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Keywords = CuO-based nanoparticles

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13 pages, 3527 KiB  
Article
Cross-Linked Poly(methyl methacrylate) Nanocomposites’ Synthesis, Characterization, and Antibacterial Effects
by Nazeeha S. Alkayal and Mashail A. Al Ghamdi
Polymers 2025, 17(3), 269; https://doi.org/10.3390/polym17030269 - 21 Jan 2025
Viewed by 373
Abstract
Polymer networks were synthesized using the condensation method between PMMA and melamine as cross-linkers. CuO nanoparticles (NPs) and activated carbon (AC) were used as a filler. The final products PMMA/Mel, PMMA/Mel-CuO, and PMMA/Mel-AC were tested for antibacterial activities against E.coli and S. aureus. [...] Read more.
Polymer networks were synthesized using the condensation method between PMMA and melamine as cross-linkers. CuO nanoparticles (NPs) and activated carbon (AC) were used as a filler. The final products PMMA/Mel, PMMA/Mel-CuO, and PMMA/Mel-AC were tested for antibacterial activities against E.coli and S. aureus. The chemical structure and composition, thermal properties, and surface morphology of the new PMMA/Mel-based nanocomposites were investigated by various techniques. The XRD and EDX results showed the successful incorporation of CuO NPs and AC into the polymer matrix. Also, the thermal stability of the PMMA/Mel polymer was significantly enhanced after adding CuO nanoparticles. This finding showed that the PMMA/Mel-CuO and PMMA/Mel-AC nanocomposites have greater activity against both bacteria than PMMA/Mel. The PMMA/Mel-CuO and PMMA/Mel-AC polymers showed high activity against S. aureus bacteria, with inhibition zones of 22.6 mm and 11.3 mm, respectively. This confirms that small-sized nanoparticles have an effective role in killing bacterial cells. Full article
(This article belongs to the Special Issue Advances in Biocompatible and Biodegradable Polymers, 4th Edition)
Show Figures

Figure 1

Figure 1
<p>Preparation of PMMA/Mel-CuO and PMMA/Mel-AC nanocomposites.</p>
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<p>(<b>a</b>) FTIR spectra and (<b>b</b>) XRD patterns of PMMA/Mel, PMMA/Mel-CuO, and PMMA/Mel-AC.</p>
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<p>TGA micrograph of cross-linked PMMA/Mel, PMMA/Mel-CuO, and PMMA/Mel-AC.</p>
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<p>SEM micrographs for (<b>a</b>,<b>b</b>) PMMA/Mel-AC and (<b>c</b>,<b>d</b>) PMMA/Mel-CuO, and EDX elemental mapping of (<b>e</b>) PMMA/Mel-AC and (<b>f</b>) PMMA/Mel-CuO.</p>
Full article ">Figure 5
<p>The antibacterial activity of cross-linked PMMA/Mel, PMMA/Mel-CuO, and PMMA/Mel-AC against (<b>A</b>) <span class="html-italic">S. aureus</span> and (<b>B</b>) <span class="html-italic">E. coli</span>.</p>
Full article ">Figure 6
<p>(<b>A</b>,<b>B</b>) Antibiotic resistance profiles of <span class="html-italic">E. coli</span> pathogenic bacteria. Antibiotic compounds used; CPM: Cefepime, GM: Gentamycin, C: Chloramphenicol, OX: Oxacillin, VA: Vancomycin, FC: Fusidic Acid, TS: Trimethoprim/Sulfamethoxazole, CD: Clindamycin, KF: Cephalothin, AP: Amoxicillin/Clavulanic Acid, E: Erythromycin, PG: Penicillin.</p>
Full article ">Figure 7
<p>(<b>A</b>–<b>D</b>): The antibacterial activity of cross-linked (<b>B</b>) PMMA/Mel, (<b>C</b>) PMMA/Mel-CuO, and (<b>D</b>) PMMA/Mel-AC against <span class="html-italic">E. coli</span> according to the colony-forming units compared to (<b>A</b>) <span class="html-italic">E.coli</span> as a control.</p>
Full article ">
25 pages, 7568 KiB  
Article
Green Synthesis of Antibacterial CuO Nanoparticles Based on the Synergy Between Cornu aspersum Snail Mucus and Ascorbic Acid
by Maria Todorova, Angelina Kosateva, Ventsislava Petrova, Bogdan Ranguelov, Stela Atanasova-Vladimirova, Georgi Avdeev, Ivanka Stoycheva, Emiliya Pisareva, Anna Tomova, Lyudmila Velkova, Aleksandar Dolashki and Pavlina Dolashka
Molecules 2025, 30(2), 291; https://doi.org/10.3390/molecules30020291 - 13 Jan 2025
Viewed by 332
Abstract
Many biologically active compounds have been identified in the mucus of the garden snail Cornu aspersum, which are effective in the treatment of several diseases such as cancer, ulcers, wounds, etc. The incorporation of these compounds into the green synthesis of copper [...] Read more.
Many biologically active compounds have been identified in the mucus of the garden snail Cornu aspersum, which are effective in the treatment of several diseases such as cancer, ulcers, wounds, etc. The incorporation of these compounds into the green synthesis of copper nanoparticles (CuONPs-Muc) was demonstrated in our previous study. Based on the synergistic effect of two reducing agents—C. aspersum snail mucus and ascorbic acid (AsA)—on CuSO4.5H2O, which also act as stabilizers of the resulting compound, a new method for the “green” synthesis of CuONPs-Muc is presented. Using two reducing agents has several advantages, such as forming spherical nanoparticles with a diameter of about 150 nm and reducing the formation time of CuONPs-Muc to 3 h. Analyses by ultraviolet–visible spectroscopy (UV-Vis) and Fourier transform infrared spectroscopy (FT-IR) show the formation of CuONPs-Muc, composed of a mixture of copper and copper oxide. This was confirmed by scanning electron microscopy combined with energy-dispersive spectroscopy (SEM/EDS) and X-ray diffraction (XRD). Another important advantage of CuONPs obtained by the new method with two reducing agents is the stronger inhibitory effect on the bacterial growth of some Gram-positive and Gram-negative bacterial strains, compared to CuONPs-Muc prepared with only one reducing agent, i.e., a fraction of mucus with an MW > 20 kDa. Full article
(This article belongs to the Special Issue Discovery of Antibacterial Drugs)
Show Figures

Figure 1

Figure 1
<p>UV-Vis spectra of synthesized CuONPs after 4 h of incubation of the fraction with an MW &gt; 20 kDa from snail <span class="html-italic">C. aspersum</span> and different concentrations of ascorbic acid, centrifuged at 4000 rpm: (1) CuONPs-Muc, (2) CuONPs-Muc 0.5 M AsA, and (3) CuONPs-Muc 1.0 M AsA.</p>
Full article ">Figure 2
<p>UV emission spectra of the mucus fraction with an MW &gt; 20 kDa from snail <span class="html-italic">C. aspersum</span> (A280 = 0.1 in 0.1 M Tris buffer, pH 8) and synthesized CuONPs at different concentrations of ascorbic acid: the fraction with an MW &gt; 20 kDa (blue line); CuONPs-Muc (red line); CuONPs-Muc 0.5 M AsA (purple line), and CuONPs-Muc 1.0 M AsA (brown line). Emission spectra were recorded with a 1 cm quartz cuvette after excitation at 295 nm.</p>
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<p>SEM images of CuONPs-Muc with reducing agent: (<b>A</b>) mucus fraction with MW &gt; 20 kDa; (<b>B</b>) two reducing compounds—mucus fraction with MW &gt; 20 kDa and 0.5 M AsA; (<b>C</b>) mucus fraction with MW &gt; 20 kDa and 1.0 M AsA; and (<b>D</b>) 1.0 M AsA only.</p>
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<p>Histograms of particle size distribution of CuONPs-Muc obtained with protein mucus fraction with MW &gt; 20 kDa as a reducing agent (<b>A</b>) and the same fraction in the presence of 1.0 M AsA (<b>B</b>).</p>
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<p>EDS analyses of CuONPs-Muc synthesized using two reducing agents of Cu<sup>2+</sup> ions: (<b>A</b>) mucus fraction with MW &gt; 20 kDa and 0.5 M AsA; (<b>B</b>) same protein fraction and 1.0 M AsA.</p>
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<p>XRD analysis of CuONPs-MucAsA, obtained in the presence of (<b>A</b>) 0.5 M AsA and (<b>B</b>) 1.0 M AsA.</p>
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<p>Infrared spectra of the mucus fraction with an MW &gt; 20 kDa (black) and the formed CuONPs-Muc (blue line); CuONPs-Muc 0.5 M AsA (green); and CuONPs-Muc 1.0M AsA (red).</p>
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<p>TG–DSC characteristics of pure mucus fraction with MW &gt; 20 kDa (olive-green line); CuONPs-Muc (red line); CuONPs-Muc 0.5 M AsA (blue; line); and CuONPs-Muc 1.0 M AsA (green line). TG (dotted line) and DSC (solid line).</p>
Full article ">Figure 9
<p>Antimicrobial effect against Gram<sup>+</sup> bacterial strains of (<b>A</b>) mucus fraction from <span class="html-italic">C. aspersum</span> with MW &gt; 20 kDa; (<b>B</b>) CuONPs synthesized with reducing agents (1) CuONPs-Muc, (2) CuONPs-Muc 0.5 M AsA, and (3) CuONPs-Muc 1.0 M AsA; (<b>C</b>) Comparative analysis between inhibitory zones of mucus fraction from <span class="html-italic">C. aspersum</span> with MW &gt; 20 kDa and different synthesized CuONPs against Gram<sup>+</sup> test bacterial strains.</p>
Full article ">Figure 10
<p>Antimicrobial effect against Gram<sup>−</sup> bacterial strains of (<b>A</b>) mucus fraction from <span class="html-italic">C. aspersum</span> with MW &gt; 20 KDa; (<b>B</b>) CuONPs synthesized with reducing agents (1) CuONPs-Muc, (2) CuONPs-Muc 0.5 M AsA, and (3) CuONPs-Muc 1.0 M AsA; (<b>C</b>) Comparative analysis between inhibitory zones of pure mucus of <span class="html-italic">C. aspersum</span> and different synthesized CuONPs against Gram<sup>−</sup> test bacterial strains.</p>
Full article ">Figure 10 Cont.
