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Search Results (3,417)

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20 pages, 5599 KiB  
Article
Modification and Aging Mechanism of Crumb Rubber Modified Asphalt Based on Molecular Dynamics Simulation
by Jian Li and Liang He
Materials 2025, 18(1), 197; https://doi.org/10.3390/ma18010197 (registering DOI) - 5 Jan 2025
Abstract
Asphalt modified with treated waste tires has good environmental protection and application value. However, the nano-modification mechanism of crumb rubber (CR) with asphalt is still unclear. This research investigates the mechanism, aging, and interfacial interaction with the aggregate of CR modification asphalt (CRMA). [...] Read more.
Asphalt modified with treated waste tires has good environmental protection and application value. However, the nano-modification mechanism of crumb rubber (CR) with asphalt is still unclear. This research investigates the mechanism, aging, and interfacial interaction with the aggregate of CR modification asphalt (CRMA). The base asphalt and CRMA (original and aged) and two typical aggregate models were constructed. The accuracy of the model was verified through multiple indicators. The effects of CR and aging on the physical properties (density, compatibility, and diffusion coefficient), mechanical properties, component interaction behavior, and interfacial interactions with aggregates of CRMA were systematically analyzed. The results showed that the CR reduced the diffusion coefficient of asphalt by about 31%. The CR inhibited the movement of the components of asphalt (especially saturate and aromatic), which significantly improved the mechanical properties of asphalt. The compatibility between asphalt and CR significantly deteriorated after aging. The difference in the solubility parameter was about four times that before aging. It is instructive for the regeneration of CRMA. Aging led to a decrease in the shear modulus and Young’s modulus of both base asphalt and CRMA, which verified and quantified the adverse effects of aging on the mechanical properties. Comparing the two aggregates, CaCO3 had a greater adhesion with asphalt than SiO2. The difference ranged from 22.5% to 39.9%, which quantified the difference in the adhesion properties of acid base aggregates with asphalt. This study can provide theoretical guidance for the modification and application of CRMA. Full article
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Figure 1
<p>Molecular structure of the original and aged base asphalt model.</p>
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<p>Molecular structures of NR and SBR synthetic monomers.</p>
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<p>Rubber single-chain model.</p>
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<p>Original and aged CRMA models.</p>
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<p>Asphalt–aggregate interface model.</p>
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<p>Variation in density of original base asphalt.</p>
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<p>Variation curve of base asphalt specific volume with temperature.</p>
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<p>Variation in density of different types of asphalt.</p>
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<p>MSD curves of asphalt fractions under conditions of different asphalt types.</p>
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<p>Diffusion coefficients of asphalt fractions.</p>
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<p>MSD curves of asphalt fractions moving on different aggregate surfaces.</p>
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<p>MSD curves of asphalt fractions moving on different aggregate surfaces.</p>
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<p>Diffusion coefficients of asphalt fractions moving on different aggregate surfaces.</p>
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<p>Variation in the modulus of different types of asphalt.</p>
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<p>Energy variation for different asphalts.</p>
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<p>Interaction energy of asphalt fractions with CR before and after aging.</p>
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<p>Adhesion work of asphalt–aggregate.</p>
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22 pages, 5592 KiB  
Article
Experiment and Simulation Study on the Crashworthiness of Markforged 3D-Printed Carbon/Kevlar Hybrid Continuous Fiber Composite Honeycomb Structures
by Jinlong Ju, Nana Yang, Lei Yu, Zhe Zhang, Hongyong Jiang, Wenhua Wu and Guolu Ma
Materials 2025, 18(1), 192; https://doi.org/10.3390/ma18010192 (registering DOI) - 5 Jan 2025
Abstract
Fiber hybridization can effectively solve the localized brittle fracture problem of composite honeycomb, but the interaction between different fibers leads to a very complex failure mechanism. Hence, 3D-printed hybrid continuous fiber composite honeycombs with a combination of carbon and Kevlar fibers are designed [...] Read more.
Fiber hybridization can effectively solve the localized brittle fracture problem of composite honeycomb, but the interaction between different fibers leads to a very complex failure mechanism. Hence, 3D-printed hybrid continuous fiber composite honeycombs with a combination of carbon and Kevlar fibers are designed to study the structural failure behaviors by the experiment and simulation method. The experimental samples, including Onyx, carbon, Kevlar, carbon/Kevlar, and Kevlar/carbon composites, are fabricated based on Markforged 3D printing technology, and the crushing tests are conducted to evaluate the failure behaviors. An equivalence finite element modeling method to replace the heterogeneous microstructure of hybrid composites is proposed to analyze the failure behaviors. Results indicate that carbon/Kevlar honeycomb exhibits the highest energy absorption and cost effectiveness, while CFRP honeycomb demonstrates the highest load-carrying capacity. It is found that carbon/Kevlar and Kevlar/carbon honeycombs have significant hybrid effects compared to single-fiber honeycombs, which also reveals the hybrid mechanisms between carbon and Kevlar fibers. Furthermore, the Onyx honeycomb, lacking long fibers, exhibits brittle collapse, whereas other honeycombs show ductile collapse due to the presence of Kevlar fibers. Combining the simulation studies, the damage evolution mechanisms of honeycombs, including fiber/matrix tension and compression, shear damage, interface damage, etc., are further revealed. This work provides valuable insights into the design and failure analysis of 3D-printed hybrid fiber composite honeycombs. Full article
(This article belongs to the Special Issue 3D-Printed Composite Structures: Design, Properties and Application)
17 pages, 4693 KiB  
Article
Rheological Characterization and Printability of Sodium Alginate–Gelatin Hydrogel for 3D Cultures and Bioprinting
by Mohan Kumar Dey and Ram V. Devireddy
Biomimetics 2025, 10(1), 28; https://doi.org/10.3390/biomimetics10010028 (registering DOI) - 4 Jan 2025
Viewed by 288
Abstract
The development of biocompatible hydrogels for 3D bioprinting is essential for creating functional tissue models and advancing preclinical drug testing. This study investigates the formulation, printability, mechanical properties, and biocompatibility of a novel Alg-Gel hydrogel blend (alginate and gelatin) for use in extrusion-based [...] Read more.
The development of biocompatible hydrogels for 3D bioprinting is essential for creating functional tissue models and advancing preclinical drug testing. This study investigates the formulation, printability, mechanical properties, and biocompatibility of a novel Alg-Gel hydrogel blend (alginate and gelatin) for use in extrusion-based 3D bioprinting. A range of hydrogel compositions were evaluated for their rheological behavior, including shear-thinning properties, storage modulus, and compressive modulus, which are crucial for maintaining structural integrity during printing and supporting cell viability. The printability assessment of the 7% alginate–8% gelatin hydrogel demonstrated that the 27T tapered needle achieved the highest normalized Printability Index (POInormalized = 1), offering the narrowest strand width (0.56 ± 0.02 mm) and the highest printing accuracy (97.2%) at the lowest printing pressure (30 psi). In contrast, the 30R needle, with the smallest inner diameter (0.152 mm) and highest printing pressure (80 psi), resulted in the widest strand width (0.70 ± 0.01 mm) and the lowest accuracy (88.8%), resulting in a POInormalized of 0.274. The 30T and 27R needles demonstrated moderate performance, with POInormalized values of 0.758 and 0.558, respectively. The optimized 7% alginate and 8% gelatin blend demonstrated favorable printability, mechanical strength, and cell compatibility with MDA-MB-213 breast cancer cells, exhibiting high cell proliferation rates and minimal cytotoxicity over a 2-week culture period. This formulation offers a balanced approach, providing sufficient viscosity for precision printing while minimizing shear stress to preserve cell health. This work lays the groundwork for future advancements in bioprinted cancer models, contributing to the development of more effective tools for drug screening and personalized medicine. Full article
(This article belongs to the Section Biomimetic Design, Constructions and Devices)
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<p>(<b>a</b>) Scaffold grid design with extruded square along the path line and (<b>b</b>) layered scaffold configuration with 90° rotation and z-axis duplication.</p>
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<p>(<b>a</b>) Laser-cut square-shaped molds and (<b>b</b>) casting process for Alg-Gel hydrogel samples.</p>
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<p>MDA-MB-231 cells seeded on 3D-bioprinted scaffolds in a 12-well plate.</p>
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<p>Rheological characterization of hydrogel mixtures with varying alginate and gelatin concentrations (4% Alg–8% Gel, 5% Alg–6% Gel, 5% Alg–6% Gel, 7% Alg–8% Gel). (<b>a</b>) Storage modulus (G′) and loss modulus (G″) as a function of angular frequency, showing an increase in both moduli with higher alginate concentration; (<b>b</b>) tan δ vs. angular frequency for the hydrogel mixtures, with tan δ values consistently below 1 across all formulations; (<b>c</b>) shear viscosity as a function of shear rate, demonstrating shear-thinning behavior in all hydrogel mixtures. (<b>d</b>) Axial stress vs. compression percentage, highlighting distinct mechanical behaviors across formulations, with 4% Alg–8% Gel showing the highest compressive strength.</p>
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<p>Swelling ratio of Alg-Gel hydrogels over time.</p>
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<p>UATR spectra of (<b>a</b>) alginate, (<b>b</b>) alginate–gelatin, and (<b>c</b>) alginate–gelatin–calcium chloride.</p>
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<p>Three-dimensional bioprinting scaffold on Petri dish.</p>
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<p>Evaluation of cell proliferation and viability of MDA-MB-213 cells cultured on Alg-Gel hydrogels over two weeks: (<b>a</b>) cell viability at 1 day; (<b>b</b>) cell viability at 1 week; (<b>c</b>) cell viability at 2 weeks, confirming hydrogel cytocompatibility and support for long-term culture; (<b>d</b>) cell viability.</p>
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14 pages, 1222 KiB  
Article
Experimental Investigation on Unloading-Induced Sliding Behavior of Dry Sands Subjected to Constant Shear Force
by Wengang Dang, Kang Tao, Jinyang Fu and Bangbiao Wu
Appl. Sci. 2025, 15(1), 401; https://doi.org/10.3390/app15010401 - 3 Jan 2025
Viewed by 341
Abstract
Infilled joints or faults are often subjected to long-term stable shear forces, and nature surface processes of normal unloading can change the frictional balance. Therefore, it is essential to study the sliding behavior of such granular materials under such unloading conditions, since they [...] Read more.
