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21 pages, 5639 KiB  
Article
Study on Vibration and Noise of Railway Steel–Concrete Composite Box Girder Bridge Considering Vehicle–Bridge Coupling Effect
by Jinyan Si, Li Zhu, Weitao Ma, Bowen Meng, Huifeng Dong, Hongyang Ning and Guanyuan Zhao
Buildings 2024, 14(8), 2509; https://doi.org/10.3390/buildings14082509 - 14 Aug 2024
Abstract
A steel–concrete composite beam bridge fully exploits the mechanical advantages of the concrete structure and steel structure, and has the advantages of a fast construction speed and large stiffness. It is of certain research value to explore the application of this bridge type [...] Read more.
A steel–concrete composite beam bridge fully exploits the mechanical advantages of the concrete structure and steel structure, and has the advantages of a fast construction speed and large stiffness. It is of certain research value to explore the application of this bridge type in the field of railway bridges. However, with the rapid development of domestic high-speed railway construction, the problem of vibration and noise radiation of high-speed railway bridges caused by train loads is becoming more and more serious. A steel–concrete composite beam bridge combines the tensile characteristics of steel and the compressive characteristics of concrete perfectly. At the same time, it also has the characteristics of a steel bridge and concrete bridge in terms of vibration and noise radiation. This feature makes the study of the vibration and noise of the bridge type more complicated. Therefore, in this paper, the characteristics of vibration and noise radiation of a high-speed railway steel–concrete composite box girder bridge are studied in detail from two aspects: the theoretical basis and a numerical simulation. The main results obtained are as follows: Relying on the idea of vehicle–rail–bridge coupling dynamics, a structural dynamics analysis model of a steel–concrete combined girder bridge for a high-speed railroad was established, and numerical program simulation of the vibration of the vehicle–rail–bridge coupling system was carried out based on the parametric design language of ANSYS 18.0 and the language of MATLAB R2021a, and the structural vibration results were analyzed in both the time domain and frequency domain. By using different time-step loading for the vehicle–rail–bridge coupling vibration analysis, the computational efficiency can be effectively improved under the condition of guaranteeing the accuracy of the result analysis within 100 Hz. Based on the power flow equilibrium equation, a statistical energy method of calculating the high-frequency noise radiation is theoretically derived. Based on the theoretical basis of the statistical energy method, the high-frequency noise in the structure is numerically simulated in the VAONE 2021 software, and the average contribution of the concrete roof plate to the three acoustic field points constructed in this paper is as high as 50%, which is of great significance in the study of noise reduction in steel–concrete composite girders. Full article
(This article belongs to the Special Issue High-Performance Steel–Concrete Composite/Hybrid Structures)
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Figure 1

Figure 1
<p>Vertical vehicle model.</p>
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<p>Slab ballastless track structure and mechanical vertical model.</p>
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<p>Bridge midspan cross-section (in dm).</p>
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<p>Spatial samples of left and right rail height irregularity. (<b>a</b>) Spatial samples of left rail height irregularity. (<b>b</b>) Spatial samples of right rail height irregularity.</p>
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<p>Train–track–bridge dynamics model.</p>
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<p>Flow chart of vehicle–rail–bridge coupling calculation.</p>
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<p>Results chart for different time stepping loading. (<b>a</b>) Time history of acceleration at midspan of beam. (<b>b</b>) Time history of vehicle acceleration. Note: Multiplier in the diagram represents multiplier for every different time stepping loading.</p>
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<p>Time history of vertical displacement of the first wheel set of the train.</p>
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<p>Vertical dynamic response of the train body. (<b>a</b>) Time history of vertical displacement of the vehicle body. (<b>b</b>) Time history of vertical acceleration of the vehicle body.</p>
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<p>Time history of vertical acceleration of the rail. (<b>a</b>) Vertical wheel–rail force. (<b>b</b>) Vertical acceleration of the rail.</p>
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<p>Time history of the dynamic response of the bridge structure. (<b>a</b>) Acceleration diagram of the bridge deck flange and midspan. (<b>b</b>) Comparison of acceleration of steel base plate and abdominal plate.</p>
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<p>Technology roadmap for statistical energy methods.</p>
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<p>Fastener force time history and frequency data results. (<b>a</b>) Time history of fastener force. (<b>b</b>) Spectrum of fastener forces.</p>
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<p>Time-domain diagram of fastener force after high-pass filtering.</p>
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<p>The modal number of bending modes of steel–concrete composite beam subsystem.</p>
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<p>Semi-infinite fluid subsystem. (The brown portion represents concrete and the purple portion represents steel.)</p>
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<p>Acoustic field point diagram (m).</p>
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<p>Simulation results of point noise radiation in medium- and high-frequency fields. (<b>a</b>) Radiation efficiency of each subsystem of bridge. (<b>b</b>) Radiation spectrum curve of medium- and high-frequency field point noise.</p>
Full article ">Figure 19
<p>Vibration simulation results of medium- and high-frequency plates. (<b>a</b>) Vibration velocity of medium- and high-frequency bridge plate. (<b>b</b>) The total vibration level of medium- and high-frequency bridge plate. Notes: TP, AP, DP, and BP represent top plate, abdominal plate, diaphragm plate, and base plate, respectively.</p>
Full article ">Figure 20
<p>Results of the contribution of the high-frequency bridge plate in three field points A, B, and C. (<b>a</b>) The contribution spectrum of high-frequency bridge plate in field point A. (<b>b</b>) The contribution spectrum of high-frequency bridge plate in field point B. (<b>c</b>) The contribution spectrum of high-frequency bridge plate in field point C. (<b>d</b>) Bar chart of the total sound pressure level contributed by the panels at the three points. Notes: TP, AP, DP, BP, and OSPL represent top plate, abdominal plate, diaphragm plate, base plate, and overall sound pressure level, respectively.</p>
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<p>Noise contribution of the plate at the medium–high-frequency field point.</p>
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<p>Overall sound pressure level at medium- and high-frequency field points.</p>
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9 pages, 1294 KiB  
Article
Assessment of the Addition of Cricket (Acheta domesticus) Powder to Chickpea (Cicer arietinum) and Flaxseed (Linum usitatissimum) Flours: A Chemometric Evaluation of Their Pasting Properties
by Joseph Robert Nastasi, Siyu Ma, Shanmugam Alagappan, Louwrens C. Hoffman and Daniel Cozzolino
Appl. Sci. 2024, 14(16), 7131; https://doi.org/10.3390/app14167131 - 14 Aug 2024
Abstract
Edible insects have been evaluated as an alternative and sustainable source of protein because of their nutritive and functional properties for humans and domestic animals. The objective of this study was to assess the use of chemometric [principal component analysis (PCA) and partial [...] Read more.
Edible insects have been evaluated as an alternative and sustainable source of protein because of their nutritive and functional properties for humans and domestic animals. The objective of this study was to assess the use of chemometric [principal component analysis (PCA) and partial least squares (PLS)] combined with Rapid Visco Analyser (RVA) profiles to evaluate the addition of cricket powder (CKP) to chickpea (CPF) and flaxseed (FxF) flours. The results of this study showed that the addition of CKP powder to both CPF and FxF flours affects the pasting properties of the samples; in particular, a reduction in the peak (PV) and final viscosity (FV) was observed. The use of chemometric data techniques such as PCA and PLS regression allowed for a better interpretation of the RVA profiles. Both PCA and PLS regression allowed to qualitative and quantitatively identify the addition level of CKP powder to CPF and FxF flour samples. Differences in the PLS loadings associated with the RVA profile due to the addition of cricket powder were observed. The development of these methodologies will provide researchers and the food industry with better tools to both improve and monitor the quality of ingredients with functional properties as well as to further understand the use of insects as alternative sources of protein. Full article
(This article belongs to the Special Issue Food Chemistry, Analysis and Innovative Production Technologies)
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Figure 1

Figure 1
<p>RVA profile of binary mixtures of CPF and CFK (<b>A</b>), FxF and CKF (<b>B</b>), and FxF and CPF (<b>C</b>). A colour gradient is applied to each graph to signify a decrease in the concentration of either CPF or FxF. The range of the binary mixture is from 100:0 % <span class="html-italic">w/w</span> (pure flour no addition of other flour) to 0:100 % <span class="html-italic">w/w</span>. CPF = chickpea flour, CKF = cricket powder, and FxF = flaxseed meal flour.</p>
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<p>Principal component scores (<b>A</b>) and loadings (<b>B</b>) derived from the RVA analysis of insect blends and pure flours. Different pasting curves are represented as circles on the score plot. The circles are colour-scaled from high (red) to low (blue) for their final viscosity measurements to aid in visual interpretation. Two significant PCs were generated for this model, where the following variance was explained: PC1: 89%, PC2: 10%. R<sup>2</sup> = 99.3% and Q<sup>2</sup> = 99.3%. <a href="#applsci-14-07131-f002" class="html-fig">Figure 2</a>B loading corresponds to PC1 and the blue line corresponds to PC2.</p>
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<p>Partial least squares loadings derived from the predicted addition level of cricket powder to chickpea and flax meal flour analysed using the RVA.</p>
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13 pages, 6545 KiB  
Article
Layer-by-Layer Assembling and Capsule Formation of Polysaccharide-Based Polyelectrolytes Studied by Whispering Gallery Mode Experiments and Confocal Laser Scanning Microscopy
by Stefan Wagner, Mateusz Olszyna, Algi Domac, Thomas Heinze, Martin Gericke and Lars Dähne
Polysaccharides 2024, 5(3), 422-434; https://doi.org/10.3390/polysaccharides5030026 - 14 Aug 2024
Abstract
The layer-by-layer (LbL) assembling of oppositely charged polyelectrolytes was studied using semi-synthetic polysaccharide derivatives, namely the polycations 6-aminoethylamino-6-deoxy cellulose (ADC) and cellulose (2-(ethylamino)ethylcarbamate (CAEC), as well as the polyanion cellulose sulfate (CS). The synthetic polymers poly(allylamine) (PAH) and poly(styrene sulfonate) (PSS) were employed [...] Read more.
