 min at 40 rpm (mode I) or 160 min at 70 rpm (mode II).</p>
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<p>SEM image of spheroidized W-Ni-Fe micropowder after nanoparticle removal.</p>
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<p>XRD pattern of spheroidized W-Ni-Fe micropowder.</p>
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<p>Particle size distribution of spheroidized W-Ni-Fe micropowder after removal of nanoparticles (I—differential distribution curve, II—integral distribution curve).</p>
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<p>SEM images of the particle structure of spheroidized W-Ni-Fe micropowder (<b>a</b>) and estimation of the tungsten grain diameter of an individual particle (<b>b</b>) after mechanical grinding.</p>
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<p>SEM image of structure of spheroidized W-Ni-Fe microparticle after mechanical grinding (<b>a</b>) and SEM+EDS images of distribution maps of W (<b>b</b>), Ni (<b>c</b>), and Fe (<b>d</b>) in its volume.</p>
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20 pages, 6592 KiB  
Article
Multiscale Modeling of Plasma-Assisted Non-Premixed Microcombustion
by Giacomo Cinieri, Ghazanfar Mehdi and Maria Grazia De Giorgi
Aerospace 2024, 11(9), 697; https://doi.org/10.3390/aerospace11090697 - 26 Aug 2024
Viewed by 3401
Abstract
This work explores microcombustion technologies enhanced by plasma-assisted combustion, focusing on a novel simulation model for a Y-shaped device with a non-premixed hydrogen-air mixture. The simulation integrates the ZDPlasKin toolbox to determine plasma-produced species concentrations to Particle-In-Cell with Monte Carlo Collision analysis for [...] Read more.
This work explores microcombustion technologies enhanced by plasma-assisted combustion, focusing on a novel simulation model for a Y-shaped device with a non-premixed hydrogen-air mixture. The simulation integrates the ZDPlasKin toolbox to determine plasma-produced species concentrations to Particle-In-Cell with Monte Carlo Collision analysis for momentum and power density effects. The study details an FE-DBD plasma actuator operating under a sinusoidal voltage from 150 to 325 V peak-to-peak and a 162.5 V DC bias. At potentials below 250 V, no hydrogen dissociation occurs. The equivalence ratio fitting curve for radical species is incorporated into the plasma domain, ensuring local composition accuracy. Among the main radical species produced, H reaches a maximum mass fraction of 8% and OH reaches 1%. For an equivalence ratio of 0.5, the maximum temperature reached 2238 K due to kinetic and joule heating contributions. With plasma actuation with radicals in play, the temperature increased to 2832 K, and with complete plasma actuation, it further rose to 2918.45 K. Without plasma actuation, the temperature remained at 300 K, reflecting ambient conditions and no combustion phenomena. At lower equivalence ratios, temperatures in the plasma area consistently remained around 2900 K. With reduced thermal power, the flame region decreased, and at Φ = 0.1, the hot region was confined primarily to the plasma area, indicating a potential blow-off limit. The model aligns with experimental data and introduces relevant functionalities for modeling plasma interactions within microcombustors, providing a foundation for future validation and numerical models in plasma-assisted microcombustion applications. Full article
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<p>General scheme of the FE-DBD offset planar actuator.</p>
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<p>Schematic representation of the computation sequence for the PIC-MCC method.</p>