<p>Antimicrobial effect against Gram<sup>−</sup> bacterial strains of (<b>A</b>) mucus fraction from <span class="html-italic">C. aspersum</span> with MW &gt; 20 KDa; (<b>B</b>) CuONPs synthesized with reducing agents (1) CuONPs-Muc, (2) CuONPs-Muc 0.5 M AsA, and (3) CuONPs-Muc 1.0 M AsA; (<b>C</b>) Comparative analysis between inhibitory zones of pure mucus of <span class="html-italic">C. aspersum</span> and different synthesized CuONPs against Gram<sup>−</sup> test bacterial strains.</p>
Full article ">
45 pages, 9048 KiB  
Article
Artificial Neural Network and Response Surface Methodology-Driven Optimization of Cu–Al2O3/Water Hybrid Nanofluid Flow in a Wavy Enclosure with Inclined Periodic Magnetohydrodynamic Effects
by Tarikul Islam, Sílvio Gama and Marco Martins Afonso
Mathematics 2025, 13(1), 78; https://doi.org/10.3390/math13010078 - 28 Dec 2024
Viewed by 854
Abstract
This study explores the optimization of a Cu–Al2O3/water hybrid nanofluid within an irregular wavy enclosure under inclined periodic MHD effects. Hybrid nanofluids, with different mixture ratios of copper (Cu) and alumina (Al2O3) nanoparticles in water, [...] Read more.
This study explores the optimization of a Cu–Al2O3/water hybrid nanofluid within an irregular wavy enclosure under inclined periodic MHD effects. Hybrid nanofluids, with different mixture ratios of copper (Cu) and alumina (Al2O3) nanoparticles in water, are used in this study. Numerical simulations using the Galerkin residual-based finite-element method (FEM) are conducted to solve the governing PDEs. At the same time, artificial neural networks (ANNs) and response surface methodology (RSM) are employed to optimize thermal performance by maximizing the average Nusselt number (Nuav), the key indicator of thermal transport efficiency. Thermophysical properties such as viscosity and thermal conductivity are evaluated for validation against experimental data. The results include visual representations of heatlines, streamlines, and isotherms for various physical parameters. Additionally, Nuav, friction factors, and thermal efficiency index are analyzed using different nanoparticle ratios. The findings show that buoyancy and MHD parameters significantly influence heat transfer, friction, and thermal efficiency. The addition of Cu nanoparticles improves heat transport compared to Al2O3 nanofluid, demonstrating the superior thermal conductivity of the Cu–Al2O3/water hybrid nanofluid. The results also indicate that adding Al2O3 nanoparticles to the Cu/water nanofluid diminishes the heat transport rate. The waviness of the geometry shows a significant impact on thermal management as well. Moreover, the statistical RSM analysis indicates a high R2 value of 98.88% for the response function, which suggests that the model is well suited for predicting Nuav. Furthermore, the ANN model demonstrates high accuracy with a mean squared error (MSE) of 0.00018, making it a strong alternative to RSM analysis. Finally, this study focuses on the interaction between the hybrid nanofluid, a wavy geometry, and MHD effects, which can optimize heat transfer and contribute to energy-efficient cooling or heating technologies. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fluid Mechanics)
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Figure 1

Figure 1
<p>Schematic view of the geometry.</p>
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<p>A comparison between the experimental results of Suresh et al. [<a href="#B61-mathematics-13-00078" class="html-bibr">61</a>] and the viscosity of the Cu–Al<sub>2</sub>O<sub>3</sub>/water hybrid nanofluid as predicted by the classical models (Brinkman and Batchelor).</p>
Full article ">Figure 3
<p>Comparison of the thermal conductivity of the Cu–Al<sub>2</sub>O<sub>3</sub>/water hybrid nanofluid as predicted by classical models (Hamilton–Crosser and Bruggeman) and the experimental data from Suresh et al. [<a href="#B61-mathematics-13-00078" class="html-bibr">61</a>].</p>
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<p>Mesh sensitivity analysis using (<b>a</b>) average <math display="inline"><semantics> <mrow> <mi>N</mi> <mi>u</mi> </mrow> </semantics></math> and (<b>b</b>) friction factor <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi mathvariant="normal">f</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Comparison of the streamlines (top) and isotherms (bottom) for <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>a</mi> <mo>=</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>4</mn> </mrow> </msup> <mo>,</mo> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, and Cu–water nanofluid. (<b>a</b>) Results from Geridonmez and Atilgan [<a href="#B65-mathematics-13-00078" class="html-bibr">65</a>] (left column); (<b>b</b>) results from the current study (right column).</p>
Full article ">Figure 6
<p>Variations of (<b>a</b>) streamlines, (<b>b</b>) heatlines, and (<b>c</b>) isotherms for <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>a</mi> <mo>=</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math>, and different values of <math display="inline"><semantics> <mrow> <mi>N</mi> </mrow> </semantics></math> for a 2% Cu–<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">l</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <msub> <mrow> <mi mathvariant="normal">O</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math>/water hybrid nanofluid at <math display="inline"><semantics> <mrow> <mi>H</mi> <mi>a</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>Variations of (<b>a</b>) streamlines, (<b>b</b>) heatlines, and (<b>c</b>) isotherms for <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>a</mi> <mo>=</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>5</mn> </mrow> </msup> </mrow> </semantics></math>, and various values of <math display="inline"><semantics> <mrow> <mi>N</mi> </mrow> </semantics></math> for 2% Cu–<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">l</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <msub> <mrow> <mi mathvariant="normal">O</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math>/water hybrid nanofluids at <math display="inline"><semantics> <mrow> <mi>H</mi> <mi>a</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 8
<p>Variations of (<b>a</b>) streamlines, (<b>b</b>) heatlines, and (<b>c</b>) isotherms for <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>a</mi> <mo>=</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>6</mn> </mrow> </msup> </mrow> </semantics></math>, and various values of <math display="inline"><semantics> <mrow> <mi>N</mi> </mrow> </semantics></math> for a 2% Cu–<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">l</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <msub> <mrow> <mi mathvariant="normal">O</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math>/water hybrid nanofluid at <math display="inline"><semantics> <mrow> <mi>H</mi> <mi>a</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 9
<p>Variations of (<b>a</b>) streamlines, (<b>b</b>) heatlines, and (<b>c</b>) isotherms for <span class="html-italic">H</span><math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, and various values of <math display="inline"><semantics> <mrow> <mi>N</mi> </mrow> </semantics></math> for 2% Cu–<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">l</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <msub> <mrow> <mi mathvariant="normal">O</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math>/water hybrid nanofluids at <span class="html-italic">R</span><math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>5</mn> </mrow> </msup> </mrow> </semantics></math>.</p>
Full article ">Figure 10
<p>Variations of (<b>a</b>) streamlines, (<b>b</b>) heatlines, and (<b>c</b>) isotherms for <math display="inline"><semantics> <mrow> <mi>H</mi> <mi>a</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>, and various values of <math display="inline"><semantics> <mrow> <mi>N</mi> </mrow> </semantics></math> for a 2% Cu–<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">l</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <msub> <mrow> <mi mathvariant="normal">O</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math>/water hybrid nanofluid at <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>a</mi> <mo>=</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>5</mn> </mrow> </msup> </mrow> </semantics></math>.</p>
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<p>Variations of (<b>a</b>) streamlines, (<b>b</b>) heatlines, and (<b>c</b>) isotherms for <span class="html-italic">H</span><math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>, and various values of the <math display="inline"><semantics> <mrow> <mi>N</mi> </mrow> </semantics></math> for 2% Cu–<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">l</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <msub> <mrow> <mi mathvariant="normal">O</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math>/water nanofluids at <span class="html-italic">R</span><math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>5</mn> </mrow> </msup> </mrow> </semantics></math>.</p>
Full article ">Figure 12
<p>Variation of the (<b>a</b>) average <span class="html-italic">Nu</span> and (<b>b</b>) friction factor for different Rayleigh numbers for base fluid, nanofluids, and hybrid nanofluid at <math display="inline"><semantics> <mrow> <mi>H</mi> <mi>a</mi> <mo>=</mo> <mn>20</mn> <mo> </mo> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">n</mi> <mi mathvariant="normal">d</mi> <mo> </mo> <mi>N</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 13
<p>Variation of the (<b>a</b>) average <span class="html-italic">Nu</span> and (<b>b</b>) friction factor for different Hartmann numbers for base fluid, nanofluids, and hybrid nanofluid at <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>a</mi> <mo>=</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>6</mn> </mrow> </msup> <mo> </mo> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">n</mi> <mi mathvariant="normal">d</mi> <mo> </mo> <mi>N</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 14
<p>Variation of the (<b>a</b>) average <math display="inline"><semantics> <mrow> <mi>N</mi> <mi>u</mi> </mrow> </semantics></math>, (<b>b</b>) friction factor, and (<b>c</b>) thermal efficiency along the heated wall with different mixture ratios of the nanoparticles at <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>a</mi> <mo>=</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>6</mn> </mrow> </msup> <mo>,</mo> <mo> </mo> <mi>H</mi> <mi>a</mi> <mo>=</mo> <mn>20</mn> <mo>,</mo> <mo> </mo> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">n</mi> <mi mathvariant="normal">d</mi> <mo> </mo> <mi>N</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 15