The layer-by-layer (LbL) assembling of oppositely charged polyelectrolytes was studied using semi-synthetic polysaccharide derivatives, namely the polycations 6-aminoethylamino-6-deoxy cellulose (ADC) and cellulose (2-(ethylamino)ethylcarbamate (CAEC), as well as the polyanion cellulose sulfate (CS). The synthetic polymers poly(allylamine) (PAH) and poly(styrene sulfonate) (PSS) were employed as well for comparison. The stepwise adsorption process was monitored by whispering gallery mode (WGM) experiments and zeta-potential measurements. Distinct differences between synthetic- and polysaccharide-based assemblies were observed in terms of the quantitative adsorption of mass and adsorption kinetics. The LbL-approach was used to prepare µm-sized capsules with the aid of porous and non-porous silica particle templates. The polysaccharide-based capsule showed a switchable permeability that was not observed for the synthetic polymer materials. At ambient pH values of 7, low-molecular dyes could penetrate the capsule wall while no permeation occurred at elevated pH values of 8. Finally, the preparation of protein-loaded LbL-capsules was studied using the combination of CAEC and CS. It was shown that high amounts of protein (streptavidin and ovomucoid) can be encapsulated and that no leaking or disintegration of the cargo macromolecules occurred during the preparation step. Based on this work, potential use in biomedical areas can be concluded, such as the encapsulation of bioactive compounds (e.g., pharmaceutical compounds, antibodies) for drug delivery or sensing purposes. Full article
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Graphical abstract

Graphical abstract
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<p>Results of whispering gallery mode adsorption experiments for the layer-by-layer assembling of bilayers of polycations (PAH: poly(allylamine); ADC: aminoethylamino-6-deoxy cellulose; CAEC: cellulose (2-(ethylamino)ethyl)carbamate) and polyanions (PSS: poly(styrene sulfonate); CS: cellulose sulfate) followed by a stability test against borate buffer (50 mM, pH value of 10) and PBS buffer displayed (<b>a</b>) over the whole process and (<b>b</b>) zoomed in for the second bilayer adsorption (after normalization to t = 0 s and WGM shift = 0 pm). Results for the quantification of the adsorption in terms of (<b>c</b>) mass of polyelectrolytes and (<b>d</b>) amount of polymer repeating units.</p>
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<p>Results of whispering gallery mode adsorption experiments for the layer-by-layer assembling of a bilayer of (<b>a</b>) aminoethylamino-6-deoxy cellulose/ADC and cellulose sulfate/CS or (<b>b</b>) poly(allylamine)/PAH and poly(styrene sulfonate)/PSS with two different mass concentrations for the polycation (black: 1 g/L, red: 0.1 g/L) zoomed in for the third bilayer adsorption (after normalization to t = 0 s and WGM shift = 0 pm).</p>
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<p>Zeta-potential (measured in pH 7 buffer) of polystyrene particles that were precoated with two double layers of poly(styrene sulfonate)/PSS and poly(allylamine)/PAH followed by alternating layers of polyanions (PSS; CS: cellulose sulfate) and polycations (PAH; ADC: aminoethylamino-6-deoxy cellulose; CAEC: cellulose (2-(ethylamino)ethyl)carbamate).</p>
Full article ">Figure 4
<p>Confocal laser scanning microscopy images capsules obtained by layer-by-layer assembling of cellulose (2-(ethylamino)ethyl)carbamate and cellulose sulfate that were functionalized at the amino groups with fluorescent 4-chloro-7-nitrobenzo-2-oxa-1,3-diazole (<b>a</b>) before and (<b>b</b>) after drying. Settings: 64× objective, 5× zoom, excitation at 405 nm, intensity of 15 %, emission from 450 to 600 nm, photomultiplier at 800 V.</p>
Full article ">Figure 5
<p>Confocal laser scanning microscopy images of capsules obtained by layer-by-layer assembling of polycations (PAH: poly(allylamine); CAEC: cellulose (2-(ethylamino)ethyl)carbamate and polyanions (PSS: poly(styrene sulfonate); CS: cellulose sulfate)) that were incubated with low-molecular sulfo-rhodamine (sRho) or high-molecular Rho-labeled PSS (PSS-Rho; 70 kDA) at different pH values. Settings: 63× objective, 1.5× zoom, excitation at 532 nm, intensity of 33 %, emission from 545 to 625 nm, photomultiplier at 650 V (<b>a</b>,<b>b</b>,<b>e</b>,<b>f</b>) or 685 V (<b>c</b>,<b>d</b>).</p>
Full article ">Figure 6
<p>Confocal laser scanning microscopy images in transmission (<b>a</b>) and fluorescence mode (<b>b</b>) of capsules obtained by layer-by-layer assembling of cellulose (2-(ethylamino)ethyl)carbamate and cellulose sulfate after loading with rhodamine-labeled streptavidin and drying. Settings 63× objective, 1× zoom, excitation at 532 nm, intensity of 33 %, emission from 545 to 625 nm, photomultiplier at 750 V.</p>
Full article ">Figure 7
<p>Confocal laser scanning microscopy images of capsules obtained by layer-by-layer assembling of cellulose (2-(ethylamino)ethyl)carbamate and cellulose sulfate that were loaded with rhodamine-labeled (Rho) streptavidin or ovomucoid and incubated in PBS-buffer for up to 4 days. Settings: 63× objective, 1× zoom, excitation at 532 nm, intensity of 33 %, emission from 545 to 625 nm, photomultiplier at 500 V.</p>
Full article ">Scheme 1
<p>Molecular structures of polycations and polyanions used in this study.</p>
Full article ">
21 pages, 339 KiB  
Review
Advancing Public Health Surveillance: Integrating Modeling and GIS in the Wastewater-Based Epidemiology of Viruses, a Narrative Review
by Diego F. Cuadros, Xi Chen, Jingjing Li, Ryosuke Omori and Godfrey Musuka
Pathogens 2024, 13(8), 685; https://doi.org/10.3390/pathogens13080685 - 14 Aug 2024
Abstract
This review article will present a comprehensive examination of the use of modeling, spatial analysis, and geographic information systems (GIS) in the surveillance of viruses in wastewater. With the advent of global health challenges like the COVID-19 pandemic, wastewater surveillance has emerged as [...] Read more.
This review article will present a comprehensive examination of the use of modeling, spatial analysis, and geographic information systems (GIS) in the surveillance of viruses in wastewater. With the advent of global health challenges like the COVID-19 pandemic, wastewater surveillance has emerged as a crucial tool for the early detection and management of viral outbreaks. This review will explore the application of various modeling techniques that enable the prediction and understanding of virus concentrations and spread patterns in wastewater systems. It highlights the role of spatial analysis in mapping the geographic distribution of viral loads, providing insights into the dynamics of virus transmission within communities. The integration of GIS in wastewater surveillance will be explored, emphasizing the utility of such systems in visualizing data, enhancing sampling site selection, and ensuring equitable monitoring across diverse populations. The review will also discuss the innovative combination of GIS with remote sensing data and predictive modeling, offering a multi-faceted approach to understand virus spread. Challenges such as data quality, privacy concerns, and the necessity for interdisciplinary collaboration will be addressed. This review concludes by underscoring the transformative potential of these analytical tools in public health, advocating for continued research and innovation to strengthen preparedness and response strategies for future viral threats. This article aims to provide a foundational understanding for researchers and public health officials, fostering advancements in the field of wastewater-based epidemiology. Full article
(This article belongs to the Special Issue Viruses in Water)
20 pages, 17160 KiB  
Article
Molecular Dynamics Study on the Mechanical Behaviors of Nanotwinned Titanium
by Bingxin Wu, Kaikai Jin and Yin Yao
Metals 2024, 14(8), 918; https://doi.org/10.3390/met14080918 - 14 Aug 2024
Abstract
Titanium and titanium alloys have been widely applied in the manufacture of aircraft engines and aircraft skins, the mechanical properties of which have a crucial influence on the safety and lifespan of aircrafts. Based on nanotwinned titanium models with different twin boundary spacings, [...] Read more.
Titanium and titanium alloys have been widely applied in the manufacture of aircraft engines and aircraft skins, the mechanical properties of which have a crucial influence on the safety and lifespan of aircrafts. Based on nanotwinned titanium models with different twin boundary spacings, the impacts of different loadings and twin boundary spacings on the plastic deformation of titanium were studied in this paper. It was found that due to the different contained twin boundaries, the different types of nanotwinned titanium possessed different dislocation nucleation abilities on the twin boundaries, different types of dislocation–twin interactions occurred, and significant differences were observed in the mechanical properties and plastic deformation mechanisms. For the {101-2} twin, basal plane dislocations were likely to nucleate on the twin boundary. The plastic deformation mechanism of the material under tensile loading was dominated by partial dislocation slip on the basal plane and face-centered cubic phase transitions, and the yield strength of the titanium increased with decreasing twin boundary spacing. However, under compression loading, the plastic deformation mechanism of the material was dominated by a combination of partial dislocation slip on the basal plane and twin boundary migration. For the {101-1} twin under tensile loading, the plastic deformation mechanism of the material was dominated by partial dislocation slip on the basal plane and crack nucleation and propagation, while under compression loading, the plastic deformation mechanism of the material was dominated by partial dislocation slip on the basal plane and twin boundary migration. For the {1124} twin, the interaction of its twin boundary and dislocation could produce secondary twins. Under tensile loading, the plastic deformation mechanism of the material was dominated by dislocation–twin and twin–twin interactions, while under compression loading, the plastic deformation mechanism of the material was dominated by partial dislocation slip on the basal plane, and the product of the dislocation–twin interactions was basal dislocation. All these results are of guiding value for the optimal design of microstructures in titanium, which should be helpful for achieving strong and tough metallic materials for aircraft manufacturing. Full article
(This article belongs to the Special Issue Deformation of Metals and Alloys: Theory, Simulations and Experiments)
Show Figures

Figure 1

Figure 1
<p>Potential energy per atom for {<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>2</mn> </mrow> </semantics></math>}&lt;<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>011</mn> </mrow> </semantics></math>&gt;, {<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>1</mn> </mrow> </semantics></math>}&lt;<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mover accent="true"> <mrow> <mn>2</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> </mrow> </semantics></math>&gt;, and {<math display="inline"><semantics> <mrow> <mn>11</mn> <mover accent="true"> <mrow> <mn>2</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>4</mn> </mrow> </semantics></math>}&lt;<math display="inline"><semantics> <mrow> <mn>22</mn> <mover accent="true"> <mrow> <mn>4</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mover accent="true"> <mrow> <mn>3</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> </mrow> </semantics></math>&gt; twin boundaries.</p>
Full article ">Figure 2
<p>Atomistic configurations of {<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>2</mn> </mrow> </semantics></math>}&lt;<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>011</mn> </mrow> </semantics></math>&gt; nanotwinned titanium with different twin boundary spacings: (<b>a</b>) 13.8 nm; (<b>b</b>) 11.1 nm; (<b>c</b>) 6.9 nm.</p>
Full article ">Figure 3
<p>Stress–strain curves of {<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>2</mn> </mrow> </semantics></math>}&lt;<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>011</mn> </mrow> </semantics></math>&gt; nanotwinned titanium with different twin boundary spacings under tensile loadings.</p>
Full article ">Figure 4