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<p>Equivalent lumped electrical circuit schematic of the plasma actuator.</p>
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<p>Main stages of the proposed method.</p>
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<p>LBV for the hydrogen–air mixture for T = 300 K and <span class="html-italic">p</span> = 1 atm vs. experimental data and prior kinetic models [<a href="#B54-aerospace-11-00697" class="html-bibr">54</a>,<a href="#B55-aerospace-11-00697" class="html-bibr">55</a>,<a href="#B56-aerospace-11-00697" class="html-bibr">56</a>,<a href="#B57-aerospace-11-00697" class="html-bibr">57</a>].</p>
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<p>(<b>a</b>) Temperature midline flame area for three different grid sizes; (<b>b</b>) flame length comparison between numerical studies and experimental data [<a href="#B44-aerospace-11-00697" class="html-bibr">44</a>] at different Φ.</p>
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<p>Applied E<sub>N</sub> for each pulse.</p>
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<p>Water vapor concentration comparison between numerical and experimental results [<a href="#B58-aerospace-11-00697" class="html-bibr">58</a>].</p>
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<p>(<b>a</b>) Mean electron particle density in the plasma region; (<b>b</b>) mean body force in the plasma region between cycles 20 and 28.</p>
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<p><span class="html-italic">p</span> = 1 atm Temperature = 300 K conditions, (<b>a</b>) log scale mass fractions for different Φ at V<sub>ptp</sub> = 325 V <span class="html-italic">p</span> = 1 atm Temperature = 300 K, (<b>b</b>) mass fraction for different V<sub>ptp</sub> Φ = 1.</p>
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<p>Sketch of the mixing and flame area.</p>
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<p>Temperature contour v = 6 m/s Φ = 0.5 (<b>a</b>) kinetic effect, (<b>b</b>) momentum and energy effect.</p>
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<p>OH concentration contour v = 6 m/s Φ = 0.5 of (<b>a</b>) plasma kinetic effect and (<b>b</b>) momentum and energy plasma effect.</p>
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<p>NO concentration contour v = 6 m/s Φ = 0.5 of (<b>a</b>) kinetic effect and (<b>b</b>) momentum and energy effect.</p>
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<p>Maximum temperature in the symmetry plane for different plasma effects under the conditions of <span class="html-italic">v</span> = 6 m/s and <span class="html-italic">ϕ</span> = 0.5.</p>
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<p>Plasma-assisted combustion temperature symmetry plane for v = 6 m/s at different equivalence ratios of (<b>a</b>) Φ = 0.5, (<b>b</b>) Φ = 0.3, and (<b>c</b>) Φ = 0.1.</p>
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15 pages, 628 KiB  
Article
Effect of Dietary Energy Level during Late Gestation on Mineral Contents in Colostrum, Milk, and Plasma of Lactating Jennies
by Fang Hui, Manman Tong, Shuyi Li, Yanli Zhao, Xiaoyu Guo, Yongmei Guo, Binlin Shi and Sumei Yan
Animals 2024, 14(16), 2383; https://doi.org/10.3390/ani14162383 - 16 Aug 2024
Viewed by 690
Abstract
This study investigated the effects of dietary energy levels during late gestation on mineral content in the plasma, colostrum, and milk of jennies postpartum. Twenty-four pregnant multiparous DeZhou jennies, aged 6.0  ±  0.1 years, with a body weight of 292  ±  33 kg, an average [...] Read more.