<p>Variation of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> <mi>u</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </semantics></math> with Hartmann number <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>H</mi> <mi>a</mi> <mo>)</mo> </mrow> </semantics></math> and Rayleigh number <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>R</mi> <mi>a</mi> <mo>)</mo> </mrow> </semantics></math>: (<b>a</b>) 2D contour plots, (<b>b</b>) desirability at optimum point, and (<b>c</b>) 3D surface plot.</p>
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<p>Variation of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> <mi>u</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </semantics></math> with nanoparticles volume fraction <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>ϕ</mi> <mo>)</mo> </mrow> </semantics></math> and Hartmann number <math display="inline"><semantics> <mrow> <mo>)</mo> <mi>H</mi> <mi>a</mi> <mo>)</mo> </mrow> </semantics></math>: (<b>a</b>) 2D contour plots, (<b>b</b>) desirability at optimum point, and (<b>c</b>) 3D surface plot.</p>
Full article ">Figure 17
<p>Variation of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> <mi>u</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </semantics></math> with nanoparticles volume fraction <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>ϕ</mi> <mo>)</mo> </mrow> </semantics></math> and Rayleigh number <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>R</mi> <mi>a</mi> <mo>)</mo> <mo>:</mo> </mrow> </semantics></math> (<b>a</b>) 2D contour plots, (<b>b</b>) desirability at optimum point, and (<b>c</b>) 3D surface plot.</p>
Full article ">Figure 18
<p>The <math display="inline"><semantics> <mrow> <mi>N</mi> <mi>u</mi> </mrow> </semantics></math> (average) predicted by RSM and ANN model compared to the actual <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> <mi>u</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
Full article ">Figure 19
<p>The mean squared error (MSE) plot.</p>
Full article ">
24 pages, 7920 KiB  
Article
Investigation of the Tribological Effects of Nano-Sized Transition Metal Oxides on a Base Oil Containing Pour Point Depressant and Viscosity Modifier
by Ádám István Szabó, Kevin Szabó and Hajnalka Hargitai
ChemEngineering 2025, 9(1), 1; https://doi.org/10.3390/chemengineering9010001 - 27 Dec 2024
Viewed by 446
Abstract
This study investigates the tribological effects of nano-sized metal oxides (ZrO2, CuO, Y2O3 and TiO2) in Group III type base oil containing 0.3% pour point depressant (PPD) and 5% viscosity modifier (VM) to enhance friction and [...] Read more.
This study investigates the tribological effects of nano-sized metal oxides (ZrO2, CuO, Y2O3 and TiO2) in Group III type base oil containing 0.3% pour point depressant (PPD) and 5% viscosity modifier (VM) to enhance friction and wear performance. The homogenized lubricant samples with varying concentrations of oxide nanoparticles (0.1–0.5 wt%) on a linear oscillating tribometer performed static and dynamic frictional tests. Optical and confocal microscopy surface analysis evaluated the wear of the specimen, and SEM and EDX analyses characterized the wear tracks, nanoparticle distributions, and quantification. The cooperation between PPD and nanoparticles significantly improved friction and wear values; however, the worn surface suffered extensively from fatigue wear. The collaboration between VM and nanoparticles resulted in a nanoparticle-rich tribofilm on the contact surface, providing excellent wear resistance that protects the component while also favorably impacting friction reduction. This study found CuO reduced wear volume by 85% with PPD and 43% with VM at 0.5 wt%, while ZrO2 achieved 80% and 63% reductions, respectively. Y2O3 reduced wear volume by 82% with PPD, and TiO2 reduced friction by 20% with VM. These nanoparticles enhanced tribological performance at optimal concentrations, but high concentrations caused tribofilm instability, highlighting the need for precise optimization. Full article
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<p>Measurement of the dimensions required to determine the wear scar diameter of a worn ball according to the ISO 19291:2016 standard.</p>
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<p>3D scan of wear scar using a confocal microscope to calculate wear volume.</p>
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<p>The friction absolute integral results of the tested oxide nanoparticles are shown with a dashed line for the lubricant sample containing 5% viscosity modifier and a solid line for the lubricant sample containing 0.3% pour point depressant. All y-axes are truncated at 0.1.</p>
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<p>The static friction results of the tested oxide nanoparticles are shown with a dashed line for the lubricant sample containing a 5% viscosity modifier and a solid line for the lubricant sample containing 0.3% pour point depressant. All y-axes are truncated at 0.12.</p>
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<p>The mean wear scar diameter results of the balls tested with oxide nanoparticles are shown with a dashed line for the lubricant sample containing a 5% viscosity modifier and a solid line for the lubricant sample containing 0.3% pour point depressant. All y-axes are truncated at 400 µm.</p>
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<p>The wear volume results of the disks tested with oxide nanoparticles are shown with a dashed line for the lubricant sample containing a 5% viscosity modifier and a solid line for the lubricant sample containing 0.3% pour point depressant.</p>
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<p>SEM and EDX images of the worn surface of the disc specimens tribotested with Group III + 0.3% pour point depressant + 0.5 wt% zirconia nanoadditive. The photos were taken from the wear track dead center (<b>left</b>) and stroke-middle section (<b>right</b>) areas with the horizontal sliding direction. In the <b>bottom</b> row are the results of the EDX analysis of the same images, with the location of the zirconium element highlighted in yellow.</p>
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<p>SEM and EDX images of the worn surface of the disc specimens tribotested with Group III + 5% viscosity modifier + 0.5 wt% zirconia nanoadditive. The photos were taken from the wear track dead center (<b>left</b>) and stroke-middle section (<b>right</b>) areas with the horizontal sliding direction. In the <b>bottom</b> row are the results of the EDX analysis of the same images, with the location of the zirconium element highlighted in yellow.</p>
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<p>SEM and EDX images of the worn surface of the disc specimens tribotested with Group III + 0.3% pour point depressant + 0.5 wt% cupric oxide nanoadditive. The photos were taken from the wear track dead center (<b>left</b>) and stroke-middle section (<b>right</b>) areas with the horizontal sliding direction. In the <b>bottom</b> row are the results of the EDX analysis of the same images, with the location of the copper element highlighted in yellow.</p>
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<p>SEM and EDX images of the worn surface of the disc specimens tribotested with Group III + 5% viscosity modifier + 0.5 wt% cupric oxide nanoadditive. The photos were taken from the wear track dead center (<b>left</b>) and stroke-middle section (<b>right</b>) areas with the horizontal sliding direction. In the <b>bottom</b> row are the results of the EDX analysis of the same images, with the location of the copper element highlighted in yellow.</p>
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<p>SEM and EDX images of the worn surface of the disc specimens tribotested with Group III + 0.3% pour point depressant + 0.5 wt% yttria nanoadditive. The photos were taken from the wear track dead center (<b>left</b>) and stroke-middle section (<b>right</b>) areas with the horizontal sliding direction. In the <b>bottom</b> row are the results of the EDX analysis of the same images, with the location of the yttrium element highlighted in yellow.</p>
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<p>SEM and EDX images of the worn surface of the disc specimens tribotested with Group III + 5% viscosity modifier + 0.3 wt% yttria nanoadditive. The photos were taken from the wear track dead center (<b>left</b>) and stroke-middle section (<b>right</b>) areas with the horizontal sliding direction. In the <b>bottom</b> row are the results of the EDX analysis of the same images, with the location of the yttrium element highlighted in yellow.</p>
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<p>SEM and EDX images of the worn surface of the disc specimens tribotested with Group III + 0.3% pour point depressant + 0.3 wt% titania nanoadditive. The photos were taken from the wear track dead center (<b>left</b>) and stroke-middle section (<b>right</b>) areas with the horizontal sliding direction. In the <b>bottom</b> row are the results of the EDX analysis of the same images, with the location of the titanium element highlighted in yellow.</p>
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<p>SEM and EDX images of the worn surface of the disc specimens tribotested with Group III + 5% viscosity modifier + 0.3 wt% titania nanoadditive. The photos were taken from the wear track dead center (<b>left</b>) and stroke-middle section (<b>right</b>) areas with the horizontal sliding direction. In the <b>bottom</b> row are the results of the EDX analysis of the same images, with the location of the titanium element highlighted in yellow.</p>
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13 pages, 3939 KiB  
Article
CuO-TiO2–Saponite Ternary Nanocomposite for Efficient Removal of Bromocresol Green Dye
by Pollyana Trigueiro, Willams A. Albuquerque, Aimée G. Jerônimo, Monica Sá Rodrigues, Emanoel L. Tavares França and Ramón Raudel Peña-Garcia
Minerals 2024, 14(12), 1268; https://doi.org/10.3390/min14121268 - 13 Dec 2024
Viewed by 673
Abstract
This study presents the synthesis of a CuO-TiO2–saponite ternary nanocomposite via a hydrothermal method, designed to efficiently remove bromocresol green dye. Characterization techniques, including X-ray diffraction, Fourier transform infrared spectroscopy, and scanning electron microscopy, confirmed significant interactions between metal oxide nanoparticles [...] Read more.