<p>Atomistic configurations of {<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>2</mn> </mrow> </semantics></math>}&lt;<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>011</mn> </mrow> </semantics></math>&gt; nanotwinned titanium with different twin boundary spacings under tensile loadings: (<b>a</b>) 13.8 nm; (<b>b</b>) 11.1 nm; (<b>c</b>) 6.9 nm.</p>
Full article ">Figure 5
<p>(<b>a</b>) Basal partial dislocation emitted from (<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>2</mn> </mrow> </semantics></math>) TB; (<b>b</b>) creation of numerous basal/prismatic interfaces.</p>
Full article ">Figure 6
<p>Stress–strain curves of {<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>2</mn> </mrow> </semantics></math>}&lt;<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>011</mn> </mrow> </semantics></math>&gt; nanotwinned titanium with different twin boundary spacings under compressive loadings.</p>
Full article ">Figure 7
<p>Atomistic configurations of {<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>2</mn> </mrow> </semantics></math>}&lt;<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>011</mn> </mrow> </semantics></math>&gt; nanotwinned titanium with different twin boundary spacings under compressive loadings: (<b>a</b>) 27.6 nm; (<b>b</b>) 11.1 nm; (<b>c</b>) 6.9 nm.</p>
Full article ">Figure 8
<p>The phenomenon of detwinning due to (<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>2</mn> </mrow> </semantics></math>) TB migration: (<b>a</b>) 11.1 nm; (<b>b</b>) 6.9 nm.</p>
Full article ">Figure 9
<p>Stress–strain curves of {<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>1</mn> </mrow> </semantics></math>}&lt;<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mover accent="true"> <mrow> <mn>2</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> </mrow> </semantics></math>&gt; nanotwinned titanium with different twin boundary spacings under tensile loadings.</p>
Full article ">Figure 10
<p>Atomistic configurations of {<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>1</mn> </mrow> </semantics></math>}&lt;<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mover accent="true"> <mrow> <mn>2</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> </mrow> </semantics></math>&gt; nanotwinned titanium with different twin boundary spacings under tensile loadings: (<b>a</b>) 13.5 nm; (<b>b</b>) 10.8 nm.</p>
Full article ">Figure 11
<p>(<b>a</b>) A (<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>1</mn> </mrow> </semantics></math>) pyramidal partial dislocation; (<b>b</b>) a prismatic partial dislocation.</p>
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<p>Stress–strain curves of {<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>1</mn> </mrow> </semantics></math>}&lt;<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mover accent="true"> <mrow> <mn>2</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> </mrow> </semantics></math>&gt; nanotwinned titanium with different twin boundary spacings under compressive loadings.</p>
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<p>Atomistic configurations of {<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>1</mn> </mrow> </semantics></math>}&lt;<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mover accent="true"> <mrow> <mn>2</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> </mrow> </semantics></math>&gt; nanotwinned titanium with different twin boundary spacings under compressive loadings: (<b>a</b>) 13.5 nm; (<b>b</b>) 6.7 nm.</p>
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<p>(<b>a</b>) A (<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>1</mn> </mrow> </semantics></math>) pyramidal slip; (<b>b</b>) the migration of (<math display="inline"><semantics> <mrow> <mn>10</mn> <mover accent="true"> <mrow> <mn>1</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>1</mn> </mrow> </semantics></math>) TB due to twinning dislocation slip.</p>
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<p>Stress–strain curves of {<math display="inline"><semantics> <mrow> <mn>11</mn> <mover accent="true"> <mrow> <mn>2</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>4</mn> </mrow> </semantics></math>}&lt;<math display="inline"><semantics> <mrow> <mn>22</mn> <mover accent="true"> <mrow> <mn>4</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mover accent="true"> <mrow> <mn>3</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> </mrow> </semantics></math>&gt; nanotwinned titanium with different twin boundary spacings under tensile loadings.</p>
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<p>Atomistic configurations of {<math display="inline"><semantics> <mrow> <mn>11</mn> <mover accent="true"> <mrow> <mn>2</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>4</mn> </mrow> </semantics></math>}&lt;<math display="inline"><semantics> <mrow> <mn>22</mn> <mover accent="true"> <mrow> <mn>4</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mover accent="true"> <mrow> <mn>3</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> </mrow> </semantics></math>&gt; nanotwinned titanium with twin boundary spacings of 17 nm under tensile loading.</p>
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<p>(<b>a</b>,<b>b</b>) The (<math display="inline"><semantics> <mrow> <mn>11</mn> <mover accent="true"> <mrow> <mn>2</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>1</mn> </mrow> </semantics></math>) twin cutting through basal SF boundaries, and (<b>c</b>,<b>d</b>) (<math display="inline"><semantics> <mrow> <mn>11</mn> <mover accent="true"> <mrow> <mn>2</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>1</mn> </mrow> </semantics></math>) TB migration leading to detwinning.</p>
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<p>Atomistic configurations of {<math display="inline"><semantics> <mrow> <mn>11</mn> <mover accent="true"> <mrow> <mn>2</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>4</mn> </mrow> </semantics></math>}&lt;<math display="inline"><semantics> <mrow> <mn>22</mn> <mover accent="true"> <mrow> <mn>4</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mover accent="true"> <mrow> <mn>3</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> </mrow> </semantics></math>&gt; nanotwinned titanium with twin boundary spacings of 7.3 nm under tensile loading.</p>
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<p>Stress–strain curves of {<math display="inline"><semantics> <mrow> <mn>11</mn> <mover accent="true"> <mrow> <mn>2</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>4</mn> </mrow> </semantics></math>}&lt;<math display="inline"><semantics> <mrow> <mn>22</mn> <mover accent="true"> <mrow> <mn>4</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mover accent="true"> <mrow> <mn>3</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> </mrow> </semantics></math>&gt; nanotwinned titanium with different twin boundary spacings under compressive loadings.</p>
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<p>Atomistic configurations of the {<math display="inline"><semantics> <mrow> <mn>11</mn> <mover accent="true"> <mrow> <mn>2</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mn>4</mn> </mrow> </semantics></math>}&lt;<math display="inline"><semantics> <mrow> <mn>22</mn> <mover accent="true"> <mrow> <mn>4</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> <mover accent="true"> <mrow> <mn>3</mn> </mrow> <mo mathvariant="normal">-</mo> </mover> </mrow> </semantics></math>&gt; nanotwinned titanium with different twin boundary spacings under compressive loadings, (<b>a</b>) 17 nm; (<b>b</b>) 7.3 nm.</p>
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17 pages, 24883 KiB  
Article
Synergistic Effects of Titanium-Based MOFs MIL-125 with Intumescent Flame Retardants in ABS Polymer Composites on Flame Retardancy Study
by Zhuoran Zhang, Yufeng Quan, Ruiqing Shen, Kun-Yu Wang, Hong-Cai Zhou and Qingsheng Wang
Fire 2024, 7(8), 284; https://doi.org/10.3390/fire7080284 - 14 Aug 2024
Viewed by 116
Abstract
The intumescent flame retardant (IFR) technique is an alternative to halogen-based flame retardants for reducing fire hazards in polymers. However, IFR has drawbacks like unsatisfactory flame-retardant efficiency and high loading requirements. In this study, MIL-125 (Ti-based metal–organic framework) is added to ABS/IFR composites [...] Read more.
The intumescent flame retardant (IFR) technique is an alternative to halogen-based flame retardants for reducing fire hazards in polymers. However, IFR has drawbacks like unsatisfactory flame-retardant efficiency and high loading requirements. In this study, MIL-125 (Ti-based metal–organic framework) is added to ABS/IFR composites to improve flame retardancy and reduce smoke emissions. Thermogravimetric analysis (TGA) results indicate that combining ammonium polyphosphate (APP) and expandable graphite (EG) increases charred residue and slows mass loss compared with the original ABS resin. The ABS/IFR/MIL-125 system stabilizes the char layer, serving as a protective shield against combustible gases during combustion. Additionally, MIL-125 enhances performance in microscale combustion calorimetry (MCC) flammability testing. In fire tests (UL-94, limiting oxygen index (LOI), and cone calorimeter), the ABS/IFR/MIL-125 system achieves a UL-94 V0 rating and the highest LOI value of 31.5% ± 0.1%. Peak heat lease rate (PHRR) values in the cone calorimeter are reduced by 72% with 20 wt.% of additives, and smoke production decreases by 53% compared with neat ABS. These results demonstrate the efficient synergistic effects of MIL-125 and IFR additives in improving the formation and stability of the intumescent char layer, thereby protecting ABS from intense burning. Full article
(This article belongs to the Special Issue Fire Hazard of Polymer Composites and Nanocomposites)
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<p>Ball-and-stick structural model of MIL-125 from VESTA 3 [<a href="#B15-fire-07-00284" class="html-bibr">15</a>]. (Brown ball for carbon atom, pink ball for hydrogen atom, red ball for oxygen atom, and blue ball for titanium atom).</p>
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<p>Schema of melt blending process for ABS/IFR/MIL-125 using twin-screw extruder.</p>
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<p>XRD patterns of ABS, MIL-125, and ABS/MIL-125 nanocomposite.</p>
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<p>SEM images of (<b>a</b>) neat ABS surface, (<b>b</b>) ABS/MIL-125 nanocomposites, (<b>c</b>) MIL-125 powder.</p>
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<p>(<b>a</b>) TGA, (<b>b</b>) DTG curves of ABS/MIL-125 nanocomposites with different weight ratios.</p>
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<p>MCC HRR curves of ABS/MIL-125 nanocomposites with different weight ratios.</p>
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<p>XRD patterns of APP, EG, MIL-125, and ABS/IFR/MIL-125 composite.</p>
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<p>SEM images of (<b>a</b>) neat ABS surface, (<b>b</b>) ABS/IFR/MIL-125 composites.</p>
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<p>(<b>a</b>) SEM image of ABS composites and EDS mapping of (<b>b</b>) P (<b>c</b>) Ti elements.</p>
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<p>(<b>a</b>) TGA, (<b>b</b>) DTG curves of ABS/IFR/MIL-125 composites with different weight ratios.</p>
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<p>MCC HRR curves of ABS/IFR/MIL-125 composites with different weight ratios.</p>
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<p>(<b>a</b>) HRR (<b>b</b>) SPR curves of ABS and ABS/IFR/MIL-125 composites in cone calorimeters.</p>
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<p>SEM images of the char residue of ABS10 (<b>a</b>,<b>b</b>) and ABS9 (<b>c</b>,<b>d</b>) after cone calorimeter tests.</p>
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<p>SEM images of the char residue of ABS15 (<b>a</b>,<b>b</b>) and ABS14 (<b>c</b>,<b>d</b>) after cone calorimeter tests.</p>
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23 pages, 1438 KiB  
Article
A Structural Equation Model on Critical Risk and Success in Public–Private Partnership: Exploratory Study
by Medya Fathi
J. Risk Financial Manag. 2024, 17(8), 354; https://doi.org/10.3390/jrfm17080354 - 13 Aug 2024
Viewed by 282
Abstract
In construction, risk is inherent in each project, and success involves meeting defined objectives beyond budget and schedule. Factors vary for infrastructure projects, and their correlation with performance must be studied. In the case of public–private partnership (PPP) transportation, the level of complexity [...] Read more.