This study investigated the effects of dietary energy levels during late gestation on mineral content in the plasma, colostrum, and milk of jennies postpartum. Twenty-four pregnant multiparous DeZhou jennies, aged 6.0  ±  0.1 years, with a body weight of 292  ±  33 kg, an average parity number of 2.7  ±  0.1, and similar expected dates of confinement (74  ±  4 days), were randomly allocated to three groups and fed three diets: high energy (12.54 MJ/kg, HE), medium energy (12.03 MJ/kg, ME), and low energy (11.39 MJ/kg, LE). Blood samples were collected from the jugular vein of each jenny at time points of 0 h, 24 h, 48 h, 5 d, 7 d, and 14 d after parturition. Additionally, milk samples were collected through manual milking, and an analysis of the mineral content was conducted. The results showed that compared with HE, both ME and LE significantly increased the levels of calcium (Ca), phosphorus (P), zinc (Zn), selenium (Se), molybdenum (Mo), and cobalt (Co) in the plasma and Ca, P, magnesium (Mg), copper (Cu), manganese (Mn), Zn, selenium (Se), molybdenum (Mo), and Co in the milk of jennies postpartum (p < 0.05); ME also increased the levels of potassium (K), iron (Fe), and Mn in plasma and K and Fe in milk (p < 0.05). The levels of Ca, K, Mg, P, Fe, Cu, Mn, Co, Se, Zn, and Mo in plasma and milk gradually decreased with increasing postpartum time. Their contents were the highest at 0 h postpartum, rapidly decreased after 24 h postpartum, and declined to the lowest on day 14 postpartum. The interaction between dietary energy level and postpartum time showed that although the concentrations of the minerals Ca, P, K, Mg, Fe, Cu, Mn, Zn, Co, Se, and Mo decreased in jennies’ plasma and milk in the treatment groups with different energy levels as postpartum time increased, the pattern of change was also influenced by dietary energy level. The influence of dietary energy level in late gestation on the mineral content of milk and plasma during the postpartum colostrum phase was higher than that during the milk phase. In conclusion, this study demonstrated that, under the current experimental conditions, the mineral content of the colostrum, milk, and plasma of jennies after parturition was dependent on the dietary energy level during late gestation. Full article
(This article belongs to the Section Equids)
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<p>Trends in the mineral content of the milk of jennies at different times after parturition. (<b>a</b>) Trends in Ca, P, K, and Mg in the milk of jennies; (<b>b</b>) trends in Zn, Fe, and Cu in the milk of jennies; (<b>c</b>) trends in Mn, Se, Mo, and Co in the milk of jennies.</p>
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12 pages, 4098 KiB  
Article
Two-Dimensional Plasma Soft X-ray Radiation Imaging System: Optimization of Amplification Stage Based on Gas Electron Multiplier Technology
by Karol Malinowski, Maryna Chernyshova, Sławomir Jabłoński, Tomasz Czarski, Andrzej Wojeński and Grzegorz Kasprowicz
Sensors 2024, 24(16), 5113; https://doi.org/10.3390/s24165113 - 7 Aug 2024
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Abstract
The objective of the proposed research is to develop plasma soft X-ray (SXR) radiation imaging that includes spectral information in addition to standard SXR tomography for the purpose of studying, for example, tungsten transport and its interplay with magnetohydrodynamics (MHD) in tokamak plasmas [...] Read more.
The objective of the proposed research is to develop plasma soft X-ray (SXR) radiation imaging that includes spectral information in addition to standard SXR tomography for the purpose of studying, for example, tungsten transport and its interplay with magnetohydrodynamics (MHD) in tokamak plasmas in an ITER-relevant approach. The SXR radiation provides valuable information about both aspects, particularly when measured with high spatial and temporal resolution and when tomographic reconstructions are performed. The spectral data will facilitate the tracking of both light and high-Z impurities. This approach is pertinent to both the advancement of a detailed understanding of physics and the real-time control of plasma, thereby preventing radiative collapses. The significance of this development lies in its ability to provide three-dimensional plasma tomography, a capability that extends beyond the scope of conventional tomography. The utilization of two-dimensional imaging capabilities inherent to Gas Electron Multiplier (GEM) detectors in a toroidal view, in conjunction with the conventional poloidal tomography, allows for the acquisition of three-dimensional information, which should facilitate the study of, for instance, the interplay between impurities and MHD activities. Furthermore, this provides a valuable opportunity to investigate the azimuthal asymmetry of tokamak plasmas, a topic that has rarely been researched. The insights gained from this research could prove invaluable in understanding other toroidal magnetically confined plasmas, such as stellarators, where comprehensive three-dimensional measurements are essential. To illustrate, by attempting to gain access to anisotropic radiation triggered by magnetic reconnection or massive gas injections, such diagnostics will provide the community with enhanced experimental tools to understand runaway electrons (energy distribution and spatial localization) and magnetic reconnection (spatial localization, speed…). This work forms part of the optimization studies of a detecting unit proposed for use in such a diagnostic system, based on GEM technology. The detector is currently under development with the