Infilled joints or faults are often subjected to long-term stable shear forces, and nature surface processes of normal unloading can change the frictional balance. Therefore, it is essential to study the sliding behavior of such granular materials under such unloading conditions, since they are usually the filling matter. We conducted two groups of normal unloading direct shear tests considering two variables: unloading rate and the magnitude of constant shear force. Dry sands may slide discontinuously during normal unloading, and the slip velocity does not increase uniformly with unloading time. Due to horizontal particle interlacing and normal relaxation, there will be sliding velocity fluctuations and even temporary intermissions. At the stage of sliding acceleration, the normal force decreases with a higher unloading rate and increases with a larger shear force at the same sliding velocity. The normal forces obtained from the tests are less than those calculated by Coulomb’s theory in the conventional constant-rate shear test. Under the same unloading rate, the range of apparent friction coefficient variation is narrower under larger shear forces. This study has revealed the movement patterns of natural granular layers and is of enlightening significance in the prevention of corresponding geohazards. Full article
(This article belongs to the Topic Geotechnics for Hazard Mitigation)
19 pages, 12209 KiB  
Article
The Effect of Geometrical Shape of Surface Texture on the Rheology and Tribology of Confined Lubricants
by Fankai Peng and Ahmad Jabbarzadeh
Lubricants 2025, 13(1), 13; https://doi.org/10.3390/lubricants13010013 - 3 Jan 2025
Viewed by 261
Abstract
Understanding lubrication at the nanoscale is essential for reducing friction. While alkanes, the primary component in most lubricants, have been studied for their molecular structure’s impact on rheology and behavior when confined by solid surfaces, the influence of confining surface texture remains underexplored. [...] Read more.
Understanding lubrication at the nanoscale is essential for reducing friction. While alkanes, the primary component in most lubricants, have been studied for their molecular structure’s impact on rheology and behavior when confined by solid surfaces, the influence of confining surface texture remains underexplored. This research uses molecular dynamics simulations to investigate the rheological behavior of thin film lubrication between various patterned rough surfaces. The study focuses on sinusoidal, sawtooth, and squaretooth wave-patterned surfaces, using hexadecane as the lubricant. The simulations examine the effects under different normal loads and shear rates. Surface patterns significantly influence the formation and structure of crystalline bridges, depending on shear rates and normal loads. The sawtooth wave-patterned surface exhibits the highest viscosity under low normal load and shear rate conditions, forming crystalline bridges with a molecular orientation perpendicular to the shear direction. The squaretooth patterns exhibit the lowest viscosities due to the nematic order in crystalline bridges with molecules aligned in the shearing direction. The sinusoidal wave-patterned surface shows intermediary viscosity with disordered crystalline bridge groups formed with random molecular orientation. The lowest viscosity provided by the squaretooth pattern surface persists across various conditions, including both transitory and steady states, under high and low loads, and over a wide range of shear rates. However, the difference in shear viscosity is reduced at higher normal loads. This research provides valuable insights for designing nanoelectromechanical systems (NEMS) and other applications where boundary conditions are critical to lubrication. Full article
(This article belongs to the Special Issue Advances in Molecular Rheology and Tribology)
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<p>(<b>a</b>) Sinusoidal, (<b>b</b>) sawtooth, and (<b>c</b>) squaretooth patterned wall.</p>
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<p>Three stages of simulation include loading, equilibrium, and sliding stages. The snapshot of the bulk system is taken from the bulk state obtained from “sllod” [<a href="#B51-lubricants-13-00013" class="html-bibr">51</a>] flow simulation. The snapshots of the loading stage are taken from the first stage of Couette flow simulations. The snapshots of the equilibrium stage are taken from the state under 10 MPa normal load without applying shear rates after equilibrating for 9.44 ns. The snapshots of the shear situations stage are taken from the state at <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>8</mn> </mrow> </msup> <msup> <mrow> <mtext> </mtext> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> shear rate under 10 MPa normal load after 18.8 ns. The film thickness is measured from the midpoint position between the peak and valleys of each surface, as shown by dashed lines.</p>
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<p>Snapshots of the equilibrated lubricant molecular structure for sinusoidal, sawtooth and squaretooth patterned surfaces under the 10 (<b>top row</b>) and 300 MPa (<b>bottom row</b>) load with the corresponding <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>g</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>g</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> values shown for each case. The molecules are shown in different colours to improve the clarity.</p>
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<p>Temperature (<b>top row</b>), pressure (<b>second row</b>), and density (<b>third row</b>) contours for sinusoidal (<b>left column</b>), sawtooth (<b>middle column</b>), and squaretooth (<b>right column</b>) wave patterned walls at a shear rate of <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>8</mn> </mrow> </msup> <msup> <mrow> <mi>s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> and 10 MPa normal load. The corresponding snapshots of molecular configuration for each system are shown in the bottom row.</p>
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<p>The density (<b>top panels</b>) and normalised velocity (<b>bottom panels</b>) profiles for films under 10 MPa normal load undergoing shear at 10<sup>8</sup> s<sup>−1</sup> (<b>left column</b>) and 10<sup>10</sup> s<sup>−1</sup> (<b>right column</b>). The results are shown for the three different surfaces.</p>
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<p>Snapshots for sinusoidal, sawtooth and squaretooth patterned wall cases at <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>8</mn> </mrow> </msup> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> (<b>top row</b>) and <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>10</mn> </mrow> </msup> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> (<b>bottom row</b>) shear rates at a 10 MPa normal loading.</p>
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<p>Snapshots for sinusoidal, sawtooth and squaretooth patterned wall (<b>left to right</b>) cases at <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>8</mn> </mrow> </msup> <mo> </mo> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> shear rate and 300 MPa normal load.</p>
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<p>Snapshots for different patterned wall cases at <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>8</mn> </mrow> </msup> <msup> <mrow> <mi>s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>10</mn> </mrow> </msup> <msup> <mrow> <mi>s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> shear rate and 10 MPa normal load.</p>
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<p>Film thickness (h), density (ρ), <span class="html-italic">g</span><sub>2</sub>, <span class="html-italic">g</span><sub>4</sub>, and shear stress σ<sub>xz</sub> as a function of time for hexadecane film confined at P = 10 MPa and at a shear rate of 10<sup>8</sup> s<sup>−1</sup> for three different wall patterns of sinusoidal, sawtooth and squaretooth.</p>
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<p>The shear viscosity (<b>top panels</b>) and film thickness (<b>bottom panels</b>) for different patterned wall cases under 10 MPa (<b>left panels</b>), and 300 MPa (<b>right panels</b>). The results are shown for sinusoidal, sawtooth, and squaretooth surfaces.</p>
Full article ">Figure 10 Cont.