In construction, risk is inherent in each project, and success involves meeting defined objectives beyond budget and schedule. Factors vary for infrastructure projects, and their correlation with performance must be studied. In the case of public–private partnership (PPP) transportation, the level of complexity is higher due to more involved parties. Risks and success factors in PPP projects affect each other, which may lead to project failure. Recognizing the critical risk factors (CRFs) and critical success factors (CSFs) is indispensable to ensure the success of PPP infrastructure project implementation. However, the existing research on the PPP risk and success relationship has not gone into sufficient detail, and more support to address the existing gaps in the body of knowledge and literature is necessary. Therefore, in response to the missing area in the public–private partnership transportation industry, this paper analyzed the correlation between PPP risks and success factors. It identified, explored, and categorized various risk and success factors by combining a literature review, expert panel interviews, and a questionnaire survey among both the public and private sectors, a win–win principle. The data collected were analyzed using the structural equation modeling (SEM) approach and relative significance. Results show the relationship between risk and success factors, their influence on PPPs, and the most important factors, known as CRFs and CSFs, with high loading factors (LF > 0.5) and high relative importance (NMS > 0.5). The top five CRFs include “Contract quality (incomplete, conflicting)”, “Staff expertise and experience”, “Financial market risk”, “Conflicting objectives and expectations”, and “Inefficient feasibility study”. The top five CSFs were found as “Appropriate risk allocation and risk-sharing”, “Strong financial capacity and capability of the private sector”, “Government providing guarantees”, “Employment of professional advisors”, and “Realistic assessment of the cost and benefits”. This study advances the understanding of risk and success factors in PPPs and contributes to the theoretical foundations, which will benefit not only public management, policy consultants, and investors but also academics interested in studying PPP transportation projects. Full article
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<p>The flow chart of the research method.</p>
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<p>Modified structural equation model with loading factors and path coefficients (first-order analysis and second-order analysis (dashed arrows)).</p>
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<p>Scatter chart with loading factors (LFs) and normalized mean score (NMS).</p>
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24 pages, 8704 KiB  
Article
Immunomodulatory R848-Loaded Anti-PD-L1-Conjugated Reduced Graphene Oxide Quantum Dots for Photothermal Immunotherapy of Glioblastoma
by Yu-Jen Lu, Reesha Kakkadavath Vayalakkara, Banendu Sunder Dash, Shang-Hsiu Hu, Thejas Pandaraparambil Premji, Chun-Yuan Wu, Yang-Jin Shen and Jyh-Ping Chen
Pharmaceutics 2024, 16(8), 1064; https://doi.org/10.3390/pharmaceutics16081064 (registering DOI) - 13 Aug 2024
Viewed by 234
Abstract
Glioblastoma multiforme (GBM) is the most severe form of brain cancer and presents unique challenges to developing novel treatments due to its immunosuppressive milieu where receptors like programmed death ligand 1 (PD-L1) are frequently elevated to prevent an effective anti-tumor immune response. To [...] Read more.
Glioblastoma multiforme (GBM) is the most severe form of brain cancer and presents unique challenges to developing novel treatments due to its immunosuppressive milieu where receptors like programmed death ligand 1 (PD-L1) are frequently elevated to prevent an effective anti-tumor immune response. To potentially shift the GBM environment from being immunosuppressive to immune-enhancing, we engineered a novel nanovehicle from reduced graphene oxide quantum dot (rGOQD), which are loaded with the immunomodulatory drug resiquimod (R848) and conjugated with an anti-PD-L1 antibody (aPD-L1). The immunomodulatory rGOQD/R8/aPDL1 nanoparticles can actively target the PD-L1 on the surface of ALTS1C1 murine glioblastoma cells and release R848 to enhance the T-cell-driven anti-tumor response. From in vitro experiments, the PD-L1-mediated intracellular uptake and the rGOQD-induced photothermal response after irradiation with near-infrared laser light led to the death of cancer cells and the release of damage-associated molecular patterns (DAMPs). The combinational effect of R848 and released DAMPs synergistically produces antigens to activate dendritic cells, which can prime T lymphocytes to infiltrate the tumor in vivo. As a result, T cells effectively target and attack the PD-L1-suppressed glioma cells and foster a robust photothermal therapy elicited anti-tumor immune response from a syngeneic mouse model of GBM with subcutaneously implanted ALTS1C1 cells. Full article
(This article belongs to the Special Issue Metal and Carbon Nanomaterials for Pharmaceutical Applications)
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<p>(<b>a</b>) Schematic illustration of the preparation of rGOQD/R8/aPDL1, including reduction of GOQD to rGOQD using polyethylene (PEI), immunoadjuvant drug R848 loading on rGOQD by π–π stacking (rGOQD/R8), and aPD-L1 conjugation on rGO/R8 using amine groups in rGOQD and aldehyde group in activated aPD-L1. (<b>b</b>) The photo-immunotherapy using rGOQD/R8/aPDL1 involves photothermal therapy and immune cell activation to exert an anti-tumor effect by (1) binding to the overexpressed PD-L1 receptors on tumor cell surface; (2) R484 release for activation of adaptive immune response; (3) photothermal-effect-induced cell death; (4) antigen release and antigen-presenting cells (APCs) activation; (5) dendritic cells (DCs) activation; (6) T cells recruitment.</p>
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<p>(<b>a</b>) The TEM image of rGOQD/R8/aPDL1 (scale bar = 500 nm). (<b>b</b>) The size distribution from dynamic light scattering analysis of GOQD, rGOQD, rGOQD/R8, and rGOQD/R8/aPDL1. (<b>c</b>) The zeta potential values of different nanoparticles (mean ± SD, <span class="html-italic">n</span> = 3). (<b>d</b>) The FTIR spectra of GOQD, rGOQD, and rGOQD/R8. (<b>e</b>) The UV-Vis spectroscopy analysis of GOQD, rGOQD, rGOQD/R8, and rGOQD/R8/aPDL1.</p>
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<p>The photothermal images (<b>a</b>), and the corresponding temperature profiles (<b>b</b>) by irradiating GOQD or rGOQD/R8 (100 μg/mL) with 808 nm laser (1.5 W/cm<sup>2</sup>) for 5 min. The control is deionized water (DIW). The thermal images (<b>c</b>), and the corresponding temperature profiles (<b>d</b>) by irradiating 25–100 μg/mL rGOQD/R8/aPDL1 with 808 nm laser (1.5 W/cm<sup>2</sup>) for 5 min. (<b>e</b>) The in vitro release of R848 from rGOQD/R8/aPDL1 at pH 5 and 7.4. (<b>f</b>) The stability of rGOQD/R8/aPDL1 in PBS and DMEM cell culture medium by measuring the particle size from DLS. All data are represented as mean ± SD (<span class="html-italic">n</span> = 3).</p>
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<p>The biocompatibility of rGOQD/R8/aPDL1 was tested with 3T3 mouse embryonic fibroblast cells (<b>a</b>) and ALTS1C1 mouse glioma cells (<b>b</b>) with MTS assays at 24, 48, and 72 h. (<b>c</b>) The flow cytometry analysis of intracellular uptake of Cy5.5-tagged rGOQD/R8 and rGOQD/R8/aPDL1 after incubation with ALTS1C1 cells for 4 and 24 h. (<b>d</b>) The corresponding quantified fluorescence intensity from flow cytometry analysis of intracellular uptake of Cy5.5-tagged nanoparticles. * <span class="html-italic">p</span> &lt; 0.05 compared with rGOQD/R8. All data are represented as mean ± SD (<span class="html-italic">n</span> = 3).</p>
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<p>(<b>a</b>) The cell viability of ALTS1C1 cells after incubating cells with rGOQD/R8 (rGOQD/R8+L) or rGOQD/R8/aPDL1 (rGOQD/R8/aPDL1+L) at varying concentrations and irradiated with 808 nm laser at 1.5 W/cm<sup>2</sup> for 5 min. (<b>b</b>) The fluorescence microscopy images of Calcein-AM and PI co-stained cells after incubation with different nanoparticles and 808 nm NIR treatment at 1.5 W/cm<sup>2</sup> for 5 min. Cells treated with PBS and rGOQD/R8 without laser irradiation were used as controls. (<b>c</b>) The CLSM images of ALTS1C1 cells by staining with fluorescein-tagged anti-calreticulin antibody after different treatments. The rGOQD/R8+L and rGOQD/R8/aPDL1+L groups are irradiated with 808 nm NIR laser at 1.5 W/cm<sup>2</sup> for 5 min. (<b>d</b>) The corresponding quantification of calreticulin fluorescence intensity by using the PAX-it software. All data are represented as mean ± SD (<span class="html-italic">n</span> = 3). <sup>α</sup> <span class="html-italic">p</span> &lt; 0.05 compared to PBS, <sup>β</sup> <span class="html-italic">p</span> &lt; 0.05 compared to rGOQD/R8, <sup>γ</sup> <span class="html-italic">p</span> &lt; 0.05 compared to rGOQD/R8+L.</p>
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<p>The ex vivo fluorescence images of major organs (<b>a</b>) and the corresponding quantification of nanoparticle distribution in each organ from fluorescence intensity (<b>b</b>) with an in vivo imaging system (IVIS) 4 h after administration of Cy5.5-labelled rGOQD/R8/aPDL1 or rGOQD/R8 to ALTS1C1 tumor-bearing mice through the tail vein. The ex vivo fluorescence images (<b>c</b>) and the corresponding fluorescence intensity (<b>d</b>) of tumors with an IVIS 4 h after administration of Cy5.5-labelled rGOQD/R8/aPDL1 or rGOQD/R8 to ALTS1C1 tumor-bearing mice through the tail vein.</p>
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<p>The thermal images (<b>a</b>) and the corresponding in vivo peak temperature profiles (<b>b</b>) of ALTS1C1 tumor-bearing mice after intravenous injection of rGOQD/R8/aPDL1 or rGOQD/R8 followed by 808 nm NIR laser irradiation 24 h post injection (mean ± SD, <span class="html-italic">n</span> = 3). The activation of dendritic cells by rGOQD/R8/aPDL1 or rGOQD/R8 was compared with confocal images of lymph nodes 19 days after treatment by immunofluorescence staining of CD11C (antigen-presenting cells) and CD86 (dendritic cells) in red and green, respectively, and counterstaining the nucleus with DAPI in blue (scale bar = 50 μm) (<b>c</b>).</p>
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<p>The in vivo therapeutic evaluation was studied using a syngeneic mouse model of GBM with subcutaneously implanted ALTSC1 cells. The mice were divided into four groups and the treatment was initiated by injection of samples on day 10, followed by intravenous injection on days 13, 17, and 20. The control group is PBS and the rGOQD/R8+L and rGOQD/R8/PDL1 groups are with 808 nm NIR laser irradiation at 1.5 W/cm<sup>2</sup> for 5 min. The tumor volume change (<b>a</b>), the scattered plot of tumor volume on day 21 (<b>b</b>), and the survival curve of animals (<b>c</b>) of ALTSC1 tumor-bearing mice after different treatments (mean ± SD, <span class="html-italic">n</span> = 3). The sacrificing criteria were when the tumor volume exceeded 1000 mm<sup>3</sup>. <sup>α</sup> <span class="html-italic">p</span> &lt; 0.05 compared to PBS, <sup>β</sup> <span class="html-italic">p</span> &lt; 0.05 compared to rGOQD/R8, <sup>γ</sup> <span class="html-italic">p</span> &lt; 0.05 compared to rGOQD/R8+L.</p>
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<p>(<b>a</b>) In vivo immune response after treatment with rGOQD/R8, rGOQD/R8+L, and rGOQD/R8/aPDL1+L. The infiltration of T cells into the tumor after various treatments was measured by immunofluorescence staining with anti-CD4 and anti-CD8 antibodies. The confocal immunofluorescence images and the corresponding quantification of fluorescence intensity of CD4 and CD8 in the tumor tissues 19 days after treatments (scale bar = 50 μm). (<b>b</b>) The immunohistochemistry (IHC) of PD-L1, tumor necrosis factor (TNF-α), and Ki-67, and H&amp;E staining of tumor tissues 19 days after treatments. <sup>α</sup> <span class="html-italic">p</span> &lt; 0.05 compared to PBS, <sup>β</sup> <span class="html-italic">p</span> &lt; 0.05 compared to rGOQD/R8, <sup>γ</sup> <span class="html-italic">p</span> &lt; 0.05 compared to rGOQD/R8+L.</p>
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21 pages, 4817 KiB  
Article
Experimental Study on Axial Compressive Performance of Recycled Steel Fiber Reinforced Concrete Short Columns with Steel Pipes
by Bin Wang, Hui Lv, Yongtao Gao, Minggao Tang, Nansheng Ding, Xiao Zhao, Hua Zhao and Xiao Hu
Buildings 2024, 14(8), 2498; https://doi.org/10.3390/buildings14082498 - 13 Aug 2024
Viewed by 305
Abstract
To explore the axial compressive mechanical properties of steel tube recycled steel fiber reinforced concrete short columns (STRSFRCSCs), axial compression tests were conducted on ten STRSFRCSCs and two steel tube reinforced concrete short columns (STRCSCs), mainly analyzing the effects of recycled steel fiber [...] Read more.