objective of achieving the best spatial resolution feasible with this technology (down to approximately 100 µm). The diagnostic design focuses on the monitoring of photons within the 2–15 keV range. The findings of the optimization studies conducted on the amplification stage of the detector, particularly with regard to the geometrical configuration of the GEM foils, are presented herein. The impact of hole shape and spacing in the amplifying foils on the detector parameters, including the spatial size of the avalanches and the electron gain/multiplication, has been subjected to comprehensive numerical analysis through the utilization of Degrad (v. 3.13) and Garfield++ (v. bd8abc76) software. The results obtained led to the identification of two configurations as the most optimal geometrical configurations of the amplifying foil for the three-foil GEM system for the designed detector. The first configuration comprises cylindrical holes with a diameter of 70 μm, while the second configuration comprises biconical holes with diameters of 70/50/70 μm. Both configurations had a hole spacing of 120 μm. Full article
(This article belongs to the Special Issue Advances in Particle Detectors and Radiation Detectors)
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<p>View of the matrix with examples of pixel connections. The identical color of the circle indicates the connected pixels. This type of merging results in an approximately 11-fold reduction in actual signal channels. However, it does necessitate the simultaneous recording of the signal generated by a single photon absorption on two adjacent pixels. Furthermore, a more uniform loading of the signal channels is ensured when the detector area is unevenly irradiated by photons. The left-hand image depicts the entire matrix, while the right-hand image is a more detailed section. The colors of the numbers relate to the direction of merging, with the same numbers denoting merged pixels.</p>
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<p>Cell models of the considered GEM foil cases: hole spacing ranging from 100 μm (<b>top</b>) to 300 μm (<b>bottom</b>) in 10 μm increments. The hole geometries, from left to right, are as follows: cylindrical Ø50 μm, cylindrical Ø70 μm, cup-shaped Ø70/50 μm, inverted cup-shaped Ø50/70 μm, and biconical Ø70/50/70 μm. The foil thickness of Kapton is 50 μm, with a 5 μm copper layer coating both sides.</p>
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<p>An illustrative example of the results obtained from Degrad. The figure illustrates the spatial distribution of primary electrons (in the XY plane) that have been thermalized to 2 eV. This distribution is a consequence of the absorption of a 6 keV photon at the point (0, 0) in a 70/30 Ar/CO<sub>2</sub> gas mixture under an electric field of Ez = 3 kV/cm. The black circles represent the size of holes in the GEM foil with Ø70 μm and 140 μm pitch, included for scale reference.</p>
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<p>FWHM distributions of avalanches on the readout electrode for the ‘70d50c70t’ case with a hole spacing of 100, 200, and 300 µm. The FWHM values were determined by fitting single avalanche distributions with the Voigt distribution.</p>
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<p>Distribution of FWHM values of electron avalanches on the readout electrode for different GEM foil configurations, including variations in hole shape and spacing. The right side of the figure displays the same distribution with the standard deviation indicated.</p>
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<p>(<b>a</b>) The mean electron gain of the GEM detector calculated for all cases under study. (<b>b</b>) The resolution of the gain distributions, which is defined as the width of the distribution divided by its mean value.</p>
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<p>(<b>a</b>–<b>e</b>) The spatial distribution of electrons that terminate their trajectories on the walls of the hole for all the examined configurations. The charge deposited by 10<sup>4</sup> electron avalanches on the Kapton (red) is presented with the respective values being 30 Me, 17 Me, 9.4 Me, 40 Me, and 7.2 Me for a given configuration. The fraction relative to the total number of electrons in the hole is 0.27, 0.20, 0.12, 0.40, and 0.12, respectively. The center of the hole is located at the z = 0 point. The section ranging from −25 to 25 µm represents the Kapton component, whereas the sections ranging from −30 to −25 µm and 25 to 30 µm represent the copper component, which comprises the electrodes on the foil.</p>
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<p>The fraction of electron deposition on the GEM film for all cases under investigation. This parameter is defined as the ratio of the number of electrons deposited on the Kapton surface to the total number of electrons passing through the hole, including both primary and secondary electrons.</p>
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17 pages, 7653 KiB  
Article
Surface Analysis of Stainless Steel Electrodes Cleaned by Atmospheric Pressure Plasma
by Jia Zhang, Mengjia Dang, Cheng Luo, Yongshan Ba and Qingkai Li
Materials 2024, 17(14), 3621; https://doi.org/10.3390/ma17143621 - 22 Jul 2024
Viewed by 1073
Abstract
The Z-pinch device is a critical component in inertial confinement fusion, where stainless steel electrodes must withstand high current densities of up to MA/cm2. Gases and difficult-to-remove impurities adhering to the electrode surfaces can ionize, significantly impacting the device’s electrical conductivity [...] Read more.