<p>The shear viscosity (<b>top panels</b>) and film thickness (<b>bottom panels</b>) for different patterned wall cases under 10 MPa (<b>left panels</b>), and 300 MPa (<b>right panels</b>). The results are shown for sinusoidal, sawtooth, and squaretooth surfaces.</p>
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<p>Time evolution of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>g</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>(stack) (<b>top row</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>g</mi> </mrow> <mrow> <mn>4</mn> </mrow> </msub> </mrow> </semantics></math>(stack) (<b>middle row</b>), and order parameter (S) (<b>bottom row</b>) for hexadecane sheared at different shear rates. The data are presented for sinusoidal (<b>left two columns</b>), sawtooth (<b>middle two columns</b>), and squaretooth (<b>right two columns</b>) patterned surfaces under normal loads of 10 MPa and 300 MPa.</p>
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<p>The first normal stress difference for all cases under (<b>a</b>) 10 MPa, (<b>b</b>) 300 MPa normal load.</p>
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22 pages, 5922 KiB  
Article
Predictive Modeling and Experimental Analysis of Cyclic Shear Behavior in Sand–Fly Ash Mixtures
by Özgür Yıldız and Ali Fırat Çabalar
Appl. Sci. 2025, 15(1), 353; https://doi.org/10.3390/app15010353 - 2 Jan 2025
Viewed by 250
Abstract
This study presents a comprehensive investigation into the cyclic shear behavior of sand–fly ash mixtures through experimental and data-driven modeling approaches. Cyclic direct shear tests were conducted on mixtures containing fly ash at 0%, 2.5%, 5%, 10%, 15%, and 20% by weight to [...] Read more.
This study presents a comprehensive investigation into the cyclic shear behavior of sand–fly ash mixtures through experimental and data-driven modeling approaches. Cyclic direct shear tests were conducted on mixtures containing fly ash at 0%, 2.5%, 5%, 10%, 15%, and 20% by weight to examine the influence of fly ash content on the shear behavior under cyclic loading conditions. The tests were carried out under a constant stress of 100 kPa to simulate field-relevant stress conditions. Results revealed that the fly ash content initially reduces shear strength at lower additive contents, but shear strength increases and reaches a maximum at 20% fly ash content. The findings highlight the trade-offs in mechanical behavior associated with varying fly ash proportions. To enhance the understanding of cyclic shear behavior, a Nonlinear Autoregressive Model with External Input (NARX) model was employed. Using data from the loading cycles as input, the NARX model was trained to predict the final shear response under cyclic conditions. The model demonstrated exceptional predictive performance, achieving a coefficient of determination (R2) of 0.99, showcasing its robustness in forecasting the cyclic shear performance based on the composition of the mixtures. The insights derived from this research underscore the potential of incorporating fly ash in sand mixtures for soil stabilization in geotechnical engineering. Furthermore, the integration of advanced machine learning techniques such as NARX models offers a powerful tool for predicting the behavior of soil mixtures, facilitating more effective and data-driven decision-making in geotechnical applications. Evidently, this study not only advances the understanding of cyclic shear behavior in fly ash–sand mixtures but also provides a framework for employing data-driven methodologies to address complex geotechnical challenges. Full article
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Figure 1
<p>Grain size distribution of Trakya sand (TS).</p>
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<p>SEM pictures of Trakya sand grains.</p>
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<p>Materials and equipment used: (<b>a</b>) fly ash, (<b>b</b>) Trakya sand, (<b>c</b>) cyclic direct shear testing equipment, (<b>d</b>) preparing specimens inside the shear box, (<b>e</b>) specimens after test.</p>
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<p>The variation in the volumetric strain at each cycle of loading.</p>
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<p>The variation in the volumetric strain of the tested specimens of; (<b>a</b>) Clean sand and mixtures of (<b>b</b>) 2.5 (%) FA, (<b>c</b>) 5 (%) FA, (<b>d</b>) 10 (%) FA, (<b>e</b>) 15 (%) FA and (<b>f</b>) 20 (%) FA.</p>
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<p>First-quarter cyclic shear behavior of tested specimens.</p>
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<p>Variations in shear stress with horizontal strain for; (<b>a</b>) Clean sand and mixtures of (<b>b</b>) 2.5 (%) FA, (<b>c</b>) 5 (%) FA, (<b>d</b>) 10 (%) FA, (<b>e</b>) 15 (%) FA and (<b>f</b>) 20 (%) FA.</p>
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<p>Variations in shear stress with horizontal strain for; (<b>a</b>) Clean sand and mixtures of (<b>b</b>) 2.5 (%) FA, (<b>c</b>) 5 (%) FA, (<b>d</b>) 10 (%) FA, (<b>e</b>) 15 (%) FA and (<b>f</b>) 20 (%) FA.</p>
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<p>Schematical demonstration of discretization of hysteresis loop.</p>
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<p>The flowchart of the analysis.</p>
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<p>The architecture of the NARX model.</p>
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<p>Best training performance of NARX model.</p>
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<p>Training state of NARX model.</p>
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<p>Response plot of NARX model.</p>
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<p>Predicted and measured stress–strain responses for specimens during fifth cycle; (<b>a</b>) Clean sand and mixtures of (<b>b</b>) 2.5 (%) FA, (<b>c</b>) 5 (%) FA, (<b>d</b>) 10 (%) FA, (<b>e</b>) 15 (%) FA and (<b>f</b>) 20 (%) FA.</p>
Full article ">Figure 14 Cont.
<p>Predicted and measured stress–strain responses for specimens during fifth cycle; (<b>a</b>) Clean sand and mixtures of (<b>b</b>) 2.5 (%) FA, (<b>c</b>) 5 (%) FA, (<b>d</b>) 10 (%) FA, (<b>e</b>) 15 (%) FA and (<b>f</b>) 20 (%) FA.</p>
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18 pages, 3083 KiB  
Article
Crystallized Pickering Emulsions from Plant Oil as a Local Alternative to Palm Oil
by Cyrill Husmann, Tamara Schmid, Chiara Waser, Ivo Kaelin, Lukas Hollenstein and Nadina Müller
Foods 2025, 14(1), 104; https://doi.org/10.3390/foods14010104 - 2 Jan 2025
Viewed by 291
Abstract
Palm and palm kernel oils are preferred ingredients in industrial food processing for baked goods and chocolate-based desserts due to their unique properties, such as their distinctive melting behaviors. However, ongoing concerns about the social and environmental sustainability of palm oil production, coupled [...] Read more.