To explore the axial compressive mechanical properties of steel tube recycled steel fiber reinforced concrete short columns (STRSFRCSCs), axial compression tests were conducted on ten STRSFRCSCs and two steel tube reinforced concrete short columns (STRCSCs), mainly analyzing the effects of recycled steel fiber (RSF) content, steel content, and concrete strength grade on their mechanical properties. The results showed that different RSF contents had no significant effect on the failure mode of the specimens, while the concrete strength grade and steel content had a significant effect on the failure mode. When the steel content was 2.84%, the specimens experienced shear failure, while when the steel content was 4.24%, they experienced waist drum failure. As the RSF content increased, the peak strain during the loading process of the specimens decreased, and the transverse deformation coefficient at the peak decreased. The addition of RSF significantly improved the ductility performance of the specimens. When the volume fraction of RSF was 2%, the bearing capacity of the specimens increased the most, reaching 13.4%, and the ductility coefficient gradually increased. The axial compressive bearing capacity and combined elastic modulus of the specimens increased with the increase in concrete strength grade, RSF content, and steel content. Full article
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<p>Recycled steel fiber (RSF) raw materials. (<b>a</b>) RSF formation process; (<b>b</b>) RSF appearance.</p>
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<p>Sample fabrication and maintenance. (<b>a</b>) Steel pipe fixation; (<b>b</b>) mixing of RSF reinforced concrete; (<b>c</b>) curing of specimens.</p>
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<p>Loading device and measurement point layout. (<b>a</b>) Placement of test specimens; (<b>b</b>) strain data acquisition instrument; (<b>c</b>) arrangement of strain gauges and displacement gauges.</p>
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<p>Failure morphology of C30-2 series specimens: (<b>a</b>) C30-2-0; (<b>b</b>) C30-2-0.5; (<b>c</b>) C30-2-1.0; (<b>d</b>) C30-2-1.5; (<b>e</b>) C30-2-2.0.</p>
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<p>Failure morphology of C30-3 series specimens: (<b>a</b>) C30-3-0; (<b>b</b>) C30-3-0.5; (<b>c</b>) C30-3-1.0; (<b>d</b>) C30-3-1.5; (<b>e</b>) C30-2-2.0.</p>
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<p>Failure morphology of C50-2 series specimens: (<b>a</b>) C50-2-1.0; (<b>b</b>) C50-2-2.0.</p>
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<p>Load deformation curves for (<b>a</b>) C30-2 series specimens, (<b>b</b>) C30-3 series specimens, and (<b>c</b>) C50-2 series specimens.</p>
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<p>Load strain curves of (<b>a</b>) C30-2 series specimens, (<b>b</b>) C30-3 series specimens, and (<b>c</b>) C50-2 series specimens.</p>
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<p>Lateral deformation coefficient. (<b>a</b>) RSF dosage remains unchanged; (<b>b</b>) RSF dosage changes.</p>
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<p>Ductility performance.</p>
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<p>Effect of different parameters on the combined elastic modulus of specimens: (<b>a</b>) C30; (<b>b</b>) C50.</p>
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<p>Influence of different parameters on the bearing capacity of specimens: (<b>a</b>) C30-2 series specimens and (<b>b</b>) C30-3 series specimens. (<b>c</b>) Influence of different wall thicknesses. (<b>d</b>) Influence of concrete strength.</p>
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14 pages, 3260 KiB  
Article
Discrete Element Modelling and Simulation Parameter Calibration for the Growing Media of Seedling Nursery Blocks
by Rangling Li, Wei Jiang, Wei Wang, Jiayu Fan, Yankun Gao and Hongying Wang
Agronomy 2024, 14(8), 1773; https://doi.org/10.3390/agronomy14081773 - 13 Aug 2024
Viewed by 188
Abstract
Using the discrete element method to simulate the interaction between growing media and machinery is an effective method to design seedling machinery and improve the precision of facility horticultural operations. In order to further improve the accuracy of the study on the interaction [...] Read more.
Using the discrete element method to simulate the interaction between growing media and machinery is an effective method to design seedling machinery and improve the precision of facility horticultural operations. In order to further improve the accuracy of the study on the interaction between seedling block-forming machines and growing media, the growing media used in the production of seedling nursery blocks was taken as the research object, and the Plackett–Burman screening test and Box–Behnken test were conducted based on the discrete element method using the EEPA model to conduct the calibration of discrete element parameters of the growing media. Optimization was conducted with an actual repose angle as the target value, and the optimal combination is as follows: the interparticle collision-recovery coefficient is 0.5066, the collision-recovery coefficient between particles and the geometric model is 0.714, the interparticle dynamic-friction coefficient is 0.381, and the tangential stiffness factor is 0.375. Finally, the soil uniaxial closed compression test was conducted with optimized calibration parameters. The relative error between the maximum axial load on the punch and the measured value in the simulation process was 4.23%, which verified the accuracy and reliability of parameter calibration of the growing media and provided support for the simulation of growing media and optimization of seedling nursery block-forming machine. Full article
(This article belongs to the Special Issue Advances in Data, Models, and Their Applications in Agriculture)
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<p>Experiment flow.</p>
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<p>Growing media-accumulation test.</p>
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<p>Test system for cold-compression molding. Note: 1 represents the positioning screw; 2 represents punch-pin; 3 represents cavity mold; 4 represents die cushion; 5 represents stripper; 6 represents control and display system.</p>
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<p>EEPA model normal contact force-normal overlap curve [<a href="#B27-agronomy-14-01773" class="html-bibr">27</a>]. Note: (1) <span class="html-italic">f</span><sub>n</sub>, <span class="html-italic">f</span><sub>0,</sub> and <span class="html-italic">f</span><sub>min</sub> are normal contact force, constant initial bonding strength, and maximum bonding force between particles, respectively; (2) <span class="html-italic">δ</span>, <span class="html-italic">δ</span><sub>p</sub>, <span class="html-italic">δ</span><sub>min,</sub> and <span class="html-italic">δ</span><sub>max</sub> are, respectively, normal overlap amount, plastic deformation amount, normal overlap amount, and maximum normal overlap amount between particles at maximum bonding force. (3) <span class="html-italic">K</span><sub>1</sub>, <span class="html-italic">K</span><sub>2,</sub> and <span class="html-italic">K</span><sub>adh</sub> are the loading branch stiffness, unloading branch stiffness, and bonding branch stiffness, respectively. (4) <span class="html-italic">n</span> and <span class="html-italic">X</span> are loading branch index and bonding branch index, respectively.</p>
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<p>Geometric model of simulation test. Note: (<b>a</b>) is the simplified model of repose angle measurement test; (<b>b</b>) is the uniaxial closed compression test model.</p>
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<p>Simulation test process of uniaxial closed compression. Note: (<b>a</b>–<b>f</b>) are the uniaxial closed compression simulation tests at 0, 0.01, 0.2, 0.21, 1.21, and 2 seconds, respectively.</p>
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<p>Comparison between measured and simulated repose angle. Note: (<b>a</b>) is the simulated repose angle for the growing media; (<b>b</b>) is the actual repose angle for the growing media.</p>
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<p>Comparison between measured and simulated maximum axial load.</p>
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19 pages, 6385 KiB  
Article
Osteoblastic Cell Sheet Engineering Using P(VCL-HEMA)-Based Thermosensitive Hydrogels Doped with pVCL@Icariin Nanoparticles Obtained with Supercritical CO2-SAS
by Rubén García-Sobrino, Isabel Casado-Losada, Carmen Caltagirone, Ana García-Crespo, Carolina García, Juan Rodríguez-Hernández, Helmut Reinecke, Alberto Gallardo, Carlos Elvira and Enrique Martínez-Campos
Pharmaceutics 2024, 16(8), 1063; https://doi.org/10.3390/pharmaceutics16081063 - 13 Aug 2024
Viewed by 203
Abstract
New clinical strategies for treating severe bone and cartilage injuries are required, especially for use in combination with implant procedures. For this purpose, p(VCL-co-HEMA) thermosensitive hydrogels have been activated with icariin-loaded nanoparticles to be used as bone-cell-harvesting platforms. Supercritical CO2-SAS technology [...] Read more.