This study presents the synthesis of a CuO-TiO2–saponite ternary nanocomposite via a hydrothermal method, designed to efficiently remove bromocresol green dye. Characterization techniques, including X-ray diffraction, Fourier transform infrared spectroscopy, and scanning electron microscopy, confirmed significant interactions between metal oxide nanoparticles and the clay mineral matrix. Diffuse reflectance and photoluminescence analyses revealed a narrow band gap and surface defects, such as oxygen vacancies, enhancing the material’s photocatalytic properties. Under UV irradiation, the nanocomposite achieved 83% discoloration of bromocresol green dye within 150 min. The inhibitor studies identified hydroxyl and superoxide radicals as key species in the degradation mechanism. This work underscores the potential of clay-mineral-based nanocomposites, where clay minerals function both as structural support and as enhancers of the semiconductor’s photocatalytic activity. Full article
(This article belongs to the Section Clays and Engineered Mineral Materials)
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<p>XRD pattern: (<b>a</b>) raw saponite and (<b>b</b>) CuO-TiO<sub>2</sub>–saponite nanocomposite obtained by hydrothermal synthesis.</p>
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<p>FTIR spectrum: (<b>a</b>) raw saponite and (<b>b</b>) CuO-TiO<sub>2</sub>–saponite nanocomposite obtained by hydrothermal synthesis.</p>
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<p>SEM images: (<b>a</b>) raw saponite and (<b>b</b>) CuO-TiO<sub>2</sub>–saponite nanocomposite.</p>
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<p>UV–Vis DRS spectrum and the optical band gap for the CuO-TiO<sub>2</sub>–saponite nanocomposite. The insert represents the band gap calculation using Tauc’s relation.</p>
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<p>Photoluminescence spectrum of the CuO-TiO<sub>2</sub>–saponite nanocomposite.</p>
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<p>(<b>a</b>) Absorbance spectra and (<b>b</b>) degradation ratio of the bromocresol green dye using the CuO-TiO<sub>2</sub>–saponite nanocomposite</p>
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<p>Scavengers’ tests of the bromocresol green dye photocatalytic removal using the CuO-TiO<sub>2</sub>–saponite nanocomposite under UV irradiation for 150 min.</p>
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<p>Reuse tests of CuO-TiO<sub>2</sub>–saponite for bromocresol green dye decolorization under UV irradiation for 150 min.</p>
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<p>Representation of bromocresol green dye decolorization mechanism over CuO-TiO<sub>2</sub>–saponite nanocomposite under UV irradiation.</p>
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24 pages, 3624 KiB  
Review
Recent Advances in the Adsorption of Different Pollutants from Wastewater Using Carbon-Based and Metal-Oxide Nanoparticles
by Shahabaldin Rezania, Negisa Darajeh, Parveen Fatemeh Rupani, Amin Mojiri, Hesam Kamyab and Mohsen Taghavijeloudar
Appl. Sci. 2024, 14(24), 11492; https://doi.org/10.3390/app142411492 - 10 Dec 2024
Viewed by 911
Abstract
In recent years, nanomaterials have gained special attention for removing contaminants from wastewater. Nanoparticles (NPs), such as carbon-based materials and metal oxides, exhibit exceptional adsorption capacity and antimicrobial properties for wastewater treatment. Their unique properties, including reactivity, high surface area, and tunable surface [...] Read more.
In recent years, nanomaterials have gained special attention for removing contaminants from wastewater. Nanoparticles (NPs), such as carbon-based materials and metal oxides, exhibit exceptional adsorption capacity and antimicrobial properties for wastewater treatment. Their unique properties, including reactivity, high surface area, and tunable surface functionalities, make them highly effective adsorbents. They can remove contaminants such as organics, inorganics, pharmaceuticals, medicine, and dyes by adsorption mechanisms. In this review, the effectiveness of different types of carbon-based NPs, including carbon nanotubes (CNTs), graphene-based nanoparticles (GNPs), carbon quantum dots (CQDs), carbon nanofibers (CNFs), and carbon nanospheres (CNSs), and metal oxides, including copper oxide (CuO), zinc oxide (ZnO), iron oxide (Fe2O3), titanium oxide (TiO2), and silver oxide (Ag2O), in the removal of different contaminants from wastewater has been comprehensively evaluated. In addition, their synthesis methods, such as physical, chemical, and biological, have been described. Based on the findings, CNPs can remove 75 to 90% of pollutants within two hours, while MONPs can remove 60% to 99% of dye in 150 min, except iron oxide NPs. For future studies, the integration of NPs into existing treatment systems and the development of novel nanomaterials are recommended. Hence, the potential of NPs is promising, but challenges related to their environmental impact and their toxicity must be considered. Full article
(This article belongs to the Special Issue Water Treatment: From Membrane Processes to Renewable Energies)
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<p>Different types of carbon and metal-oxide nanoparticles.</p>
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<p>Schematic of different adsorption mechanisms.</p>
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<p>Synthesis methods of NPs.</p>
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<p>Structural representation of (<b>a</b>) SWCNTs and (<b>b</b>) MWCNTs. Source: Adapted from [<a href="#B57-applsci-14-11492" class="html-bibr">57</a>].</p>
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<p>Different types of graphene nanomaterials: (<b>a</b>) graphene, (<b>b</b>) GO, (<b>c</b>) rGO, and (<b>d</b>) GQD. Source: [<a href="#B66-applsci-14-11492" class="html-bibr">66</a>].</p>
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<p>(<b>a</b>–<b>c</b>) Formation of a cup-stacked CNF structure and a (<b>d</b>) platelet CNF structure. Source: adapted from [<a href="#B84-applsci-14-11492" class="html-bibr">84</a>].</p>
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<p>Schematic of the adsorption mechanism using magnetic nanosheets. Source: [<a href="#B121-applsci-14-11492" class="html-bibr">121</a>].</p>
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<p>Different crystalline structures of TiO<sub>2</sub> nanomaterials: anatase, rutile, and brookite. The red ball represents the Ti<sup>2+</sup> ion, and the white ball is O<sub>2</sub><sup>−</sup>. Source: [<a href="#B154-applsci-14-11492" class="html-bibr">154</a>].</p>
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<p>Crystal structures of hematite, magnetite, and maghemite. Source: [<a href="#B164-applsci-14-11492" class="html-bibr">164</a>].</p>
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<p>Different cost allocations of nanomaterials in the wastewater treatment process.</p>
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7 pages, 1907 KiB  
Proceeding Paper
Investigation of the Effects of CuO Nanoparticles on the Tribological Properties of Thermally Aged Group III Base Oil
by Ádám István Szabó and Péter Bence Pápai
Eng. Proc. 2024, 79(1), 82; https://doi.org/10.3390/engproc2024079082 - 12 Nov 2024
Viewed by 393
Abstract
Protecting our environment is a primary focus in various industries, including the automotive sector, which aims to reduce friction and wear to minimize emissions. This study examines the effect of cupric oxide nanoparticles on artificially aged Group III base oil under lab conditions. [...] Read more.
Protecting our environment is a primary focus in various industries, including the automotive sector, which aims to reduce friction and wear to minimize emissions. This study examines the effect of cupric oxide nanoparticles on artificially aged Group III base oil under lab conditions. The oil, aged using a temperature-focused method, was homogenized with 0.5 wt% cupric oxide nanoparticles. A ball-on-disc tribological system registered static and hydrodynamic friction. Wear track sizes indicated the nanoadditive’s positive impact compared to the oil without additives. The experiments revealed the anti-aging effect of cupric oxide nanoceramics. Lubricant aged with cupric oxide performed similarly to new oil, and cupric oxide nanoparticles positively affected friction and wear. The oil supplemented before aging showed better tribological results than after aging. Full article
(This article belongs to the Proceedings of The Sustainable Mobility and Transportation Symposium 2024)
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<p>Wear scar on the ball (<b>a</b>) and wear on the disc (<b>b</b>) under the Keyence VHX-1000 digital microscope, supplemented with a false-color height image of the disc wear (<b>c</b>) taken with the Leica DCM3D confocal microscope.</p>
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<p>Comparison of FT-IR spectra of Group III base oil and CuO-aged Group III oil samples.</p>
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<p>Comparison of kinematic viscosity among the examined oil samples.</p>
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<p>Comparison of static friction (COF) and dynamic friction (FAI) results among the examined oil samples.</p>
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<p>Comparison of wear scar diameter (WSD) results on the balls among the examined oil samples.</p>
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<p>Comparison of wear volume (WV) results on the discs among the examined oil samples.</p>
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27 pages, 5077 KiB  
Article
Green Synthesis of Ag and Cu Nanoparticles Using E. telmateia Ehrh Extract: Coating, Characterization, and Bioactivity on PEEK Polymer Substrates
by Şakir Altınsoy, Kadriye Kızılbey and Hümeyra Berfin İlim
Materials 2024, 17(22), 5501; https://doi.org/10.3390/ma17225501 - 11 Nov 2024
Viewed by 947
Abstract
PEEK-based implant materials have gained increasing attention as an alternative to titanium due to their biocompatibility and bone-like elasticity. However, PEEK’s surface quality and wear resistance are lower than those of metals. This study aimed to enhance the bioactivity and surface quality of [...] Read more.