Palm and palm kernel oils are preferred ingredients in industrial food processing for baked goods and chocolate-based desserts due to their unique properties, such as their distinctive melting behaviors. However, ongoing concerns about the social and environmental sustainability of palm oil production, coupled with consumer demands for palm oil-free products, have prompted the industry to seek alternatives which avoid the use of other tropical or hydrogenated fats. This project investigated replacing palm oils with chemically unhardened Swiss sunflower or rapeseed oils. Target applications were cookies and chocolate fillings. These oils were physically modified through emulsification, stabilized with finely ground oil press cake particles and crystallized waxes. Findings indicated that the emulsification of the oils increased viscosity and that the addition of wax was beneficial for long-term stability; however, the extent of this effect depended on the combination of oil and wax types. Furthermore, wax pre-crystallization and low shear during crystallization significantly improved emulsion stability. Despite these improvements, the resulting emulsions did not achieve sufficient stability and exhibited lower viscosity than palm oil. Future experiments should explore higher wax concentrations (1% or more) and develop analytical methods to better understand the wax composition and its role in oleogel formation. Full article
(This article belongs to the Section Food Physics and (Bio)Chemistry)
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<p>Summary of the different emulsification and crystallization process steps, where CO stands for ‘Crystallized Oil’, COP for ‘Crystallized Oil with Press Cake’, PECO for ‘Particle-Stabilized Emulsified Crystallized Oil’, CEPO for ‘Crystallized Emulsified Particle-Stabilized Oil’, and COPE for ‘Crystallized Oil with Particle Emulsification’.</p>
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<p>Emulsion stability indicated by the emulsion index after 504 h of storage at 18 °C for different types of emulsification processes (COPE: crystallized oil–wax–press cake suspension emulsified with water), CEPO: crystallized wax–oil suspension emulsified with water plus press cake) and PECO: particle stabilized W/O emulsions, additionally crystallized) crystallized at 147 s<sup>−1</sup> and 880 s<sup>−1</sup> in the surface scraped freezer, as well as differences between rapeseed oil and sunflower oil, <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>9</mn> </mrow> </semantics></math>. Letters (a, ab, b) indicate groups of significance, i.e., group means not sharing any letter are significantly different by the Wilcoxon test with a <span class="html-italic">p</span>-value of 0.05.</p>
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<p>Emulsion index as a function of time for crystallization and subsequent mixing with press cake and emulsified (CEPO) rapeseed oil in a scraped surface freezer at a shear rate of 147 s<sup>−1</sup>. The datapoints are compared to an estimated logistic model. Long-term behavior <math display="inline"><semantics> <mrow> <mi>L</mi> </mrow> </semantics></math>, decay time <math display="inline"><semantics> <mrow> <mi>T</mi> </mrow> </semantics></math> until the emulsion index <math display="inline"><semantics> <mrow> <mi>E</mi> <mi>I</mi> </mrow> </semantics></math> equals 90%, their 1<math display="inline"><semantics> <mrow> <mi>σ</mi> </mrow> </semantics></math>-error, and the goodness of fit (R-squared) of the least-squares estimate are shown in the diagram.</p>
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<p>Relation between the emulsion stability (<math display="inline"><semantics> <mrow> <mi>L</mi> </mrow> </semantics></math>-<math display="inline"><semantics> <mrow> <mi>T</mi> </mrow> </semantics></math>-plane), process sequences (top CEPO, middle COPE, and bottom PECO), and shear rates [s<sup>−1</sup>] of the scraped surface heat exchanger (shades of gray) and oil recipe (rapeseed oil circles, sunflower oil crosses). The datapoints correspond to the least-squares estimate over 3 replications with 3 measurements each (<math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>9</mn> </mrow> </semantics></math>). The error bars show the 1<math display="inline"><semantics> <mrow> <mi>σ</mi> </mrow> </semantics></math> estimation errors.</p>
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<p>Viscosity measured after 48 h of storage at 18 °C for different types of crystallization processes (CO: crystallized oil, COP: crystallized oil with press cake) and emulsification processes (COPE: crystallized oil–wax–press cake suspension emulsified with water, CEPO: crystallized wax–oil suspension emulsified with water plus press cake, and PECO: particle stabilized W/O emulsions, additionally crystallized) crystallized at 147 s<sup>−1</sup> and 880 s<sup>−1</sup> in the surface scraped freezer, as well as differences between rapeseed oil and sunflower oil, <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>9</mn> </mrow> </semantics></math>. Letters (a, b, c) indicate groups of significance, i.e., a group that does not share any letter is significantly different by the Wilcoxon test with a <span class="html-italic">p</span>-value of 0.05.</p>
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<p>Viscosity as a function of shear rate for CEPO produced with the rapeseed oil shear rate of the scraped heat exchanger of 147 s<sup>−1</sup>). The data are compared to the estimated model for shear thinning viscosity. Corresponding model parameters, their 1σ-error, and the goodness of fit (R-squared) of the least-squares estimate are shown in the diagram.</p>
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<p>Pairwise scatterplot of the characterization of the emulsion stability (<math display="inline"><semantics> <mrow> <mi>L</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>)</mo> </mrow> </semantics></math> and viscosity (<math display="inline"><semantics> <mrow> <mi>n</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>k</mi> </mrow> </semantics></math>, and<math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mrow> <mi>η</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> <mo>)</mo> </mrow> </semantics></math>. The data were grouped by process type (shades of gray). The isolines of the estimated bivariate probability (kernel density estimates) are shown in the upper triangular matrix and the estimated univariate probabilities in the diagonal.</p>
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<p>Emulsion stability indicated by the emulsion index after 504 h of storage at 18 °C for COPE: crystallized oil–wax–press cake suspension emulsified with water and crystallized at a shear rate of 147 s<sup>−1</sup> in the surface scraped freezer containing rapeseed or sunflower oil and either 0.5% microcrystalline, sunflower, or rapeseed wax, <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>9</mn> </mrow> </semantics></math>. Letters (a, b, c) indicate groups of significance, i.e., a group that does not share any letter is significantly different by the Wilcoxon test with a <span class="html-italic">p</span>-value of 0.05.</p>
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<p>Viscosity measured after 48 h of storage at 18 °C for crystallization (COP: crystallized oil with press cake) and emulsification process (COPE: crystallized oil-wax-press cake-suspension emulsified with water), both crystallized at 147 s<sup>−1</sup> in the surface scraped freezer for the raw materials rapeseed and sunflower oil and two types of wax, i.e., sunflower was and microcrystalline (MC) wax, added to each recipe, <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>9</mn> </mrow> </semantics></math>. Letters (a, b, c, d, e, f) indicate groups of significance, i.e., a group that does not share any letter is significantly different by the Wilcoxon test with a <span class="html-italic">p</span>-value of 0.05.</p>
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15 pages, 2623 KiB  
Article
Impact of Vanadium and Zirconium Contents on Properties of Novel Lightweight Ti3ZryNbVx Refractory High-Entropy Alloys
by Noura Al-Zoubi, Amer Almahmoud and Abdalla Obeidat
Solids 2025, 6(1), 2; https://doi.org/10.3390/solids6010002 - 2 Jan 2025
Viewed by 318
Abstract
This research explores the physical properties of refractory high-entropy alloys Ti3ZryNbVx (0.5 ≤ x ≤ 3.5; 1 ≤ y ≤ 2), utilizing the first-principles exact muffin-tin orbitals method, in addition to the coherent potential approximation. We examine the [...] Read more.
This research explores the physical properties of refractory high-entropy alloys Ti3ZryNbVx (0.5 ≤ x ≤ 3.5; 1 ≤ y ≤ 2), utilizing the first-principles exact muffin-tin orbitals method, in addition to the coherent potential approximation. We examine the atomic size difference (δ), the valence electron concentration (VEC) and the total energy of the body-centered cubic (bcc), the face-centered cubic (fcc) and the hexagonal close-packed (hcp) lattices, revealing a disordered solid solution with a bcc lattice as the stable phase of these alloys. The stability of the bcc Ti3ZryNbVx alloys increases with the addition of vanadium, and slightly decreases with increasing Zr concentration. All the investigated RHEAs have densities less than 6.2 g/cm3. Adding V to the Ti-Zr-Nb-V system reduces the volume and slightly enhances the density of the studied alloys. Our results show that increasing V content increases the tetragonal shear modulus C′, which assures that V enhances the mechanical stability of the bcc phase, and also increases the elastic moduli. Moreover, all the examined alloys are ductile. Vickers hardness and bond strength increase as V concentration increases. In contrast, decreasing Zr content reduces the density and increases the hardness and the bond strength of the present RHEAs, potentially resulting in systems with desirable mechanical properties and lower densities. These findings provide theoretical insights into the behavior of RHEAs, and emphasize the necessity for additional experimental investigations. Full article
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<p>The energy differences between the hcp and bcc phases (<b>left panel</b>) and between the fcc and bcc phases (<b>right panel</b>) of Ti<sub>3</sub>ZrNbV<span class="html-italic"><sub>x</sub></span><sub>,</sub> Ti<sub>3</sub>Zr<sub>1.5</sub>NbV<span class="html-italic"><sub>x</sub></span> and Ti<sub>3</sub>Zr<sub>2</sub>NbV<span class="html-italic"><sub>x</sub></span> RHEAs, as a function of V content (<span class="html-italic">x</span>).</p>
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<p>The right panel is the calculated Wigner–Seitz radius (in Bohr) and the left panel is the volumetric density (in g/cm<sup>3</sup>) of the Ti<sub>3</sub>ZrNbV<span class="html-italic"><sub>x</sub></span>, Ti<sub>3</sub>Zr<sub>1.5</sub>NbV<span class="html-italic"><sub>x</sub></span> and Ti<sub>3</sub>Zr<sub>2</sub>NbV<span class="html-italic"><sub>x</sub></span> RHEAs as a function of V content (<span class="html-italic">x</span>).</p>
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<p>The calculated single-crystal elastic constants <span class="html-italic">C</span><sub>11</sub>, <span class="html-italic">C</span><sub>12</sub>, <span class="html-italic">C</span>’ and <span class="html-italic">C</span><sub>44</sub> (units of GPa) for the bcc Ti<sub>3</sub>ZrNbV<span class="html-italic"><sub>x</sub></span>, Ti<sub>3</sub>Zr<sub>1.5</sub>NbV<span class="html-italic"><sub>x</sub></span> and Ti<sub>3</sub>Zr<sub>2</sub>NbV<span class="html-italic"><sub>x</sub></span> as a function of V atomic fraction <span class="html-italic">x</span>.</p>
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<p>The theoretical elastic moduli <span class="html-italic">B</span>, <span class="html-italic">G</span>, <span class="html-italic">E</span> and the <span class="html-italic">B</span>/<span class="html-italic">G</span> ratio for the bcc Ti<sub>3</sub>ZrNbV<span class="html-italic"><sub>x</sub></span>, Ti<sub>3</sub>Zr<sub>1.5</sub>NbV<span class="html-italic"><sub>x</sub></span> and Ti<sub>3</sub>Zr<sub>2</sub>NbV<span class="html-italic"><sub>x</sub></span>, versus V content (<span class="html-italic">x</span>).</p>
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<p>The calculated Poisson’s ratio <span class="html-italic">ν</span>(<span class="html-italic">x</span>) and Vickers hardness in the bcc phase for the Ti<sub>3</sub>ZrNbV<span class="html-italic"><sub>x</sub></span>, Ti<sub>3</sub>Zr<sub>1.5</sub>NbV<span class="html-italic"><sub>x</sub></span> and Ti<sub>3</sub>Zr<sub>2</sub>NbV<span class="html-italic"><sub>x</sub></span> RHEAs, versus V content (<span class="html-italic">x</span>).</p>
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<p>The calculated total density of states (TDOS) for the bcc Ti<sub>3</sub>ZrNbV<span class="html-italic"><sub>x</sub></span>, Ti<sub>3</sub>Zr<sub>1.5</sub>NbV<span class="html-italic"><sub>x</sub></span> and Ti<sub>3</sub>Zr<sub>2</sub>NbV<span class="html-italic"><sub>x</sub></span> RHEAs with different values of V content <span class="html-italic">x</span> (<span class="html-italic">x</span> = 0.5, 2, and 3.5). Vertical dashed lines indicate the Fermi energies at 0 Ry.</p>
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23 pages, 2532 KiB  
Article
Fabrication of Thymoquinone and Ascorbic Acid-Loaded Spanlastics Gel for Hyperpigmentation: In Vitro Release, Cytotoxicity, and Skin Permeation Studies
by Ahlam Zaid Alkilani, Rua’a Alkhaldi, Haneen A. Basheer, Bassam I. Amro and Maram A. Alhusban
Pharmaceutics 2025, 17(1), 48; https://doi.org/10.3390/pharmaceutics17010048 - 2 Jan 2025
Viewed by 436
Abstract
Background/Objectives: The demand for a safe compound for hyperpigmentation is continuously increasing. Bioactive compounds such as thymoquinone (TQ) and ascorbic acid (AA) induce inhibition of melanogenesis with a high safety profile. The aim of this study was to design and evaluate spanlastics [...] Read more.