New clinical strategies for treating severe bone and cartilage injuries are required, especially for use in combination with implant procedures. For this purpose, p(VCL-co-HEMA) thermosensitive hydrogels have been activated with icariin-loaded nanoparticles to be used as bone-cell-harvesting platforms. Supercritical CO2-SAS technology has been applied to encapsulate icariin, a small molecule that is involved in osteoblastic differentiation. Thus, physical-chemical analysis, including swelling and transmittance, showed the impact of HEMA groups in hydrogel composition. Moreover, icariin (ICA) release from p(VCL-co-HEMA) platforms, including pVCL@ICA nanoparticles, has been studied to evaluate their efficacy in relevant conditions. Finally, the thermosensitive hydrogels’ cell compatibility, transplant efficiency, and bone differentiation capacity were tested. This study identifies the optimal formulations for icariin-activated hydrogels for both control and HEMA formulations. Using this technique, osteoblastic sheets that were rich in collagen type I were successfully transplanted and recultivated, maintaining an optimal extracellular matrix (ECM) composition. These findings suggest a new cell-sheet-based therapy for bone regeneration purposes using customized and NP-activated pVCL-based cell platforms. Full article
(This article belongs to the Special Issue Supercritical Techniques for Pharmaceutical Applications)
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<p>Scheme of the proposal evaluated in this work.</p>
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<p>(<b>a</b>) Bright-field images of C2C12-GFP premyoblastic cell transplants 24 h after the cell transplant stage; (scale bar: 200 µm). (<b>b</b>) Metabolic activity (Alamar Blue) of cell transplants at 24 h. Significant differences were indicated as follows: * (<span class="html-italic">p</span> ≤ 0.05) and ** (<span class="html-italic">p</span> ≤ 0.01).</p>
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<p>SEM micrograph evaluation: temperature optimization process at 40 (<b>a</b>) and 35 °C (<b>b</b>). Pressure optimization process at 100 (<b>c</b>) and 200 bar (<b>d</b>). pVCL-ICA concentrations at 20 (<b>e</b>) and 30 mg/mL (<b>f</b>). ICA load at 5 (<b>g</b>) and 20 wt.% (<b>h</b>). The scale bar for (<b>a</b>) is 10 µm and (<b>b</b>–<b>h</b>) is 2 µm.</p>
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<p>(<b>a</b>) ICA release profile (mg) from the nanoparticles (NP5, 10, and 20) at 37 °C. (<b>b</b>) ICA release from the hydrogels (mg) at 37 °C, using PBS as a medium. (<b>c</b>) Release percentage of ICA from the hydrogels, also at 37 °C.</p>
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<p>MC3T3−E1 osteoblastic cell cultures, proliferating over hydrogels at day 14 (scale bar: 200 µm).</p>
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<p>(<b>a</b>) PicroSirius Red staining (collagen I) from MC3T3−E1 osteoblastic transplants from hydrogels at day 8 after transplant; (scale bar: 200 µm). (<b>b</b>) PicroSirius Red quantification. Significant differences were indicated as follows: ** (<span class="html-italic">p</span> ≤ 0.01).</p>
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15 pages, 12310 KiB  
Article
Structural Analysis of the Historical Sungurlu Clock Tower
by Ahmet Gökdemir and Zülküf Baki
Appl. Sci. 2024, 14(16), 7085; https://doi.org/10.3390/app14167085 - 12 Aug 2024
Viewed by 241
Abstract
Background: The strength of historical buildings built in different centuries with various materials and construction techniques and harboring many structural problems depends on the structural system, geometrical condition, and material properties. Sungurlu clock tower, whose system and geometry are in good condition, has [...] Read more.
Background: The strength of historical buildings built in different centuries with various materials and construction techniques and harboring many structural problems depends on the structural system, geometrical condition, and material properties. Sungurlu clock tower, whose system and geometry are in good condition, has been damaged under environmental and climatic effects, earthquakes, and other loads, and has survived to the present day by preserving its structural integrity to a great extent with the repairs it has undergone. Methods: In addition to static analysis, the robustness and durability of the design of the tower were tested by dynamic analysis with the SAP2000 program. In the model that will represent the actual system behavior of the tower, the lengths of the elements; nodal points; bearings; joints; shapes such as bars, shells, and plates; characteristic values of the materials to be used; as well as the system, element sections, and all loads and combinations of masses or dynamic forces acting on the system are defined. Results: In the reports presented visually, the moment, shear force, axial forces, and other forces to which the tower was exposed after the architectural and structural problems were eliminated were seen in a diagram. Since the effects of the damage could not be predicted, in this study, to measure the reaction of the building against earthquakes and other loads, the numerical model representing its original condition was prepared and analyzed according to the theoretical method and assumptions made by the restitution, survey, and static observation reports. Conclusions: With this program, which allows for the preparation of this model, it was concluded that the loads coming to the structure according to the principles of ductility, rigidity, and strength could be safely transferred to the ground without causing damage to the structural system and its elements. From the deformation, stress, velocity, acceleration, and reaction force graphs obtained, it was understood that the tower exhibited the expected structural behavior under its own weight and live loads. The stress and reaction force graphs showed that the structural materials are adequate for the resistance of the structure and system against the existing loads and possible earthquakes. Full article
(This article belongs to the Section Civil Engineering)
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<p>Sungurlu clock tower.</p>
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<p>Loss of parts when molding balconies.</p>
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<p>Original (<b>left</b>) and present (<b>right</b>) view of the balcony.</p>
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<p>Splits and fractures caused by physical impact.</p>
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<p>Superficial and deep wear.</p>
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<p>Flaking on the stone surface.</p>
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<p>Unqualified repairs to the stone surface.</p>
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<p>Surface soiling on the north facade (<b>left</b>) and interior (<b>right</b>).</p>
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<p>Joint discharge and unqualified joint repairs.</p>
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<p>Wooden elements in the structure.</p>
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<p>Original clock, eaves cladding, and metal door.</p>
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<p>Plans, sections, and views (units cm).</p>
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<p>Plans, sections, and views (units cm).</p>
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<p>Spectrum curves.</p>
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<p>Resulting translations on G+EQx (left/units mm) and G+EQy (right/units mm).</p>
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<p>Mode shapes and displacements.</p>
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<p>Normal (<b>left</b>/units Mpa), tensile (<b>middle</b>/units kPa), and compressive stresses (<b>right</b>/units kPa) (G+EQx).</p>
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<p>Normal (<b>left</b>/units Mpa), tensile (<b>middle</b>/units kPa), and compressive stresses (<b>right</b>/units kPa) (G+EQy).</p>
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<p>G+EQx and G+EQy shear stresses (units kPa).</p>
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<p>Tower, entrance, top, and bottom views.</p>
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26 pages, 8979 KiB  
Article
Predicting the Adhesive Layer Thickness in Hybrid Joints Involving Pre-Tensioned Bolts
by Frederico Ricca, Francisco J. Galindo-Rosales, Alireza Akhavan-Safar, Lucas F. M. da Silva, Thomas Fkyerat, Koichi Yokozeki, Till Vallée and Tobias Evers
Polymers 2024, 16(16), 2284; https://doi.org/10.3390/polym16162284 - 12 Aug 2024
Viewed by 330
Abstract
While most academic studies focus on the properties of cured joints, this research addresses the manufacturing process of hybrid joints in their uncured state. Hybrid joints that combine adhesive bonding with pre-tensioned bolts exhibit superior mechanical performance compared to exclusively bonded or bolted [...] Read more.
While most academic studies focus on the properties of cured joints, this research addresses the manufacturing process of hybrid joints in their uncured state. Hybrid joints that combine adhesive bonding with pre-tensioned bolts exhibit superior mechanical performance compared to exclusively bonded or bolted joints. However, the adhesive flow during manufacturing in hybrid joints often results in a nonuniform adhesive thickness, where obtaining an exact thickness is crucial for accurate load capacity predictions. This paper presents experiments involving three different adhesives, providing precise measurements of the adhesive layer thickness distribution, which served as a reference when evaluating and validating the subsequent numerical predictions. The numerical predictions were performed using computational fluid dynamics (CFD) to model the flow behavior of the adhesives during the bonding process and their interactions with the metal substrates. The CFD predictions of the adhesive layer thickness showed good agreement with the experimental data, with the relative differences between the average experimental and numerical thickness values ranging from 4.07% to 27.1%. The results were most accurate for the adhesive with sand particles, whose particles remained intact, ensuring that the adhesive’s rheology remained unchanged. The results highlight the importance of the rheological behavior of the adhesive in the final distribution of the adhesive layer thickness, thereby expanding the understanding of these joints. Full article
(This article belongs to the Section Polymer Physics and Theory)
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<p>Geometry of the hybrid joints (<b>left</b>) and measurement points for the adhesive thickness, distributed uniformly on a 5 mm grid over the connection plates, C1 and C2 (<b>right</b>).</p>
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<p>Adhesive layer thickness measurements aided by a robotic handling system programmed for point-to-point movements. (<b>a</b>) The robot programmed for thickness measurements; (<b>b</b>) a close-up of the thickness measurements.</p>
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<p>FSI numerical models.</p>
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<p>Numerical steps involved in the FSI simulations.</p>
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<p>Rheological characterization and Carreau fit.</p>
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<p>Results of the adhesive layer thickness measurements.</p>
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<p>Numerical results obtained for S370 adhesive with a moving wall velocity of 1 mm/s.</p>
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<p>S370 deflection curves.</p>
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<p>DP490 normal force produced for different velocities.</p>
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<p>Numerical results obtained for DP490 adhesive with a moving wall velocity of 1 mm/s.</p>
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<p>Numerical results obtained for DP490 adhesive with a moving wall velocity of 0.2 mm/s.</p>
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<p>Numerical results obtained for DP490 adhesive with a moving wall velocity of 0.067 mm/s.</p>
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<p>Numerical results obtained for SW7240 adhesive with a moving wall velocity of 1 mm/s.</p>
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<p>Numerical results obtained for SW7240 adhesive with a moving wall velocity of 0.2 mm/s.</p>
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<p>SW7240 deflection curves.</p>
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<p>Distribution of adhesive thickness in hybrid joints: a comparison between the numerical and experimental results. The small square in the top left of each image shows the numerical results, while the larger square with the bolting hole presents the experimental data.</p>
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<p>Distribution of adhesive thickness in hybrid joints: a comparison between the numerical and experimental results. The small square in the top left of each image shows the numerical results, while the larger square with the bolting hole presents the experimental data.</p>
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<p>Adhesive squeeze flow before (<b>a</b>) and after (<b>b</b>) the squeezing process.</p>
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<p>Squeeze flow axisymmetric model.</p>
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<p>Validation of the numerical model by a comparison with numerical and experimental results from the literature [<a href="#B50-polymers-16-02284" class="html-bibr">50</a>].</p>
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<p>Squeeze flow axisymmetric model mesh before (<b>top</b>) and after (<b>bottom</b>) its compression.</p>
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19 pages, 5357 KiB  
Article
Follicular Immune Landscaping Reveals a Distinct Profile of FOXP3hiCD4hi T Cells in Treated Compared to Untreated HIV
by Spiros Georgakis, Michail Orfanakis, Cloe Brenna, Simon Burgermeister, Perla M. Del Rio Estrada, Mauricio González-Navarro, Fernanda Torres-Ruiz, Gustavo Reyes-Terán, Santiago Avila-Rios, Yara Andrea Luna-Villalobos, Oliver Y. Chén, Giuseppe Pantaleo, Richard A. Koup and Constantinos Petrovas
Vaccines 2024, 12(8), 912; https://doi.org/10.3390/vaccines12080912 - 12 Aug 2024
Viewed by 252
Abstract
Follicular helper CD4hi T cells (TFH) are a major cellular pool for the maintenance of the HIV reservoir. Therefore, the delineation of the follicular (F)/germinal center (GC) immune landscape will significantly advance our understanding of HIV pathogenesis. We have applied [...] Read more.