PEEK-based implant materials have gained increasing attention as an alternative to titanium due to their biocompatibility and bone-like elasticity. However, PEEK’s surface quality and wear resistance are lower than those of metals. This study aimed to enhance the bioactivity and surface quality of PEEK by coating it with silver and copper nanoparticles synthesized via a green method using Equisetum telmateia Ehrh. extract. PEEK samples (Ø 25 mm, 3 mm thick) were coated with single and double layers using spray (airbrush-spray) and drop-coating methods. Comprehensive analyses including SEM, EDX, FT-IR, UV-Vis, surface roughness, release studies, antioxidant and cytotoxicity activity, and antibacterial tests were conducted on the coated samples. The results demonstrated that AgNPs and CuNPs coatings significantly improved the surface quality of PEEK. SEM analysis revealed particle sizes ranging from 48 to 160 nm for AgNPs and 50–135 nm for CuNPs, with superior dispersion obtained using the airbrush-spray method. Surface roughness measurements showed a reduction of 17–33% for AgNPs-coated samples and 7–15% for CuNPs-coated samples compared to uncoated PEEK, with airbrush-spray coatings providing smoother surfaces. Antioxidant activity tests indicated that AgNPs provided 35% higher antioxidant activity compared to CuNPs. Additionally, antibacterial tests revealed that AgNPs exhibited a higher zone of inhibition (up to 14 mm for S. aureus and 18 mm for E. coli) compared to CuNPs, which exhibited zones of 8 mm and 10 mm, respectively. This study concludes that green-synthesized AgNPs, in particular, enhance the bioactivity and surface properties of PEEK, making it a promising material for biomedical applications such as infection-resistant implants. Full article
(This article belongs to the Topic Advanced Biomaterials: Processing and Applications)
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<p>The schematic view of the experimental steps.</p>
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<p>Size distribution of AgNPs (<b>a</b>) and CuNPs (<b>b</b>) by intensity.</p>
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<p>UV-Vis spectrum of AgNO<sub>3</sub> and AgNPs (<b>a</b>), and UV-Vis spectrum of CuSO<sub>4</sub> and CuNPs (<b>b</b>).</p>
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<p>FT-IR spectrum AgNO<sub>3</sub> and AgNPs (<b>a</b>), and PEEK samples coated with AgNPs (<b>b</b>).</p>
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<p>FT-IR spectrum CuSO<sub>4</sub>, CuNPs (<b>a</b>), and PEEK samples coated with CuNPs (<b>b</b>).</p>
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<p>SEM images of AgNPs (<b>a</b>) and CuNPs (<b>b</b>).</p>
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<p>SEM images of AgNPs using the drop-casting method: (<b>a</b>) single-layer coating; (<b>b</b>) double-layer coating.</p>
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<p>SEM images of AgNPs using the airbrush-spray method: (<b>a</b>) single-layer coating; (<b>b</b>) double-layer coating.</p>
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<p>SEM images of CuNPs using the drop-casting method: (<b>a</b>) single-layer coating; (<b>b</b>) double-layer coating.</p>
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<p>SEM images of CuNPs using the airbrush-spray method: (<b>a</b>) single-layer coating; (<b>b</b>) double-layer coating.</p>
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<p>EDS spectrum analyses of the PEEK.</p>
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<p>EDS spectrum of AgNPs (<b>a</b>) and CuNPs (<b>b</b>) synthesized using <span class="html-italic">E. telmateia</span> Ehrh. plant extract.</p>
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<p>EDS spectrum of AgNPs (<b>a</b>) and CuNPs (<b>b</b>) synthesized using <span class="html-italic">E. telmateia</span> Ehrh. plant extract.</p>
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<p>EDS spectrum of PEEK samples coated with AgNPs drop-casting single layer (<b>a</b>), double layer (<b>b</b>) and airbrush-spray single layer (<b>c</b>), double layer (<b>d</b>).</p>
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<p>EDS spectrum of PEEK samples coated with AgNPs drop-casting single layer (<b>a</b>), double layer (<b>b</b>) and airbrush-spray single layer (<b>c</b>), double layer (<b>d</b>).</p>
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<p>EDS spectrum of PEEK samples coated with CuNPs drop-casting single layer (<b>a</b>), double layer (<b>b</b>) and airbrush-spray single layer (<b>c</b>), double layer (<b>d</b>).</p>
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<p>EDS spectrum of PEEK samples coated with CuNPs drop-casting single layer (<b>a</b>), double layer (<b>b</b>) and airbrush-spray single layer (<b>c</b>), double layer (<b>d</b>).</p>
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<p>Surface roughness results.</p>
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<p>Antioxidant activity results.</p>
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<p>MTT test results of AgNPs (<b>a</b>) and CuNPs (<b>b</b>).</p>
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<p>Antibacterial activities of AgNPs and CuNPs.</p>
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21 pages, 12291 KiB  
Article
The Role of Heater Size and Location in Modulating Natural Convection Behavior in Cu-Water Nanofluid-Loaded Square Enclosures
by Abdulaziz Alasiri
Sustainability 2024, 16(22), 9648; https://doi.org/10.3390/su16229648 - 6 Nov 2024
Viewed by 900
Abstract
Enhancing the energy efficiency of thermal systems reduces their consumption, lowers costs, and reduces undesired environmental impact, thus making these systems more sustainable. The current work introduces a passive method for heat transfer enhancement that is carried out using natural convection by nanofluid. [...] Read more.
Enhancing the energy efficiency of thermal systems reduces their consumption, lowers costs, and reduces undesired environmental impact, thus making these systems more sustainable. The current work introduces a passive method for heat transfer enhancement that is carried out using natural convection by nanofluid. This work introduces a computational study of the process of natural convection within a square cavity containing Cu/H2O nanofluid. The cavity wall on the left side undergoes partial isothermal heating, while the opposing side is fully cooled isothermally, with all other boundaries maintained adiabatic. A mathematical model formulated based on a 2-D model was used to provide the solution for the system of governing equations of mass, momentum, and energy conservation, employing the finite element technique. A commercial CFD package is utilized to perform the computational simulation. The present investigation delves into the impact of the Rayleigh number, nanoparticle concentration, heater length, and heater location on the flow field and heat transfer characteristics. The model outcomes were displayed for a wide range of the pertinent parameters as 103 ≤ Ra ≤ 106, 0.25 ≤ lh ≤ 1.0, 0.125 ≤ hc ≤ 0.875, and 0.02 ≤ ϕ ≤ 0.10. Also, correlation equations relating the average Nusselt number to these crucial parameters are derived. These equations are simple and can be applied in practice easily in many fields, such as electric and electronic equipment cooling and thermal management of heat sources. Also, these equations gather all the parameters that affect the heat transfer process. They are shedding light on the intricate interplay between these parameters in the natural convection heat transfer process. Full article
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<p>Problem description and coordinate system.</p>
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<p>The present model predictions against previous works [<a href="#B35-sustainability-16-09648" class="html-bibr">35</a>,<a href="#B36-sustainability-16-09648" class="html-bibr">36</a>,<a href="#B38-sustainability-16-09648" class="html-bibr">38</a>].</p>
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<p>(<b>a</b>) Impact of Rayleigh number on the average Nusselt number. (<b>b</b>) Temperature contours for various Rayleigh numbers for l<sub>h</sub> = 0.25, h<sub>c</sub> = 0.5, and ϕ = 0.06. (<b>c</b>) Stream function contours for different Rayleigh numbers for l<sub>h</sub> = 0.25, h<sub>c</sub> = 0.5, and ϕ = 0.06.</p>
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<p>(<b>a</b>) Impact of Rayleigh number on the average Nusselt number. (<b>b</b>) Temperature contours for various Rayleigh numbers for l<sub>h</sub> = 0.25, h<sub>c</sub> = 0.5, and ϕ = 0.06. (<b>c</b>) Stream function contours for different Rayleigh numbers for l<sub>h</sub> = 0.25, h<sub>c</sub> = 0.5, and ϕ = 0.06.</p>
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<p>(<b>a</b>) Impact of Rayleigh number on the average Nusselt number. (<b>b</b>) Temperature contours for various Rayleigh numbers for l<sub>h</sub> = 0.25, h<sub>c</sub> = 0.5, and ϕ = 0.06. (<b>c</b>) Stream function contours for different Rayleigh numbers for l<sub>h</sub> = 0.25, h<sub>c</sub> = 0.5, and ϕ = 0.06.</p>