Background/Objectives: The demand for a safe compound for hyperpigmentation is continuously increasing. Bioactive compounds such as thymoquinone (TQ) and ascorbic acid (AA) induce inhibition of melanogenesis with a high safety profile. The aim of this study was to design and evaluate spanlastics gel loaded with bioactive agents, TQ and AA, for the management of hyperpigmentation. Methods: Several spanlastics formulations were successfully fabricated and characterized in terms of morphology, vesicle size, zeta potential, and release. Results: The optimized TQ-loaded spanlastic formulation showed an average size of 223.40 ± 3.50 nm, and 133.00 ± 2.80 nm for AA-loaded spanlastic formulation. The optimized spanlastics formulation showed the highest entrapment efficiency (EE%) of 97.18 ± 2.02% and 93.08 ± 1.95%, for TQ and AA, respectively. Additionally, the edge activator concentration had a significant effect (p < 0.05) on EE%; it was found that by increasing the amount of EA, the EE% increases. Following that, the optimal spanlastics fomulation loaded with TQ and AA were incorporated into gel and explored for appearance, pH, spreadability, stability, rheology, in vitro release, ex vivo permeation study, and MTT cytotoxicity. The formulated spanlastics gel (R-1) has a pH of 5.53. Additionally, R-1 gel was significantly (p < 0.05) more spreadable than control gel, and exhibited a shear thinning behavior. Most importantly, ex vivo skin deposition studies confirmed superior skin deposition of TQ and AA from spanlastic gels. Additionally, results indicated that tyrosinase inhibition was primarily due to TQ. When comparing TQ alone with the TQ-AA combination, inhibition ranged from 18.35 to 42.73% and 24.28 to 42.53%, respectively. Both TQ spanlastics and the TQ-AA combination showed a concentration-dependent inhibition of tyrosinase. Conclusions: Spanlastic gel might represent a promising carrier for the dermal delivery of TQ and AA for the management of hyperpigmentation conditions. Full article
(This article belongs to the Section Nanomedicine and Nanotechnology)
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<p>Preparation of TQ and AA Spanlastics using the ethanol injection method.</p>
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<p>The developed spanlastics formula Z1 and Q4 containing AA and TQ, respectively.</p>
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<p>TEM images of spanlastics (<b>A</b>) Q-4 and (<b>B</b>) Z-1.</p>
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<p>FTIR Spectra of (<b>A</b>) Blank spanlastics, TQ, Q-4, and Physical mix and FTIR Spectra of (<b>B</b>) Blank spanlastics, AA, Z-1, and Physical mix.</p>
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<p>The prepared spanlastics R-1 gel and control gel (C-1).</p>
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<p>The flow curves (<b>A</b>) R-1 gel and C-1 gels determined at 32 °C. (<b>B</b>) The amplitude sweeps for R-1 gel. (<b>C</b>) The amplitude sweeps for C-1 gel. (<b>D</b>) The frequency sweeps for R-1 and C-1 gels. Data are presented as mean ± SD (n = 3).</p>
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<p>The release profile of Z-1, Q-4, TQ, and AA in R-1 gel. Data are presented as mean ± SD (n = 3).</p>
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<p>The Effect of R-1 gel and R-1 blank gel on HDF cell line viability compared to control, where no drug is added. Data are presented as mean ± SD (n = 3).</p>
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<p>The Tyrosinase Activity of TQ Spanlastics (Q-4), AA Spanlastics (Z-1), and their Combination Compared to Control, where no inhibitor is added. Data are presented as mean ± SD (n = 3).</p>
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18 pages, 4027 KiB  
Article
Analysis of the Structural Behavior Evolution of Reinforced Soil Retaining Walls Under the Combined Effects of Rainfall and Earthquake
by Xinxin Li, Xiaoguang Cai, Sihan Li, Xin Huang, Chen Zhu and Honglu Xu
Buildings 2025, 15(1), 115; https://doi.org/10.3390/buildings15010115 - 31 Dec 2024
Viewed by 430
Abstract
Major earthquakes and rainfall may occur at the same time, necessitating further investigation into the dynamic characteristics and responses of reinforced soil retaining walls subjected to the combined forces of rainfall and seismic activity. Three sets of shaking table tests on model retaining [...] Read more.
Major earthquakes and rainfall may occur at the same time, necessitating further investigation into the dynamic characteristics and responses of reinforced soil retaining walls subjected to the combined forces of rainfall and seismic activity. Three sets of shaking table tests on model retaining walls were designed, a modular reinforced earth retaining wall was utilized as the subject of this study, and a custom-made device was made to simulate rainfall conditions of varying intensities. These tests monitored the rainwater infiltration pattern, macroscopic phenomena, panel displacement, tension behavior, dynamic characteristics, and acceleration response of the modular reinforced earth retaining wall during vibration under different rainfall intensities. The results indicated the following. (1) Rainwater infiltration can be categorized into three stages: rapid rise, rapid decline, and slow decline to stability. The duration for infiltration to reach stability increases with greater rainfall. (2) An increase in rainfall intensity enhances the seismic stability of the retaining wall panel, as higher rainfall intensity results in reduced sand leakage from the panel, thereby diminishing panel deformation during vibration. (3) Increased rainfall intensity decreases the shear strength of the soil, leading to a greater load on the reinforcement. (4) The natural vibration frequencies of the three groups of retaining walls decreased by 0.21%, 0.54%, and 2.326%, respectively, indicating some internal damage within the retaining walls, although the degree of damage was not severe. Additionally, the peak displacement of the panel increased by 0.91 mm, 0.63 mm, and 0.61 mm, respectively. (5) The amplification effect of rainfall on internal soil acceleration is diminished, with this weakening effect becoming more pronounced as rainfall intensity increases. These research findings can provide a valuable reference for multi-disaster risk assessments of modular reinforced soil retaining walls. Full article
(This article belongs to the Section Building Structures)
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<p>Grain size distribution curve.</p>
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<p>Connection mode between reinforcement and panel.</p>
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<p>Rainfall device.</p>
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<p>Flow diagram of different pump tube speeds.</p>
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<p>Instrument layout.</p>
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<p>Time history of El Centro ground motion.</p>
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<p>Time history of white noise.</p>
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<p>Wetting front during and after rainfall: (<b>a</b>) R1; (<b>b</b>) R2; (<b>c</b>) R3.</p>
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<p>Wetting front during and after rainfall: (<b>a</b>) R1; (<b>b</b>) R2; (<b>c</b>) R3.</p>
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<p>Time history curve of moisture content: (<b>a</b>) R1; (<b>b</b>) R2; (<b>c</b>) R3.</p>
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<p>Panel sand leakage during vibration: (<b>a</b>) R1; (<b>b</b>) R2, (<b>c</b>) R3.</p>
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<p>Top crack after vibration.</p>
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<p>Deformation trend of panel.</p>
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<p>(<b>a</b>) Panel deformation before vibration; (<b>b</b>) panel deformation after vibration.</p>
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<p>Tension distribution of reinforcement: (<b>a</b>) R1; (<b>b</b>) R2; (<b>c</b>) R3.</p>
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<p>Distribution of natural frequency and damping ratio: (<b>a</b>) R1; (<b>b</b>) R2; (<b>c</b>) R3.</p>
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<p>Comparison of natural frequency and damping ratio distribution: (<b>a</b>) comparison of natural frequency distribution; (<b>b</b>) summary comparison of damping ratio distribution.</p>
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<p>Acceleration amplification factor distribution: (<b>a</b>) R1; (<b>b</b>) R2; (<b>c</b>) R3.</p>
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26 pages, 11888 KiB  
Article
The Behavior of Fluid Flow and Solute Transport in 3D Crossed Rock Fractures
by Xuefeng Han, Kangsheng Xue and Shaojie Zhang
Processes 2025, 13(1), 67; https://doi.org/10.3390/pr13010067 - 31 Dec 2024
Viewed by 257
Abstract
Understanding the behavior of fluid flow and solute transport in fractured rock is of great significance to geoscience and engineering. The discrete fracture network is the predominate channel for fluid flow through fractured rock as the permeability of fracture is several magnitudes higher [...] Read more.