Follicular helper CD4hi T cells (TFH) are a major cellular pool for the maintenance of the HIV reservoir. Therefore, the delineation of the follicular (F)/germinal center (GC) immune landscape will significantly advance our understanding of HIV pathogenesis. We have applied multiplex confocal imaging, in combination with the relevant computational tools, to investigate F/GC in situ immune dynamics in viremic (vir-HIV), antiretroviral-treated (cART HIV) People Living With HIV (PLWH) and compare them to reactive, non-infected controls. Lymph nodes (LNs) from viremic and cART PLWH could be further grouped based on their TFH cell densities in high-TFH and low-TFH subgroups. These subgroups were also characterized by different in situ distributions of PD1hi TFH cells. Furthermore, a significant accumulation of follicular FOXP3hiCD4hi T cells, which were characterized by a low scattering in situ distribution profile and strongly correlated with the cell density of CD8hi T cells, was found in the cART-HIV low-TFH group. An inverse correlation between plasma viral load and LN GrzBhiCD8hi T and CD16hiCD15lo cells was found. Our data reveal the complex GC immune landscaping in HIV infection and suggest that follicular FOXP3hiCD4hi T cells could be negative regulators of TFH cell prevalence in cART-HIV. Full article
(This article belongs to the Special Issue Antiviral T and B Cell Immunity)
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<p>Similar in situ cell density of T<sub>FH</sub> cells in viremic and cART PLWH LNs. (<b>A</b>) Representative examples of CD20 (red), CD4 (green), DAPI (blue), CD57 (gray) and PD1 (cyan) staining pattern from tonsil and control viremic and cART HIV LNs (scale bar: 30 μm). (<b>B</b>) The Histo-cytometry gating scheme used for the identification of T<sub>FH</sub> cell subsets based on their expression of PD1 and CD57 is shown. F and EF areas were manually identified based on the density of the CD20 signal, and the relevant cell counts were extracted for specific tissue localities. (<b>C</b>) Histo-cytometry-identified PD1<sup>hi</sup>CD57<sup>hi</sup> T<sub>FH</sub> cells were backgated to the original image using Imaris software. Each sphere represents one cell. (<b>D</b>) Bar graph (left) demonstrating the cell density (normalized per mm<sup>2</sup> counts) of follicular PD1<sup>hi</sup> T<sub>FH</sub> cells in tonsils (N = 5), control LNs (N = 5), viremic HIV (N = 12) and cART HIV LNs (N = 20). Viremic and cART were further subdivided based on their T<sub>FH</sub> counts (HIV vir-high T<sub>FH</sub> (N = 6), HIV vir-low-T<sub>FH</sub> (N = 6), HIV cART-high-T<sub>FH</sub> (N = 6) and HIV cART-low-T<sub>FH</sub> (N = 14)). Each symbol represents one donor. The <span class="html-italic">p</span> values were calculated using the Mann–Whitney test and were corrected using FDR correction with q = 0.05 (<a href="#app1-vaccines-12-00912" class="html-app">Supplementary Table S2</a>). Dot graph (right) shows the distribution of PD1<sup>hi</sup> cell densities among the samples. Each symbol represents a follicle. (<b>E</b>) Bar graph demonstrating the normalized per mm<sup>2</sup> numbers of follicular PD1<sup>hi</sup>CD57<sup>hi</sup> T<sub>FH</sub> cells in the same groups/subgroups of the samples. (<b>F</b>) Linear regression analysis to address the correlation between PD1<sup>hi</sup> and PD1<sup>hi</sup>CD57<sup>hi</sup> absolute follicular counts between different subgroups. Each symbol represents a follicle. R-squared and <span class="html-italic">p</span>-values are displayed on graphs. (<b>G</b>) Linear regression analysis (lower) to address the correlation between normalized follicular PD1<sup>hi</sup> counts and blood viral load—pVL. Dot graph (upper) showing the pVL differences (as a log scale) between the viremic HIV subgroups.</p>
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<p>A highly scattered distribution of T<sub>FH</sub> cells in HIV-infected compared to non-infected lymphoid tissues. (<b>A</b>) Representative immunofluorescence images showing the distribution of CD20<sup>hi</sup> (blue) and PD1<sup>hi</sup> (red) cells in follicles from one control, one vir-HIV high-T<sub>FH</sub> and one cART HIV high-T<sub>FH</sub> tissue (scale bar: 30 μm). The corresponding digitalized (generated by the Python distance analysis script) images are shown too. (<b>B</b>) The distribution bar graphs for the minimum distance between CD20<sup>hi</sup> and PD1<sup>hi</sup> cells in two follicular areas (control—left, cART—right) (upper panel). Diagrams showing the theoretical (blue) and experimental (red) Poisson curves for the distribution of PD1<sup>hi</sup> cells in two follicular areas (control—left, cART—right) (lower panel). Bar graphs showing the G function analysis for total CD20<sup>hi</sup> (<b>C</b>) and PD1<sup>hi</sup> T<sub>FH</sub> cells (<b>D</b>) in individual follicles from control and infected LNs (control LNs (N = 54), vir-HIV high-T<sub>FH</sub> (N = 42), vir-HIV low-T<sub>FH</sub> (N = 51), cART HIV high-T<sub>FH</sub> (N = 31) and cART HIV low-T<sub>FH</sub> (N = 55)). (<b>E</b>) The mean of minimum distance values between CD20<sup>hi</sup> and PD1<sup>hi</sup> cells in individual follicles from control and infected LNs is shown (control LNs (N = 54), vir-HIV high-T<sub>FH</sub> (N = 42), vir-HIV low-T<sub>FH</sub> (N = 51), cART HIV high-T<sub>FH</sub> (N = 31) and cART HIV low-T<sub>FH</sub> (N = 55)). (<b>F</b>) The G function analysis for PD1<sup>hi</sup>CD57<sup>hi</sup> (left) and PD1<sup>hi</sup>CD57<sup>lo</sup> (right) T<sub>FH</sub> cells in tonsils, control and infected LNs is shown (tonsils (N = 29), control LNs (N = 29), vir-HIV high-T<sub>FH</sub> (N = 13), vir-HIV low-T<sub>FH</sub> (N = 5), cART HIV high-T<sub>FH</sub> (N = 11) and cART HIV low-T<sub>FH</sub> (N = 7)). (<b>G</b>) Bar graphs showing the mean of minimum distances between CD20<sup>hi</sup> and PD1<sup>hi</sup>CD57<sup>lo</sup> (left) or PD1<sup>hi</sup>CD57<sup>hi</sup> (right) T<sub>FH</sub> cells in tonsils, control and infected LNs (tonsils (N = 29), control LNs (N = 29), vir-HIV high-T<sub>FH</sub> (N = 13), vir-HIV low-T<sub>FH</sub> (N = 5), cART HIV high-T<sub>FH</sub> (N = 11) and cART HIV low-T<sub>FH</sub> (N = 7)). Each dot represents one follicle in all presented graphs. The <span class="html-italic">p</span> values were calculated using the Mann–Whitney test and were corrected using FDR correction with q = 0.05 (<a href="#app1-vaccines-12-00912" class="html-app">Supplementary Table S2</a>).</p>
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<p>Preferential accumulation of FOXP3<sup>hi</sup>CD4<sup>hi</sup> T cells in cART HIV follicular areas. (<b>A</b>) Representative examples of CD20 (red), CD4 (green), DAPI (blue) and FOXP3 (yellow) staining pattern from tonsil and control viremic and cART HIV LNs (scale bar: 30 μm). (<b>B</b>) Histo-cytometry immunophenotyping gating strategy used for the sequential identification of FOXP3<sup>hi</sup>CD4<sup>hi</sup> T cells in follicular and extrafollicular areas in a control LN (<b>C</b>) Representative backgating of Histo-cytometry-identified FOXP3<sup>hi</sup>CD4<sup>hi</sup> T cells, using Imaris software. Each sphere represents one cell. (<b>D</b>) Bar graph (left) demonstrating the normalised per mm<sup>2</sup> counts of extrafollicular FOXP3<sup>hi</sup>CD4<sup>hi</sup> T cells in tonsils (N = 5), control LNs (N = 5), HIV vir-high-T<sub>FH</sub> (N = 6), HIV vir-low-T<sub>FH</sub> (N = 6), HIV cART-high-T<sub>FH</sub> (N = 6) and HIV cART-low-T<sub>FH</sub> (N = 14). (<b>E</b>) Bar graph showing the normalised per mm<sup>2</sup> counts of follicular FOXP3<sup>hi</sup>CD4<sup>hi</sup> T cells in the same tissue samples. Each symbol represents one donor. (<b>F</b>) Bar graph with connecting lines demonstrating the normalized per mm<sup>2</sup> counts of FOXP3<sup>hi</sup>CD4<sup>hi</sup> T cells in follicular and extrafollicular regions in each tissue analyzed. (<b>G</b>) Representative immunofluorescence images showing the distribution of FOXP3<sup>hi</sup> (green) and CD20<sup>hi</sup> (red) cells in follicles from one cART-high-T<sub>FH</sub> and one cART-low-T<sub>FH</sub> tissue. The corresponding digitalized (generated by the Python distance analysis script) images are shown too. (<b>H</b>) Bar graph showing the calculated G function values for follicular FOXP3<sup>hi</sup>CD4<sup>hi</sup> T cells in cART-high T<sub>FH</sub> (blue circles) and cART-low T<sub>FH</sub> (blue triangles) tissues (HIV cART-high T<sub>FH</sub> (N = 22) and HIV cART-low T<sub>FH</sub> (N = 64)). The <span class="html-italic">p</span> values were calculated using the Mann–Whitney test and were corrected using FDR correction with q = 0.05 (<a href="#app1-vaccines-12-00912" class="html-app">Supplementary Table S2</a>).</p>