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<p>(<b>a</b>) Change in the average Nusselt number with heater location for various amounts of nanoparticles and various Rayleigh numbers, heater size l<sub>h</sub> = 0.25. (<b>b</b>) Temperature contours for different heater locations for l<sub>h</sub> = 0.25, Ra = 1 × 10<sup>5</sup>, ϕ = 0.06. (<b>c</b>) Stream function contours for different heater locations for l<sub>h</sub> = 0.25, Ra = 1 × 10<sup>5</sup>, and ϕ = 0.06.</p>
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<p>(<b>a</b>) Change in the average Nusselt number with heater location for various amounts of nanoparticles and various Rayleigh numbers, heater size l<sub>h</sub> = 0.25. (<b>b</b>) Temperature contours for different heater locations for l<sub>h</sub> = 0.25, Ra = 1 × 10<sup>5</sup>, ϕ = 0.06. (<b>c</b>) Stream function contours for different heater locations for l<sub>h</sub> = 0.25, Ra = 1 × 10<sup>5</sup>, and ϕ = 0.06.</p>
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<p>(<b>a</b>) Change in the average Nusselt number with heater location for various amounts of nanoparticles and various Rayleigh numbers, heater size l<sub>h</sub> = 0.25. (<b>b</b>) Temperature contours for different heater locations for l<sub>h</sub> = 0.25, Ra = 1 × 10<sup>5</sup>, ϕ = 0.06. (<b>c</b>) Stream function contours for different heater locations for l<sub>h</sub> = 0.25, Ra = 1 × 10<sup>5</sup>, and ϕ = 0.06.</p>
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<p>(<b>a</b>) Change in average Nusselt number with heater size for various volume fractions of nanoparticles and Rayleigh numbers and heater location h<sub>c</sub> = 0.5. (<b>b</b>) Temperature contours for various heater sizes for Ra = 1 × 10<sup>5</sup>, h<sub>c</sub> = 0.5, and ϕ = 0.06. (<b>c</b>) Stream function contours for different heater sizes for Ra = 1 × 10<sup>5</sup>, h<sub>c</sub> = 0.5, and ϕ = 0.06.</p>
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<p>(<b>a</b>) Change in average Nusselt number with heater size for various volume fractions of nanoparticles and Rayleigh numbers and heater location h<sub>c</sub> = 0.5. (<b>b</b>) Temperature contours for various heater sizes for Ra = 1 × 10<sup>5</sup>, h<sub>c</sub> = 0.5, and ϕ = 0.06. (<b>c</b>) Stream function contours for different heater sizes for Ra = 1 × 10<sup>5</sup>, h<sub>c</sub> = 0.5, and ϕ = 0.06.</p>
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<p>(<b>a</b>) Change in average Nusselt number with heater size for various volume fractions of nanoparticles and Rayleigh numbers and heater location h<sub>c</sub> = 0.5. (<b>b</b>) Temperature contours for various heater sizes for Ra = 1 × 10<sup>5</sup>, h<sub>c</sub> = 0.5, and ϕ = 0.06. (<b>c</b>) Stream function contours for different heater sizes for Ra = 1 × 10<sup>5</sup>, h<sub>c</sub> = 0.5, and ϕ = 0.06.</p>
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<p>(<b>a</b>) Average Nusselt number versus the volume percentage of nanoparticles for various heater sizes for heater size l<sub>h</sub> = 0.25 and Ra = 10<sup>5</sup>. (<b>b</b>) Average Nusselt number versus the volume percentage of nanoparticles for various heater sizes for heater location h<sub>c</sub> = 0.5, Ra = 10<sup>5</sup>. (<b>c</b>) Temperature contours for different volume fractions of nanoparticles for Ra = 10<sup>5</sup>, l<sub>h</sub> = 0.25, and h<sub>c</sub> = 0.5. (<b>d</b>) Stream function contours for different volume fractions of nanoparticles for Ra = 10<sup>5</sup>, l<sub>h</sub> = 0.25, and h<sub>c</sub> = 0.5.</p>
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<p>(<b>a</b>) Average Nusselt number versus the volume percentage of nanoparticles for various heater sizes for heater size l<sub>h</sub> = 0.25 and Ra = 10<sup>5</sup>. (<b>b</b>) Average Nusselt number versus the volume percentage of nanoparticles for various heater sizes for heater location h<sub>c</sub> = 0.5, Ra = 10<sup>5</sup>. (<b>c</b>) Temperature contours for different volume fractions of nanoparticles for Ra = 10<sup>5</sup>, l<sub>h</sub> = 0.25, and h<sub>c</sub> = 0.5. (<b>d</b>) Stream function contours for different volume fractions of nanoparticles for Ra = 10<sup>5</sup>, l<sub>h</sub> = 0.25, and h<sub>c</sub> = 0.5.</p>
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<p>(<b>a</b>) Average Nusselt number versus the volume percentage of nanoparticles for various heater sizes for heater size l<sub>h</sub> = 0.25 and Ra = 10<sup>5</sup>. (<b>b</b>) Average Nusselt number versus the volume percentage of nanoparticles for various heater sizes for heater location h<sub>c</sub> = 0.5, Ra = 10<sup>5</sup>. (<b>c</b>) Temperature contours for different volume fractions of nanoparticles for Ra = 10<sup>5</sup>, l<sub>h</sub> = 0.25, and h<sub>c</sub> = 0.5. (<b>d</b>) Stream function contours for different volume fractions of nanoparticles for Ra = 10<sup>5</sup>, l<sub>h</sub> = 0.25, and h<sub>c</sub> = 0.5.</p>
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<p>Correlation equations for the average Nusselt number (<b>a</b>) h<sub>c</sub> ≤ 0.5, (<b>b</b>) h<sub>c</sub> ≥ 0.5, (<b>c</b>) h<sub>c</sub> = 0.5.</p>
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<p>Correlation equations for the average Nusselt number (<b>a</b>) h<sub>c</sub> ≤ 0.5, (<b>b</b>) h<sub>c</sub> ≥ 0.5, (<b>c</b>) h<sub>c</sub> = 0.5.</p>
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21 pages, 9321 KiB  
Article
The Influence of As-Cast Grain Size on the Formation of Recrystallized Grains and the Related Mechanical Properties in Al–Zn–Mg–Cu-Based Alloy Sheets
by Jonggyu Jeon, Sangjun Lee, Jeheon Jeon, Maru Kang and Heon Kang
Materials 2024, 17(21), 5267; https://doi.org/10.3390/ma17215267 - 29 Oct 2024
Viewed by 693
Abstract
The influence of as-cast grain size on recrystallization and the related mechanical properties of Al–Zn–Mg–Cu-based alloys was investigated. Grain sizes ranging from 163 to 26 μm were achieved by adding Ti, Cr and Mn, and ZnO nano-particles, which acted as heterogeneous nucleation sites. [...] Read more.
The influence of as-cast grain size on recrystallization and the related mechanical properties of Al–Zn–Mg–Cu-based alloys was investigated. Grain sizes ranging from 163 to 26 μm were achieved by adding Ti, Cr and Mn, and ZnO nano-particles, which acted as heterogeneous nucleation sites. A decrease in the as-cast grain size led to a corresponding reduction in the recrystallized grain size from 54 to 13 μm. Notably, as-cast grain sizes below 100 μm provided additional nucleation sites at grain boundaries, allowing for a reasonable prediction of recrystallized grain size. Finer grains also contributed to enhanced mechanical properties, with yield strength increasing as recrystallized grain size decreased without significant loss of elongation. Additional strengthening was observed due to η-precipitates at grain boundaries, further improving the properties of fine-grained sheets. Full article
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<p>Schematic of the experimental procedure.</p>
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<p>OM images of the as-cast alloys: alloys (<b>a</b>) A, (<b>b</b>) B, (<b>c</b>) C, and (<b>d</b>) D. When the Ti, Cr and Mn, and ZnO nano-particles are added cumulatively, the average grain size of cast alloys decreases from 163 to 26 μm.</p>
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<p>SEM images of the as-cast alloys: alloys (<b>a</b>) A, (<b>b</b>) B, (<b>c</b>) C, and (<b>d</b>) D. (<b>e</b>) The variation in the volume fraction of eutectic particles plotted with the average grain size of cast alloys. As the average grain size of the cast alloys decreases from 163 to 26 μm, the volume fraction of the eutectic particles increases from 4.86 to 8.03 vol.%.</p>
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<p>SEM images of alloys with heat treatment for homogenization at 430 °C for 12 h: alloys (<b>a</b>) A, (<b>b</b>) B, (<b>c</b>) C, and (<b>d</b>) D. (<b>e</b>) The variation in the particle size and volume fraction of eutectic particles with the average grain size of cast alloys. The average size of the remaining eutectic particles significantly reduces from 46.1 to 20.6 μm as the as-cast grain size decreases.</p>
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<p>SEM images of hot-rolled sheets with thickness of 3 mm: alloy sheets (<b>a</b>) A, (<b>b</b>) B, (<b>c</b>) C, and (<b>d</b>) D. Enlarged images for each specimen are shown in the white rectangle. (<b>e</b>) The variation in the particle size and volume fraction of eutectic particles of the alloy sheets. The average size of eutectic particles decreases from 4.4 to 3.0 μm as the as-cast grain size decreases.</p>
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<p>IPF maps and ODF of alloy sheets recrystallized at 480 °C for 1 h according to their crystallographic direction along the normal direction (ND) of the sheet: alloy sheets (<b>a</b>) A, (<b>b</b>) B, (<b>c</b>) C, and (<b>d</b>) D. The average grain size of alloy sheets A, B, C, and D is 54, 37, 16, and 13 μm, respectively. Maximum intensity decreased from 5.25 to 2.04 as the average grain size decreased from 54 to 13 μm.</p>
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<p>Scanning TEM images of the alloy sheets recrystallized at 480 °C for 1 h: alloy sheets (<b>a</b>) A, (<b>b</b>) B, (<b>c</b>) C, and (<b>d</b>) D. The chemical compositions of the marked particles from S1 to S5 are presented in <a href="#materials-17-05267-t002" class="html-table">Table 2</a>.</p>