Understanding the behavior of fluid flow and solute transport in fractured rock is of great significance to geoscience and engineering. The discrete fracture network is the predominate channel for fluid flow through fractured rock as the permeability of fracture is several magnitudes higher than that of the rock matrix. As the basic components of the fracture network, investigating the fluid flow in crossed fractures is the prerequisite of understanding the fluid flow in fractured rock. First, a program based on the successive random addition algorithm was developed to generate rough fracture surfaces. Next, a series of fracture models considering shear effects and different surface roughness were constructed. Finally, fluid dynamic analyses were performed to understand the role of flowrate and surface roughness in the evolution of flow field, concentration field, solute breakthrough, and solute mixing inside the crossed fractures. Results indicated that the channeling flow at the fracture intersection became more pronounced with the increasing Péclet number (Pe) and Joint Roughness Coefficient (JRC), the evolution of the concentration field was influenced by Pe and the distribution of the concentration field was influenced by JRC. For Pe < 10, the solute transport process was dominated by molecular diffusion. For 100 > Pe > 10, the solute transport process was in the complete mixing mode. In addition, for Pe > 100, the solute transport process was in the streamline routing mode. The concentration distribution was affected by the local aperture at the fracture intersection corresponding to different surface roughness. Meanwhile, the solute mixing equation was improved based on this result. The research results are beneficial for further revealing the mechanism of fluid flow and solute transport phenomenon in fractured rock. Full article
(This article belongs to the Section Chemical Processes and Systems)
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<p>The interpolation function diagram and schematic diagram of the fracture surface. (<b>a</b>–<b>d</b>) are the interpolation function diagrams corresponding to different fractal dimensions. (<b>e</b>–<b>h</b>) are the schematic diagrams of fracture surface with JRC values determined by the fractal dimensions.</p>
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<p>Upper and lower fracture surface area division, crossed fracture model, and illustration of the fracture walls. (<b>a</b>) Upper surface; (<b>b</b>) lower surface; (<b>c</b>) crossed rough-walled fracture model; and (<b>d</b>) illustration of the fracture walls. The top, bottom, left, and right walls (in blue) were termed as boundaries A, B, C, and D, corresponding to branches 1, 2, 3, and 4, respectively.</p>
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<p>(<b>a</b>) HT position of the crossed fractures intersection part; (<b>b</b>) geometric structure of the intersection part and division of the local aperture measurement profile.</p>
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<p>Local aperture at the intersection of the four branches along the Y-axis and local aperture ratios between branch 1 and branch 2, between branch 1 and branch 3, and between branch 1 and branch 4. (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) correspond to the local apertures when JRC values were 2.5, 7.5, 12.5, 17.5, respectively, and (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) are the respective local aperture ratios.</p>
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<p>Method block diagram.</p>
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<p>Breakthrough curves at boundary B with different mesh settings when JRC = 17.5: (<b>a</b>) Pe = 1; (<b>b</b>) Pe = 10; (<b>c</b>) Pe = 100; (<b>d</b>) Pe = 600.</p>
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<p>Evolution of the flow fields for JRC = 17.5 with increasing Pe: (<b>a</b>) Pe = 1; (<b>b</b>) Pe = 10; (<b>c</b>) Pe = 100; (<b>d</b>) Pe = 600.</p>
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<p>The evolution of the flow fields with increasing Pe for different JRC values: (<b>a</b>) JRC = 2.5; (<b>b</b>) JRC =7.5; (<b>c</b>) JRC =12.5; (<b>d</b>) JRC =12.5. In addition, the aperture variation zones are marked when JRC = 17.5.</p>
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<p>Comparison of flow fields between rough-walled and parallel-plate crossed fracture models. (<b>a</b>) Comparison of flow field distribution of combination 1 (inlets 1 and 4); (<b>b</b>) comparison of flow field distribution of combination 2 (inlets 1 and 2); and (<b>c</b>) comparison of flow field distribution of combination 3 (inlets 1 and 3).</p>
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<p>Evolution of the concentration distributions for different Pe when JRC = 17.5: (<b>a</b>–<b>d</b>) Pe = 1; (<b>e</b>–<b>h</b>) Pe = 10; (<b>i</b>–<b>l</b>) Pe = 100; (<b>m</b>–<b>p</b>) Pe = 600.</p>
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<p>Evolution of the concentration distributions for different JRC values when Pe = 600: (<b>a</b>–<b>d</b>) JRC = 2.5; (<b>e</b>–<b>h</b>) JRC = 7.5; (<b>i</b>–<b>l</b>) JRC = 12.5; (<b>m</b>–<b>p</b>) JRC = 17.5.</p>
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<p>Concentration evolutions in the XY plane direction and in the Y = 0 cross-section for different Pe when <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0.4</mn> <mover accent="true"> <mi>t</mi> <mo>¯</mo> </mover> </mrow> </semantics></math>: (<b>a</b>) Pe = 1; (<b>b</b>) Pe = 600.</p>
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<p>Breakthrough curves at boundaries B and C for different Pe when JRC = 17.5: (<b>a</b>) Pe = 1; (<b>b</b>) Pe = 10; (<b>c</b>) Pe = 100; (<b>d</b>) Pe = 600.</p>
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<p>Breakthrough curves at boundaries B, C, and D for different Pe when Pe = 600: (<b>a</b>) JRC = 2.5; (<b>b</b>) JRC = 7.5; (<b>c</b>) JRC = 12.5; (<b>d</b>) JRC = 17.5.</p>
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<p>Breakthrough curves at boundaries B, C, and D for different Pe when JRC = 17.5: (<b>a</b>) Pe = 2; (<b>b</b>) Pe = 3; (<b>c</b>) Pe = 4; (<b>d</b>) Pe = 5.</p>
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<p>Comparison of the mixing ratio as a function of Pe [<a href="#B14-processes-13-00067" class="html-bibr">14</a>,<a href="#B16-processes-13-00067" class="html-bibr">16</a>,<a href="#B19-processes-13-00067" class="html-bibr">19</a>,<a href="#B20-processes-13-00067" class="html-bibr">20</a>,<a href="#B28-processes-13-00067" class="html-bibr">28</a>].</p>
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16 pages, 4834 KiB  
Article
Performance Evaluation of Enhanced Oil Recovery by Host–Guest Interaction of β-Cyclodextrin Polymer/Hydrophobically Associative Polymer
by Xi Li, Zhongbing Ye and Pingya Luo
Molecules 2025, 30(1), 109; https://doi.org/10.3390/molecules30010109 - 30 Dec 2024
Viewed by 255
Abstract
In this work, a hydrophobically associative polymer (HAP) was mixed with β-cyclodextrin and epichlorohydrin polycondensate (β-CDP) in an aqueous solution to enhance the intermolecular interaction through host–guest inclusion between hydrophobes and cyclodextrins. Results showed that the host–guest interaction improved the thickening ability and [...] Read more.