The Z-pinch device is a critical component in inertial confinement fusion, where stainless steel electrodes must withstand high current densities of up to MA/cm2. Gases and difficult-to-remove impurities adhering to the electrode surfaces can ionize, significantly impacting the device’s electrical conductivity efficiency. In this paper, the surface of stainless steel electrodes was subjected to cleaning using a large-area plasma jet under atmospheric pressure. The wettability, chemical composition, and chemical state of the electrode surface were characterized using a water contact angle measuring instrument and X-ray photoelectron spectroscopy (XPS). The cleaning effect under different discharge parameters was systematically analyzed. The results revealed a significant reduction in the content of carbon pollutants on the surface of stainless steel electrodes, decreasing from 62.95% to a minimum of 37.68% after plasma cleaning. Moreover, the water contact angle decreased from 70.76° to a minimum of 29.31°, and the content of water molecules adsorbed on the surface decreased from 17.31% to a minimum of 5.9%. Based on the evolution process of micro-element content and chemical state on the surface of stainless steel electrode, the cleaning process of adhering substances on the surface by atmospheric pressure plasma was analyzed by the layered cleaning model for surface pollutants on stainless steel. Full article
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<p>Atmospheric pressure plasma jet generation device and plasma jet.</p>
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<p>Three measuring points (1–3) on the surface of the stainless steel electrode.</p>
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<p>Diagram of water contact angle.</p>
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<p>The proportion of chemical composition at three measuring points on the surface of stainless steel (<b>a</b>) power: 550 W; (<b>b</b>) power: 750 W.</p>
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<p>Water contact angles at different positions on the surface of stainless steel.</p>
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<p>XPS Survey spectra of the untreated stainless steel electrode surface.</p>
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<p>XPS Survey spectra of the stainless steel electrode surface under different cleaning times at a power of 550 W: (<b>a</b>) 0.5 min; (<b>b</b>) 1.0 min; (<b>c</b>) 1.5 min; (<b>d</b>) 2.0 min; (<b>e</b>) 2.5 min; (<b>f</b>) 3.0 min.</p>
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<p>XPS Survey spectra of the stainless steel electrode surface under different cleaning times at a power of 750 W: (<b>a</b>) 0.5 min; (<b>b</b>) 1.0 min; (<b>c</b>) 1.5 min; (<b>d</b>) 2.0 min; (<b>e</b>) 2.5 min; (<b>f</b>) 3.0 min.</p>
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<p>The change trend of the proportion of chemical composition on the surface of stainless steel under different cleaning parameters: (<b>a</b>) power: 550 W; (<b>b</b>) power: 750 W.</p>
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<p>High-resolution XPS spectra fitting of untreated electrode surface signals: (<b>a</b>) C (1s); (<b>b</b>) O (1s).</p>
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<p>The change trend of the chemical state of C element under different cleaning parameters: (<b>a</b>) power: 550 W; (<b>b</b>) power: 750 W.</p>
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<p>The change trend of the chemical state of O element under different cleaning parameters: (<b>a</b>) power: 550 W; (<b>b</b>) power: 750 W.</p>
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<p>Simplified model of surface pollutants on stainless steel electrodes exposed to atmospheric environment.</p>
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<p>Plasma cleaning model of the stainless steel electrode surface at different cleaning times at a power of 550 W.</p>
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<p>Plasma cleaning model of the stainless steel electrode surface at different cleaning times at a power of 750 W.</p>