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<p>Accumulated LN GrzB<sup>hi</sup>CD8<sup>hi</sup> T cells are negatively associated with PLWH blood viral load. (<b>A</b>) Representative examples of GRZb (red), CD8 (green) and DAPI (blue) staining pattern from tonsil and control vir HIV and cART HIV LNs (scale bar: 30 μm). (<b>B</b>) Histo-cytometry gating strategy used for the sequential identification of bulk CD8<sup>hi</sup> and GrzB<sup>hi</sup>CD8<sup>hi</sup> T cells. (<b>C</b>) Representative backgating of Histo-cytometry-identified GrzB<sup>hi</sup>CD8<sup>hi</sup> cells using Imaris software. Each sphere represents one cell. (<b>D</b>) Bar graph (upper) demonstrating the normalized per mm<sup>2</sup> counts of bulk CD8<sup>hi</sup> cells in tonsils (N = 5), control LNs (N = 5), vir-HIV high-T<sub>FH</sub> (N = 6), vir-HIV low-T<sub>FH</sub> (N = 6), cART HIV high-T<sub>FH</sub> (N = 6) and cART HIV low-T<sub>FH</sub> (N = 14). Bar graph (lower) showing the normalized per mm<sup>2</sup> counts of GrzB<sup>hi</sup>CD8<sup>hi</sup> T cells in the same tissue samples. Each symbol represents one donor. (<b>E</b>) Linear regression analysis showing the correlation between LN CD8<sup>hi</sup> (upper panel) and LN GrzB<sup>hi</sup>CD8<sup>hi</sup> (lower panel) normalized T cell counts with blood viral load. (<b>F</b>) Linear regression analysis showing the correlation between LN CD8<sup>hi</sup> and LN GrzB<sup>hi</sup>CD8<sup>hi</sup> T cell counts in cART HIV high-T<sub>FH</sub> (upper panel) and cART HIV low-T<sub>FH</sub> (lower panel). (<b>G</b>) Linear regression analysis to address the correlation between extrafollicular (upper panel) and follicular (lower panel) FOXP3<sup>hi</sup>CD4<sup>hi</sup> and LN CD8<sup>hi</sup> normalized T cell counts in cART HIV low-T<sub>FH</sub> subgroup. (<b>H</b>) Bar graph showing the G function values for LN GrzB<sup>hi</sup>CD8<sup>hi</sup> T cells in the HIV subgroups (vir-HIV high-T<sub>FH</sub> (N = 6), vir-HIV low-T<sub>FH</sub> (N = 5), cART HIV high-T<sub>FH</sub> (N = 6) and cART HIV low-T<sub>FH</sub> (N = 10)). The <span class="html-italic">p</span> values were calculated using the Mann–Whitney test and corrected using FDR correction with q = 0.05 (<a href="#app1-vaccines-12-00912" class="html-app">Supplementary Table S2</a>).</p>
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<p>Altered innate immunity signatures among control and HIV samples. (<b>A</b>) Representative examples of DAPI (blue), CD15 (cyan), CD16 (magenta), CD163 (yellow) and CD68 (red) staining pattern from tonsil and control vir-HIV and cART-HIV LNs (scale bar: 30 μm). (<b>B</b>) Histo-cytometry gating strategy used for the sequential identification of CD15<sup>hi</sup>, CD16<sup>hi</sup>, CD163<sup>hi</sup> and CD68<sup>hi</sup> innate cells. (<b>C</b>) Bar graphs demonstrating the normalized per mm<sup>2</sup> counts of bulk CD68<sup>hi</sup>CD163<sup>lo</sup> (upper left), CD68<sup>lo</sup>CD163<sup>hi</sup> (upper middle), CD68<sup>hi</sup>CD163<sup>hi</sup> (upper right), CD15<sup>hi</sup>CD16<sup>lo</sup> (lower left), CD15<sup>hi</sup>CD16<sup>lo</sup> (lower middle) and CD15<sup>hi</sup>CD16<sup>lo</sup> (lower right) in tonsils (N = 5), control LNs (N = 5), vir-HIV high-T<sub>FH</sub> (N = 6), vir-HIV low-T<sub>FH</sub> (N = 6), cART HIV high-T<sub>FH</sub> (N = 6) and cART HIV low-T<sub>FH</sub> (N = 14). Each symbol represents one donor. (<b>D</b>) Linear regression analysis to address the correlation between CD8<sup>hi</sup> (upper panel) or GrzB<sup>hi</sup>CD8<sup>hi</sup> (lower panel) T cells and CD16<sup>hi</sup>CD15<sup>lo</sup> cells in vir-HIV high-T<sub>FH</sub> tissues. (<b>E</b>) Bar graph showing the mean values of the minimum distances between GrzB<sup>hi</sup>CD8<sup>hi</sup> T and CD16<sup>hi</sup> cells in tissues from the HIV subgroups vir-HIV high-T<sub>FH</sub> (N = 6), vir-HIV low-T<sub>FH</sub> (N = 3), cART HIV high-T<sub>FH</sub> (N = 4) and cART HIV low-T<sub>FH</sub> (N = 10). Each dot represents a different donor. The <span class="html-italic">p</span> values were calculated using the Mann–Whitney test, and <span class="html-italic">p</span> values were corrected using FDR correction with q = 0.05 (<a href="#app1-vaccines-12-00912" class="html-app">Supplementary Table S2</a>).</p>
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22 pages, 44198 KiB  
Article
Real-Time Simulation of Tube Hydroforming by Integrating Finite-Element Method and Machine Learning
by Liang Cheng, Haijing Guo, Lingyan Sun, Chao Yang, Feng Sun and Jinshan Li
J. Manuf. Mater. Process. 2024, 8(4), 175; https://doi.org/10.3390/jmmp8040175 - 12 Aug 2024
Viewed by 289
Abstract
The real-time, full-field simulation of the tube hydroforming process is crucial for deformation monitoring and the timely prediction of defects. However, this is rather difficult for finite-element simulation due to its time-consuming nature. To overcome this drawback, in this paper, a surrogate model [...] Read more.
The real-time, full-field simulation of the tube hydroforming process is crucial for deformation monitoring and the timely prediction of defects. However, this is rather difficult for finite-element simulation due to its time-consuming nature. To overcome this drawback, in this paper, a surrogate model framework was proposed by integrating the finite-element method (FEM) and machine learning (ML), in which the basic methodology involved interrupting the computational workflow of the FEM and reassembling it with ML. Specifically, the displacement field, as the primary unknown quantity to be solved using the FEM, was mapped onto the displacement boundary conditions of the tube component with ML. To this end, the titanium tube material as well as the hydroforming process was investigated, and a fairly accurate FEM model was developed based on the CPB06 yield criterion coupled with a simplified Kim–Tuan hardening model. Numerous FEM simulations were performed by varying the loading conditions to generate the training database for ML. Then, a random forest algorithm was applied and trained to develop the surrogate model, in which the grid search method was employed to obtain the optimal combination of the hyperparameters. Sequentially, the principal strain, the effective strain/stress, as well as the wall thickness was derived according to continuum mechanics theories. Although further improvements were required in certain aspects, the developed FEM-ML surrogate model delivered extraordinary accuracy and instantaneity in reproducing multi-physical fields, especially the displacement field and wall-thickness distribution, manifesting its feasibility in the real-time, full-field simulation and monitoring of deformation states. Full article
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Figure 1

Figure 1
<p>(<b>a</b>) Schematic description of the tubular blank and the dimension. ND, RD, and TD denote normal direction, rolling direction, and tangent direction, respectively. (<b>b</b>) Inverse-pole-figure map of the tube material. (<b>c</b>) Histogram of grain size distribution. (<b>d</b>) Pole figures to show the texture components of the material.</p>
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<p>(<b>a</b>) Stress–strain curves obtained from four repeated tensile tests along RD. The tensile curve quoted from [<a href="#B45-jmmp-08-00175" class="html-bibr">45</a>] is also superimposed for comparison. (<b>b</b>) DIC images showing the evolution of the axial strain distribution during tension. (<b>c</b>) Lankford coefficient curves derived from the DIC results.</p>
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<p>(<b>a</b>) Schematic representations of the tube hydroforming processes. (<b>b</b>) Typical loading curves for tube hydroforming.</p>
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<p>Simplified finite-element model of the hydroforming process.</p>
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<p>True stress–true strain curve (symbol) obtained by uniaxial tension in RD and the fitting/extrapolation results (solid line) by the simplified K-T model.</p>
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<p>(<b>a</b>) Comparison between the simulated results (symbol) and the experimental results (solid line). (<b>b</b>) Evolution of the axial-to-width strain ratio of the tensile specimen using FEM simulation (bold lines) and tensile tests (thin lines).</p>
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<p>Comparison of the (<b>a</b>) total branch heights and (<b>b</b>) wall thicknesses of the tube ends between the prediction and the measured results.</p>
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<p>(<b>a</b>) A shell element ABCD with a rectangular shape prior to deformation. (<b>b</b>) The shape of the element after arbitrary deformation. <span class="html-italic">a</span><sub>0</sub> and <span class="html-italic">b</span><sub>0</sub> denote the edge lengths prior to deformation while <span class="html-italic">a</span> and <span class="html-italic">b</span> are those after deformation.</p>
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<p>(<b>a</b>) The pressurization curves of the two hydroforming simulation runs. (<b>b</b>) Predicted displacement curves of the boundaries for the two virtual tests.</p>
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<p>Comparison of the displacement field between (<b>a</b>–<b>c</b>) sample 35-6.7 and (<b>e</b>,<b>f</b>) sample 65-2.1 under the same boundary condition shown in <a href="#jmmp-08-00175-f009" class="html-fig">Figure 9</a>. (<b>a</b>,<b>d</b>), (<b>b</b>,<b>e</b>), and (<b>c</b>,<b>f</b>) are displacement distributions in the x, y, and z direction.</p>
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<p>Structure and workflow of the proposed surrogate model.</p>
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<p>The parameter grid showing various pressurization curves used for simulation. The insert graphs depict forming defects at different forming conditions.</p>
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<p>Pressurization curve used for the extra-simulation test.</p>
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<p>Comparison of multi-physical field between those predicted by surrogate model (dot) and the FEM model (solid) at different forming times. The loading condition is depicted in <a href="#jmmp-08-00175-f013" class="html-fig">Figure 13</a>.</p>
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<p>Quantitative comparison of the predicted field variables between ML model and offline FEM model for all nodes/elements at various forming times: (<b>a</b>) displacement of nodes; (<b>b</b>) effective strain of elements; (<b>c</b>) effective stress of elements; (<b>d</b>) wall thickness. The mean error is also tabulated in the figure, where <span class="html-italic">y</span><sub>ML</sub> and <span class="html-italic">y</span><sub>FE</sub> are the predicted results by ML model and FEM model, respectively.</p>
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