In this work, a hydrophobically associative polymer (HAP) was mixed with β-cyclodextrin and epichlorohydrin polycondensate (β-CDP) in an aqueous solution to enhance the intermolecular interaction through host–guest inclusion between hydrophobes and cyclodextrins. Results showed that the host–guest interaction improved the thickening ability and viscoelasticity of the HAP solution and maintained its shear thinning behavior. The host–guest inclusion system demonstrated special viscosity–temperature curves and variable activation energy. Enhanced oil recovery (EOR) performance tests showed that the oil increment produced by the host–guest inclusion system was 5.5% and 9.3% higher than that produced by the HAP and the partially hydrolyzed polyacrylamide solution, respectively. Compared with pure HAP, β-CDP/HAP has a better comprehensive performance and is more attractive for EOR in high-temperature reservoirs. Full article
(This article belongs to the Special Issue Applied Chemistry in Asia)
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Graphical abstract

Graphical abstract
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<p>Apparent shear viscosity as a function of shear rate for the three polymer solutions in 4500 mg·L<sup>−1</sup> NaCl brine (<span class="html-italic">C</span><sub>p</sub> = 1750 mg·L<sup>−1</sup>, <span class="html-italic">T</span> = 25 °C).</p>
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<p>Shear degradation of polymer solutions (<span class="html-italic">C</span><sub>p</sub> = 1750 mg·L<sup>−1</sup>; Waring blender rotational speed, 6000 r/min).</p>
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<p>Effect of NaCl concentration on the viscosity of polymer solutions (<span class="html-italic">C</span><sub>p</sub> = 1750 mg·L<sup>−1</sup>; <span class="html-italic">γ</span> = 7.34 s<sup>−1</sup>).</p>
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<p>Effect of temperature on viscosity of polymer solutions (<span class="html-italic">C</span><sub>p</sub> = 1750 mg·L<sup>−1</sup>; <span class="html-italic">γ</span> = 7.34 s<sup>−1</sup>).</p>
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<p>The frequency sweep curve for the polymer solutions (<span class="html-italic">C</span><sub>p</sub> = 1750 mg·L<sup>−1</sup>; <span class="html-italic">T</span> = 45 °C).</p>
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<p>Resistance factor (RF) and residual resistance factor (RRF) as a function of pore volume (PV) of the polymer slugs (<span class="html-italic">C</span><sub>p</sub> = 1750 mg·L<sup>−1</sup>; <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>γ</mi> </mrow> <mo>˙</mo> </mover> </mrow> <mi>w</mi> </msub> </mrow> </semantics></math> = 7.34 s<sup>−1</sup>; <span class="html-italic">T</span> = 45 °C).</p>
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<p>Cumulative oil recovery and water cut reported as a function of cumulative PV for (<b>A</b>) HPAM, (<b>B</b>) HAP, and (<b>C</b>) β-CDP/HAP (<span class="html-italic">C</span><sub>p</sub> = 1750 mg·L<sup>−1</sup>; <span class="html-italic">T</span> = 45 °C).</p>
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<p>Schematic diagram of β-cyclodextrin, β-CDP, HPAM, and HAP.</p>
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<p>Schematic diagram of β-CD and epichlorohydrin condensation.</p>
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23 pages, 3901 KiB  
Article
Hypoplastic Modeling of Soil–Structure Contact Surface Considering Initial Anisotropy and Roughness
by Jingtao Yu, Junwang Cao, Zixuan Chen, Jintao Zhu, Yulong Zhang and Pengqiang Yu
Appl. Sci. 2025, 15(1), 244; https://doi.org/10.3390/app15010244 - 30 Dec 2024
Viewed by 257
Abstract
The development of a constitutive model for soil–structure contact surfaces remains a pivotal area of research within the field of soil–structure interaction. Drawing from the Gudehus–Bauer sand hypoplasticity model, this paper employs a technique that reduces the stress tensor and strain rate tensor [...] Read more.
The development of a constitutive model for soil–structure contact surfaces remains a pivotal area of research within the field of soil–structure interaction. Drawing from the Gudehus–Bauer sand hypoplasticity model, this paper employs a technique that reduces the stress tensor and strain rate tensor components to formulate a hypoplastic model tailored for sand–structure interfaces. To capture the influence of initial anisotropy, a deposition direction peak stress coefficient is incorporated; meanwhile, a friction parameter is introduced to address the surface roughness of the contact. Consequently, a comprehensive hypoplastic constitutive model is developed that takes into account both initial anisotropy and roughness. Comparative analysis with experimental data from soils on contact surfaces with diverse boundary conditions and levels of roughness indicates that the proposed model accurately forecasts shear test outcomes across various contact surfaces. Utilizing the finite element software ABAQUS 2021, an FRIC subroutine was developed, which, through simulating direct shear tests on sand–structure contact surfaces, has proven its efficacy in predicting the shear behavior of these interfaces. Full article
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<p>Schematic diagram of the contact surface coordinate system.</p>
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<p>Schematic diagram of contact surface stress and strain components.</p>
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<p>Variations in peak stress coefficient with the deposition direction.</p>
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<p>Diagram of peak-to-valley distance of the structural surface.</p>
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<p>Schematic diagram of strain–displacement relationship at the contact surface.</p>
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<p>Comparison of experimental results with simulation results for the contact surface between Toyoura sand and low-carbon steel plates.</p>
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<p>Experimental results and simulation results for Changping composite gravel–artificial rough steel plate contact surface under different roughness conditions.</p>
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<p>Experimental results and simulation results for Changping uniform gravel–artificial rough steel plate contact surface under different roughness conditions.</p>
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<p>Experimental results and simulation results for uniform density sand–concrete contact surface under staged stress path.</p>
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<p>Basic algorithm flow of the finite element method for the hypoplasticity model of contact surfaces.</p>
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<p>Schematic diagram of the geometric structure and boundary conditions of the finite element model for direct shear test: (<b>a</b>) front view and (<b>b</b>) side view.</p>
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<p>Model grid schematic diagram.</p>
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<p>Comparison between the numerical results of the hypoplastic model for contact surfaces and the theoretical solution.</p>
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<p>Shear stress–displacement relationship curves for contact surfaces under different interface roughness conditions: (<b>a</b>) e = 0.65 and (<b>b</b>) e = 0.95.</p>
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<p>Shear stress–displacement relationship curves for contact surfaces under different initial deposition angle conditions: (<b>a</b>) e = 0.65 and (<b>b</b>) e = 0.90.</p>
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<p>Comparison of shear stress–displacement relations between the Mohr–Coulomb model and the hypoplasticity model.</p>
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17 pages, 3714 KiB  
Article
Estimating VS30 at South Korean Seismic Observatory Stations Through Horizontal and Vertical Ground Motions
by Eric Yee and Chang-kyu Lee
Appl. Sci. 2025, 15(1), 214; https://doi.org/10.3390/app15010214 - 30 Dec 2024
Viewed by 251
Abstract
This investigation attempts to estimate time-averaged shear wave velocity in the upper 30 m of surficial material, VS30, from the horizontal-to-vertical spectral ratios, HVSRs, of seismic observatory stations in the South Korean region. From 2016 to 2023, a collection of 783 [...] Read more.