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<p>Water contact angle of the stainless steel electrode surface before and after plasma cleaning: (<b>a</b>) untreated; (<b>b</b>–<b>g</b>) power: 550 W; (<b>h</b>–<b>m</b>) power: 750 W.</p>
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<p>The change trend of water contact angle on the surface of stainless steel electrode before and after plasma cleaning (averages ± standard deviations): (<b>a</b>) power: 550 W; (<b>b</b>) power: 750 W.</p>
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<p>Time efficiency characteristics of plasma cleaning efficiency under different cleaning times at a power of 550 W (averages ± standard deviations): (<b>a</b>) 0.5 min; (<b>b</b>) 1.0 min; (<b>c</b>) 1.5 min; (<b>d</b>) 2.0 min; (<b>e</b>) 2.5 min; (<b>f</b>) 3.0 min.</p>
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<p>Time efficiency characteristics of plasma cleaning efficiency under different cleaning times at a power of 750 W (averages ± standard deviations): (<b>a</b>) 0.5 min; (<b>b</b>) 1.0 min; (<b>c</b>) 1.5 min; (<b>d</b>) 2.0 min; (<b>e</b>) 2.5 min; (<b>f</b>) 3.0 min.</p>
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<p>XPS detection results of the stainless steel electrode surface exposed to different times in the atmospheric environment.</p>
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17 pages, 4486 KiB  
Article
Production of High-Power Nitrogen Sputtering Plasma for TiN Film Preparation
by Taishin Sato, Sawato Igarashi, Katsuyuki Takahashi, Seiji Mukaigawa and Koichi Takaki
Processes 2024, 12(7), 1314; https://doi.org/10.3390/pr12071314 - 25 Jun 2024
Viewed by 1130
Abstract
High-density nitrogen plasma was produced using a high-power pulsed power modulator to sputter titanium targets for the preparation of titanium nitride film. The high-power pulsed sputtering discharge unit consisted of two targets facing each other with the same electrical potential. The titanium target [...] Read more.
High-density nitrogen plasma was produced using a high-power pulsed power modulator to sputter titanium targets for the preparation of titanium nitride film. The high-power pulsed sputtering discharge unit consisted of two targets facing each other with the same electrical potential. The titanium target plates were used as target materials with dimensions of 60 mm length, 20 mm height, and 5 mm thickness. The gap length was set to be 10 mm. The magnetic field was created with a permanent magnet array behind the targets. The magnetic field strength at the gap between the target plates was 70 mT. The electrons were trapped by the magnetic and electric fields to enhance the ionization in the gap. The nitrogen and argon gases were injected into the chamber with 4 Pa gas pressure. The applied voltage to the target plates had an amplitude from −600 V to −1000 V with 600 μs in pulse width. The target current was approximately 10 A with the consumed power of 13 kW. The discharge sustaining voltage was almost constant and independent of the applied voltage, in the same manner as the conventional normal glow discharge. The ion density and electron temperature at the surface of the ionization region were obtained as 1.7 × 1019 m−3 and 3.4 eV, respectively, by the double probe measurements. The vertical distribution of ion density and electron temperature ranged from 1.1 × 1017 m−3 (at 6 cm from the target edge) to 1.7 × 1019 m−3 and from 2.4 eV (at 6 cm from the target edge) to 3.4 eV, respectively. From the emission spectra, the intensities of titanium atoms (Ti I), titanium ions (Ti II), and nitrogen ions (N2+) increased with increasing input power. However, the intensities ratio of Ti II to Ti I was not affected by the intensities from N2+. Full article
(This article belongs to the Special Issue Plasma Science and Plasma-Assisted Applications)
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<p>Schematic of the HPPS discharge unit.</p>
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<p>Schematic of the experimental setup.</p>
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<p>Measurement for spatial distribution of plasma as a function of (<b>a</b>) vertical distance <span class="html-italic">L</span> and (<b>b</b>) horizontal distance <span class="html-italic">R</span>.</p>