This investigation attempts to estimate time-averaged shear wave velocity in the upper 30 m of surficial material, VS30, from the horizontal-to-vertical spectral ratios, HVSRs, of seismic observatory stations in the South Korean region. From 2016 to 2023, a collection of 783 three-component ground motions were obtained from 19 stations operated by the Korea Institute of Geoscience and Mineral Resources. HVSRs were extracted from 5% damped acceleration and velocity RotD50 response spectra at each site. Peak HVSR frequencies and amplitudes were extracted and regressed to field-measured VS30s at available sites. An evaluation of different frequency and amplitude conditions was made to ascertain any effects on the regression. Findings included confirmation on minimum frequency and having amplitude conditions were unnecessary. Additionally, another peak frequency to VS30 relationship derived from Central and Eastern North America captured most of the behavior found in the Korean dataset. Full article
(This article belongs to the Section Earth Sciences)
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<p>Locations of KIGAM seismic observatory stations.</p>
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<p>Epicenters of earthquake events with M<sub>L</sub> ≥ 3.0 during the study period.</p>
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<p>Examples of HVSR versus frequency with the indicated station abbreviation and <span class="html-italic">V</span><sub>S30</sub>. The light gray lines represent the HVSRs derived from individual earthquake events. The solid red line represents the mean of the logarithmically distributed HVSR per frequency, and the dashed red line is ±1 standard deviation. The lower boundary of the green shaded area represents the mean of the HVSR, while the solid blue line is 1.5 × the averaged HVSR. (<b>a</b>) station YSUK, showing a well-defined peak; (<b>b</b>) station HCH, showing multiple peaks but <span class="html-italic">f</span><sub>p</sub> &lt; 1 Hz; (<b>c</b>) station WDL, showing <span class="html-italic">A</span><sub>p</sub> &gt; 2, but &lt;1.5 × HVSR<sub>avg</sub>; and (<b>d</b>) station YSUM, showing <span class="html-italic">A</span><sub>p</sub> &lt; 2, but &gt;1.5 × HVSR<sub>avg</sub>.</p>
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<p>(<b>a</b>) Resultant <span class="html-italic">f</span><sub>p</sub> and <span class="html-italic">V</span><sub>S30</sub> for acceleration and velocity datasets. Regressed line for acceleration data is shown in bold with dashed lines being ±1 standard deviation; (<b>b</b>) Residuals between the logarithm of measured data and the logarithm of estimates from acceleration and velocity-based data regression models.</p>
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<p>Examples of different acceleration HVSRs derived using an arithmetic mean in blue or a mean of logarithms shown in red. The gray lines represent the HVSRs derived from individual earthquake events. Dashed lines represent the mean ±1 standard deviation: (<b>a</b>) Station YSUK, where the means do not differ by much; (<b>b</b>) Station GCN, where the means differ significantly for <span class="html-italic">f</span> &lt; 3 Hz.</p>
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<p>Resultant <span class="html-italic">f</span><sub>p</sub> and <span class="html-italic">V</span><sub>S30</sub> for acceleration and velocity datasets when removing a minimum frequency condition. Regressed line for acceleration data is shown in bold with dashed lines being ±1 standard deviation. This regression does not consider HVSR with <span class="html-italic">f</span><sub>p</sub> &lt; 1 Hz. Station HVSR characteristics that did not satisfy the <span class="html-italic">A</span><sub>p</sub> conditions are shown in gray.</p>
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<p>(<b>a</b>) Resultant <span class="html-italic">f</span><sub>p</sub> and <span class="html-italic">V</span><sub>S30</sub> for acceleration and velocity datasets when absolute peak HVSR amplitude is selected. Regressed line for acceleration data is shown in bold with dashed lines being ±1 standard deviation; (<b>b</b>) Residuals between the logarithm of measured data and the logarithm of estimates from acceleration and velocity-based data regression models.</p>
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<p>Comparison of GA14J and GA14G models to current dataset. (<b>a</b>) Plot of resultant <span class="html-italic">f</span><sub>p</sub> and <span class="html-italic">V</span><sub>S30</sub> for acceleration dataset. GA14J model is shown in black lines while GA14G model is shown in violet lines. Dashed lines represent ±1 standard deviation of respective models. Shaded area is the ±1 standard deviation of the current model and is shown for comparison; (<b>b</b>) Residuals between measured data and estimates from acceleration data. Dashed lines indicate ±1 standard deviation of GA14 models. Again, shaded area is the ±1 standard deviation of the current model and is shown for comparison.</p>
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<p>Comparison of HA16 and HA18 models to current dataset. (<b>a</b>) Plot of resultant <span class="html-italic">f</span><sub>p</sub> and <span class="html-italic">V</span><sub>S30</sub> for acceleration dataset. HA16 model is shown in black lines, while HA18 model is shown in violet lines. Dashed lines represent ±1 standard deviation of respective models. Shaded area is the ±1 standard deviation of the current model and is shown for comparison; (<b>b</b>) Residuals between measured data and estimates from acceleration data. Dashed lines indicate ±1 standard deviation of HA models. Again, shaded area is the ±1 standard deviation of the current model and is shown for comparison.</p>
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<p>Comparison of YSH20 model to current dataset. (<b>a</b>) Plot of resultant <span class="html-italic">f</span><sub>p</sub> and <span class="html-italic">V</span><sub>S30</sub> for acceleration dataset. Dashed lines represent ±1 standard deviation of respective model. Shaded area is the ±1 standard deviation of the current model and is shown for comparison; (<b>b</b>) Residuals between measured data and estimates from acceleration data. Dashed lines indicate ±1 standard deviation of YSH20 model. Again, shaded area is the ±1 standard deviation of the current model and is shown for comparison.</p>
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18 pages, 4698 KiB  
Article
Computational Fluid Dynamics Simulation and Analysis of Non-Newtonian Drilling Fluid Flow and Cuttings Transport in an Eccentric Annulus
by Muhammad Ahsan, Shah Fahad and Muhammad Shoaib Butt
Mathematics 2025, 13(1), 101; https://doi.org/10.3390/math13010101 - 30 Dec 2024
Viewed by 307
Abstract
This study examines the flow behavior as well as the cuttings transport of non-Newtonian drilling fluid in the geometry of an eccentric annulus, accounting for what impacts drill pipe rotation might have on fluid velocity, as well as annular eccentricity on axial and [...] Read more.
This study examines the flow behavior as well as the cuttings transport of non-Newtonian drilling fluid in the geometry of an eccentric annulus, accounting for what impacts drill pipe rotation might have on fluid velocity, as well as annular eccentricity on axial and tangential distributions of velocity. A two-phase Eulerian–Eulerian model was developed by using computational fluid dynamics to simulate drilling fluid flow and cuttings transport. The kinetic theory of granular flow was used to study the dynamics and interactions of cuttings transport. Non-Newtonian fluid properties were modeled using power law and Bingham plastic formulations. The simulation results demonstrated a marked improvement in efficiency, as much as 45%, in transport by increasing the fluid inlet velocity from 0.54 m/s to 2.76 m/s, reducing the amount of particle accumulation and changing axial and tangential velocity profiles dramatically, particularly at narrow annular gaps. At a 300 rpm rotation, the drill pipe brought on a spiral flow pattern, which penetrated tangential velocities in the narrow gap that had increased transport efficiency to almost 30% more. Shear-thinning behavior characterizes fluid of which the viscosity, at nearly 50% that of the central core low-shear regions, was closer to the wall high-shear regions. Fluid velocity and drill pipe rotation play a crucial role in optimizing cuttings transport. Higher fluid velocities with controlled drill pipe rotation enhance cuttings removal and prevent particle build-up, thereby giving very useful guidance on how to clean the wellbore efficiently in drilling operations. Full article
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<p>Schematic of the eccentric annulus.</p>
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<p>Computational mesh and geometry domain.</p>
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<p>Axial velocities: (<b>a</b>) Re 1140, 0 rmp, Plane-1; (<b>b</b>) 1150, 300 rmp, Plane-1; (<b>c</b>) Re 1140, 0 rmp, Plane-2; (<b>d</b>) 1150, 300 rmp, Plane-2; (<b>e</b>) Re 1140, 0 rmp, Plane-3; (<b>f</b>) 1150, 300 rmp, Plane-3 (literature data source [<a href="#B23-mathematics-13-00101" class="html-bibr">23</a>]).</p>
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<p>Axial velocities: (<b>a</b>) Re 9300, 0 rmp, Plane-1; (<b>b</b>) 9200, 300 rmp, Plane-1; (<b>c</b>) Re 9300, 0 RPMrmpPlane-2; (<b>d</b>) 9200, 300 rmp, Plane-2; (<b>e</b>) Re 9300, 0 RPM, Plane-3; (<b>f</b>) 9200, 300 rmp, Plane-3 (literature data source [<a href="#B23-mathematics-13-00101" class="html-bibr">23</a>]).</p>
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<p>Tangential velocities: (<b>a</b>) Re 1150, 300 rpm, Plane-1; (<b>b</b>) 9200, 300 rpm, Plane-1; (<b>c</b>) Re 1150, 300 rpm, Plane-2; (<b>d</b>) 9200, 300 rpm, Plane-2; (<b>e</b>) Re 1150, 300 rpm, Plane-3; (<b>f</b>) 9200, 300 rpm, Plane-3 (literature data source [<a href="#B23-mathematics-13-00101" class="html-bibr">23</a>]).</p>
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<p>Velocity magnitude contours: (<b>a</b>) Re: 1140, 0 rpm; (<b>b</b>) Re: 1150, 300 rpm; (<b>c</b>) Re: 9300, 0 rpm; (<b>d</b>) Re: 9200, 300 rpm.</p>
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<p>Velocity vectors: (<b>a</b>) Re: 1140, 0 rpm; (<b>b</b>) Re: 1150, 300 rpm; (<b>c</b>) Re: 9300, 0 rpm; (<b>d</b>) Re: 9200, 300 rpm.</p>
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<p>Molecular viscosity contours: (<b>a</b>) Re: 1140, 0 rpm; (<b>b</b>) Re: 1150, 300 rpm; (<b>c</b>) Re: 9300, 0 rpm; (<b>d</b>) Re: 9200, 300 rpm.</p>
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