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<p>Typical signals of the double-probe measurement; (<b>a</b>) probe current waveforms at <span class="html-italic">L</span> = 0 for various bias voltages, and (<b>b</b>) its V–I characteristics.</p>
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<p>Schematic of the arrangement for optical measurement of the HPPS plasma.</p>
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<p>Waveforms of the target voltage and the current for cases of (<b>a</b>) nitrogen and (<b>b</b>) argon gases at 4 Pa of gas pressure and −1000 V of the applied voltage. The temporal plasma consumed powers of the nitrogen and argon are shown in (<b>c</b>).</p>
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<p>V–I characteristics obtained from the voltage and current waveforms in <a href="#processes-12-01314-f006" class="html-fig">Figure 6</a>. (<b>a</b>) the transient glow region; from <span class="html-italic">I</span><sub>T</sub> = 0 (upper) to point of <span class="html-italic">t</span><sub>p</sub>, steady state glow region; between points <span class="html-italic">t</span><sub>p</sub> and <span class="html-italic">t</span><sub>t</sub>, and afterglow region; from point <span class="html-italic">t</span><sub>t</sub> to <span class="html-italic">I</span><sub>T</sub> = 0 (lower). (<b>b</b>) steady-state glow voltage for two gases as a function of glow current. The glow current was controlled by the applied voltage in range from −600 V to −1000 V at 4 Pa gas pressure.</p>
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<p>Electrical characteristics of the discharge for nitrogen and argon gas at 4 Pa of gas pressure for various applied voltages; (<b>a</b>) the target voltage; (<b>b</b>) the target current; and (<b>c</b>) the consumed power.</p>
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<p>Consumed power for nitrogen and argon gases as function of target current at various applied voltages. The solid lines are estimated using circuit equations for various applied voltages. The triangle and circle plots are measured values for nitrogen and argon gases, respectively.</p>
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<p>The electron temperature and ion density as a function of vertical distance <span class="html-italic">L</span> from the electrode surface at gas pressure of 4 Pa and −1000 V of applied voltage. The triangle and circle plots are measured values for nitrogen and argon gases, respectively.</p>
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<p>The ion density as a function of of horizontal distance <span class="html-italic">R</span> from the electrode at axial distance of <span class="html-italic">L</span> = 30 mm, gas pressure of 4 Pa, and applied voltage of −1000 V. The triangle and circle plots are measured values for nitrogen and argon gases, respectively.</p>
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<p>Spectrum of the light emission of the nitrogen plasma at 4 Pa of gas pressure and the −1000 V of the applied voltage.</p>
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<p>Intensities of the optical emission spectrum at 391.44, 498.17, and 334.94 nm corresponding excitation states of N<sub>2</sub><sup>+</sup>, Ti I, and Ti II, respectively, as a function of power consumed in the plasma at 4 Pa of gas pressure and the −1000 V of the applied voltage.</p>
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<p>Intensities of the optical emission spectrum at Ti I (498.17 nm) and Ti II (334.94 nm) as a function of intensity of N<sub>2</sub><sup>+</sup> 391.44 nm, at 4 Pa gas pressure and the −1000 V applied voltage. The intensity ratio between Ti II and Ti I is also plotted as a function of N<sub>2</sub><sup>+</sup> intensity.</p>
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<p>Intensities of the optical emission spectrum at Ti I (498.17 nm) and Ti II (334.94 nm) as a function of intensity of Ar II (368.25 nm) at 4 Pa gas pressure and the −1000 V applied voltage. The intensity ratio between Ti II and Ti I is also plotted as a function of Ar II intensity.</p>
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