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<p>Engineering stress–strain curves of alloy sheets: (<b>a</b>) sheets recrystallized at 480 °C for 1 h and (<b>b</b>) sheets aged at 120 °C for 12 h. Surface images of the deformed tensile specimens were interpolated in (<b>a</b>). When Ti, Cr and Mn, and ZnO nano-particles are cumulatively added (as the average grain size of the alloy sheets decreases), the YS and UTS of recrystallized and aged sheets gradually increase without significant loss of the EL.</p>
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<p>IPF and KAM maps of sheets deformed by 20%: alloy sheets (<b>a</b>) A, (<b>b</b>) B, (<b>c</b>) C, and (<b>d</b>) D. The average grain size of alloy sheets A, B, C, and D was 92, 55, 20, and 19 μm, respectively. The stored strain energy was concentrated at local grain boundaries in the alloy sheets A and B, whereas the stored strain energy was distributed uniformly over the entire grain boundary in the alloy sheets C and D.</p>
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<p>Schematic of the grain refinement mechanism during the conventional TMT processes in coarse- and fine-grained alloys. A larger area of as-cast grain boundary provides numerous potential nucleation sites for recrystallized grains, and the finely dispersed micro-scale particles induce active PSN.</p>
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<p>Recrystallized grain size vs. the ratio of the particle size to volume fraction (<math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>P</mi> </msub> <mo>/</mo> <msub> <mi>f</mi> <mi>P</mi> </msub> </mrow> </semantics></math>). The effect of grain refinement by micro-scale particles is indicated by solid black line. Thus, the effect of grain refinement contributed by micro-scale particles and grain boundaries can be separated. The alloy A has little effect of grain refinement contributed by grain boundaries, whereas alloys C and D have a large effect.</p>
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<p>The number of nuclei for recrystallized grains induced by grain boundaries (<math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>G</mi> <mo>.</mo> <mi>B</mi> <mo>.</mo> </mrow> </msub> </mrow> </semantics></math>) vs. the area of grain boundary per unit volume (<math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mrow> <mi>G</mi> <mo>.</mo> <mi>B</mi> <mo>.</mo> </mrow> </msub> </mrow> </semantics></math>). When the as-cast grain size is less than 100 μm, the <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>G</mi> <mo>.</mo> <mi>B</mi> <mo>.</mo> </mrow> </msub> </mrow> </semantics></math> has a linear relationship with the <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mrow> <mi>G</mi> <mo>.</mo> <mi>B</mi> <mo>.</mo> </mrow> </msub> </mrow> </semantics></math> and is given by <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>G</mi> <mo>.</mo> <mi>B</mi> <mo>.</mo> </mrow> </msub> <mo>=</mo> <mn>2.56</mn> <msup> <mi>E</mi> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> <mo>×</mo> <msub> <mi>A</mi> <mrow> <mi>G</mi> <mo>.</mo> <mi>B</mi> <mo>.</mo> </mrow> </msub> <mo>−</mo> <mn>1.50</mn> <msup> <mi>E</mi> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math>.</p>
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<p>Estimated strength increment for different strengthening mechanisms in aged alloy sheet D compared to aged alloy sheet A. When the average grain size decreases from 54 to 13 μm, the value of <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>σ</mi> <mrow> <mi>G</mi> <mo>.</mo> <mi>B</mi> <mo>.</mo> </mrow> </msub> </mrow> </semantics></math> is 16.7 MPa. The values of <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>σ</mi> <mrow> <mi>P</mi> <mi>a</mi> <mi>r</mi> <mi>t</mi> <mi>i</mi> <mi>c</mi> <mi>l</mi> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math> contributed by addition of Ti, Cr and Mn, and ZnO nano-particles are 19.6, 24.9, and 16.3 MPa, respectively. The additional precipitate strengthening of 27.4 MPa in alloy D is due to the effect of grain refinement in the recrystallized sheet on the precipitation behavior.</p>
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18 pages, 12959 KiB  
Article
Multifunctional Nanocomposite Hydrogel with Enhanced Chemodynamic Therapy and Starvation Therapy for Inhibiting Postoperative Tumor Recurrence
by Zeliang Li and Xiaoxuan Ma
Int. J. Mol. Sci. 2024, 25(21), 11465; https://doi.org/10.3390/ijms252111465 - 25 Oct 2024
Viewed by 788
Abstract
Surgical resection is the primary treatment for melanoma; however, preventing tumor recurrence after resection remains a significant clinical challenge. To address this, we developed a multifunctional nanocomposite hydrogel (H-CPG) composed of glucose oxidase (GOx)-coated CuS@PDA@GOx (CPG) nanoparticles, aminated hyaluronic acid (HA-ADH), and oxidized [...] Read more.
Surgical resection is the primary treatment for melanoma; however, preventing tumor recurrence after resection remains a significant clinical challenge. To address this, we developed a multifunctional nanocomposite hydrogel (H-CPG) composed of glucose oxidase (GOx)-coated CuS@PDA@GOx (CPG) nanoparticles, aminated hyaluronic acid (HA-ADH), and oxidized rhizomatous polysaccharides (OBSP), which are interconnected through hydrogen bonds and dynamic Schiff base linkages. In the acidic tumor micro-environment, the hydrogel releases GOx, catalyzing the production of hydrogen peroxide (H2O2), which enhances chemokinetic activity through a Cu2+-mediated Fenton-like reaction. This process generates hydroxyl radicals that intensify oxidative stress and promote macrophage polarization from the M2 to M1 phenotype. This polarization triggers the release of pro-inflammatory cytokines, thereby inhibiting tumor recurrence. Additionally, the hydrogel induces photothermal effects that help eradicate residual bacteria at the wound site. Overall, the H-CPG hydrogel offers a dual mechanism to prevent melanoma recurrence and reduce resistance to monotherapy, presenting a promising strategy for postoperative tumor management. Full article
(This article belongs to the Section Molecular Nanoscience)
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<p>Mechanism diagram of the synthesis of CuS@PDA@GOx nanoparticles and H-CPG hydrogel. Mechanism diagram of recurrence inhibition in postoperative melanoma application of H-CPG hydrogel.</p>
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<p>Preparation and characterization of CPG: (<b>A</b>) EDS image of CPG. (<b>B</b>) Size distribution and TEM image of CPG. (<b>C</b>) XRD image of CPG. (<b>D</b>) zeta potential maps of CuS, CP, CPG. (<b>E</b>) CAS-H<sub>2</sub>O<sub>2</sub>, CuS-H<sub>2</sub>O<sub>2</sub>, CP-H<sub>2</sub>O<sub>2</sub>, CPG-H<sub>2</sub>O<sub>2</sub>, CPG-Glucose (Glu) for the determination of -OH generation. (<b>F</b>) Photothermal heating curves for 10 min at different concentrations of CPG. (<b>G</b>) Photothermal stability of CPG with 3 on/off cycles. (Mean ± SD (<span class="html-italic">n</span> = 3)). *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Morphology, degradation properties, mechanical properties, and photothermal properties of H-CPG hydrogels: (<b>A</b>) Optical images of hydrogels. The two solutions of HA-ADH and OBSP were mixed in the same volume. (<b>B</b>) SEM plots of the hydrogels of H-CPG. (<b>C</b>) FTIR plots of H-CPG, HA-ADH, OBSP. (<b>D</b>) Tensile stress–strain curves of the hydrogels of H, H-C, H-CP, H-CPG. (<b>E</b>) H, H-C, H-CP, H-CPG hydrogels of maximum withstand pressure plots. (<b>F</b>) H-CPG strain scans showing gel–sol transition points. (<b>G</b>) G′ and G″ change curves of H-CPG hydrogel at alternating high strain (400%) and low strain (1%). (<b>H</b>) Oscillation frequencies (1–100 rad/s) of scanned H-CPG hydrogels at 37 °C and 1 Hz. (<b>I</b>) H, H-C, H-CP, H-CPG hydrogels swelling properties in PBS at 37 °C. (<b>J</b>) Photothermal images of PBS, H, and H-CPG at 10 min. (<b>K</b>) Standard curve of CuS@PDA@GOx. (<b>L</b>) CPG release profiles of H-CPG at (pH = 7.4) and (pH = 6.5). (<b>M</b>) Degradation of glucose by H-CPG hydrogels. (Mean ± SD (<span class="html-italic">n</span> = 3) ). ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>In vitro antitumor activity of hydrogels: (<b>A</b>) Live/dead staining images of B16F10 cells after various treatments. Scale bar: 100 µm. (<b>B</b>) Cell viability of B16F10 cells after different materials treatments. (<b>C</b>) Migration images of B16F10 cells after different materials treatments. Scale bar: 100 µm. (<b>D</b>) Visualized mobility of B16F10 cells after different materials treatments. (<b>E</b>) Flow-through apoptosis images of B16F10 cells after different materials treatments. (Mean ± SD (<span class="html-italic">n</span> = 3)). ns indicates no significant difference. ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>(<b>A</b>) Intracellular H<sub>2</sub>S levels after different treatments using WSP-5 as a probe. Scale bar: 200 µm. (<b>B</b>) Mean fluorescence intensity of H<sub>2</sub>S corresponding to A. (<b>C</b>) Microscopic measurements of intracellular mitochondrial damage by JC-1 probe. Scale bar: 100 µm. (<b>D</b>) Mean fluorescence intensity of JC-monomers corresponding to Figure B. (<b>E</b>) Mean fluorescence intensity of JC-aggregates corresponding to Figure B. (<b>F</b>) By staining with Dichloride hydro fluorescein diacetate (DCFH-DA) probe to assess the ROS levels of B16F10 cells after different materials treatments. Scale bar: 200 µm. (<b>G</b>) Fluorescence intensity of the DCFH-DA probe corresponding to Figure E. (<b>H</b>) ROS levels of B16F10 cells were analyzed by flow cytometry. (Mean ± SD (<span class="html-italic">n</span> = 3)), ns indicates no significant difference. ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>H-CPG-induced immunotherapy: (<b>a</b>) Immunofluorescence of TAM cells after different materials treatments. Scale bar: 100 µm. (<b>b</b>) Percentage of CD206 positive cells corresponding to Figure A. (<b>c</b>) Percentage of CD86 positive cells corresponding to Figure A. (<b>d</b>) Percentage of CD86/CD206 cells corresponding to Figure A. (<b>e</b>) Flow chart of TAMs cells after different materials treatments. (Mean ± SD (<span class="html-italic">n</span> = 3)). *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Evaluation of the antimicrobial effect: (<b>a</b>) Antimicrobial effect of different materials against <span class="html-italic">E. Coil</span>. The arrow points to the damaged bacterial structure. Scale bar: 2 µm. (<b>b</b>) Relative survival of <span class="html-italic">E. coli</span> corresponding to Figure a. (<b>c</b>) Antimicrobial effect of different materials against <span class="html-italic">S. aureus</span>. Scale bar: 2 µm. (<b>d</b>) Relative survival of <span class="html-italic">S. aureus</span> corresponding to Figure d. (Mean ± SD (n = 3). *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>(<b>A</b>) Postoperative treatment protocols for tumors in mice in vivo. (<b>B</b>) Postoperative tumor resection model. (<b>C</b>) Thermograms of Control, H, and H-CPG group mice after 10 min of irradiation with 808 nm near-infrared radiation (1.0 W/cm<sup>2</sup>). (<b>D</b>) Tumor and wound repair in mice after 18 days of treatment, The red circle part represents the tumor size and wound distribution after 18 days of treatment. (<b>E</b>) Temperature distribution range corresponding to Figure C. (<b>F</b>) Tumor size in mice after 18 days of treatment. (<b>G</b>) Corresponds to (<b>F</b>) the size of the tumor volume. (Mean ± SD (<span class="html-italic">n</span> = 5)). *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>(<b>A</b>) Photographs associated with histological analysis of tumor cells after 18 days of treatment. The red arrow represents the tumor vascular marker. Scale bar: 200 µm. (<b>B</b>) Percentage of CD206 immune cells after treatment with different drugs.(<b>C</b>) Percentage of IL-6 immune cells after treatment with different drugs. (<b>D</b>) Percentage of TNF-α immune cells after treatment with different drugs. (Mean ± SD (<span class="html-italic">n</span> = 5)), ns indicates no significant difference. * <span class="html-italic">p</span> &lt; 0.05, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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30 pages, 2989 KiB  
Review
Metal Peroxide Nanoparticles for Modulating the Tumor Microenvironment: Current Status and Recent Prospects
by Jagadeesh Rajaram and Yaswanth Kuthati
Cancers 2024, 16(21), 3581; https://doi.org/10.3390/cancers16213581 - 24 Oct 2024
Viewed by 1088
Abstract
Background: The significant expansion of nanobiotechnology and nanomedicine has led to the development of innovative and effective techniques to combat various pathogens, demonstrating promising results with fewer adverse effects. Metal peroxide nanoparticles stand out among the crucial yet often overlooked types of nanomaterials, [...] Read more.
Background: The significant expansion of nanobiotechnology and nanomedicine has led to the development of innovative and effective techniques to combat various pathogens, demonstrating promising results with fewer adverse effects. Metal peroxide nanoparticles stand out among the crucial yet often overlooked types of nanomaterials, including metals. These nanoparticles are key in producing oxygen (O2) and hydrogen peroxide (H2O2) through simple chemical reactions, which are vital in treating various diseases. These compounds play a crucial role in boosting the effectiveness of different treatment methods and also possess unique properties due to the addition of metal ions. Methods: This review discusses and analyzes some of the most common metal peroxide nanoparticles, including copper peroxide (CuO2), calcium peroxide (CaO2), magnesium peroxide (MgO2), zinc peroxide (ZnO2), barium peroxide (BaO2), and titanium peroxide (TiOx) nanosystems. These nanosystems, characterized by their greater potential and treatment efficiency, are primarily needed in nanomedicine to combat various harmful pathogens. Researchers have extensively studied the effects of these peroxides in various treatments, such as catalytic nanotherapeutics, photodynamic therapy, radiation therapy, and some combination therapies. The tumor microenvironment (TME) is particularly unique, making the impact of nanomedicine less effective or even null. The presence of high levels of reactive oxygen species (ROS), hypoxia, low pH, and high glutathione levels makes them competitive against nanomedicine. Controlling the TME is a promising approach to combating cancer. Results: Metal peroxides with low biodegradability, toxicity, and side effects could reduce their effectiveness in treating the TME. It is important to consider the distribution of metal peroxides to effectively target cancer cells while avoiding harm to nearby normal cells. As a result, modifying the surface of metal peroxides is a key strategy to enhance their delivery to the TME, thereby improving their therapeutic benefits. Conclusions: This review discussed the various aspects of the TME and the importance of modifying the surface of metal peroxides to enhance their therapeutic advantages against cancer, as well as address safety concerns. Additionally, this review covered the current challenges in translating basic research findings into clinical applications of therapies based on metal peroxide nanoparticles. Full article
(This article belongs to the Topic Nanomaterials and Diseases)
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<p>Illustrates various metal peroxides, including copper peroxide (CuO<sub>2</sub>), calcium peroxide (CaO<sub>2</sub>), magnesium peroxide (MgO<sub>2</sub>), zinc peroxide (ZnO<sub>2</sub>), barium peroxide (BaO<sub>2</sub>), and titanium peroxide (TiOx).</p>
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<p>The characteristic features of the TME, such as low pH, hypoxia, and an immunosuppressive environment.</p>
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<p>Schematic diagram of the mechanism of CDT for cancer treatment.</p>
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<p>(<b>A</b>) Schematic illustration of the preparation of self-assembled copper–amino acid mercaptide nanoparticles (Cu-Cys NPs). In situ glutathione-activated and H<sub>2</sub>O<sub>2</sub>-reinforced CDTs were achieved using the Cu-Cys NPs with the help of the Fenton reaction +. Reprinted with permission from Ref. [<a href="#B138-cancers-16-03581" class="html-bibr">138</a>]. 2019, AMERICAN CHEMICAL SOCIETY. (<b>B</b>) Schematic illustration of CuO<sub>2</sub>@G5-BS/TF nanocomplexes against the TME. The nanocomplex achieves cell death by Enhanced CDT, Ferroptosis, and Cuprotosis due to the presence of Fe<sup>3+</sup> and Cu<sup>2+</sup>. Reprinted with permission from Ref. [<a href="#B142-cancers-16-03581" class="html-bibr">142</a>]. 2024.</p>
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<p>(<b>A</b>) Schematic illustration of a hyaluronic acid-modified calcium and copper peroxide nanocomposite against the TME. Enhanced tumor therapy was achieved through the synergistic effect of a Fenton-like reaction by Cu<sup>2+</sup> and mitochondria dysfunction by Ca<sup>2+</sup> in ROS generation through the nanocomposite. Reprinted with permission from Ref. [<a href="#B144-cancers-16-03581" class="html-bibr">144</a>]. 2022, AMERICAN CHEMICAL SOCIETY”. (<b>B</b>) Schematic illustration of a CaO<sub>2</sub>-Cu/ICG@PCM nanoplatform against the TME. The nanocomplex achieves bioimaging and tumor therapy through a combinational treatment of PDT, CDT, and calcium overload, along with CT imaging. Reprinted with permission from Ref. [<a href="#B154-cancers-16-03581" class="html-bibr">154</a>]. 2021, ELSEVIER, ELSEVIER.</p>
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<p>(<b>a</b>). Schematic illustration of PAA–TiOx nanocomplexes against tumors. After take-up by the cancer cells, the nanocomplex achieves cell death by radiotherapy. “Reprinted with permission from Ref. [<a href="#B159-cancers-16-03581" class="html-bibr">159</a>]. 2021, ELSEVIER” (<b>b</b>). A. Schematic illustration of Fe-ZnO<sub>2</sub>@HA nanoparticles shows the changes in the rigid extracellular membrane after Fe-ZnO<sub>2</sub>@HA enters into the tumor environment. B. Fe-ZnO<sub>2</sub>@HA represents the mechanism of action of Fe-ZnO<sub>2</sub>@HA to induce apoptosis through Ferroptosis and Pyroptosis. “Reprinted with permission from Ref. [<a href="#B162-cancers-16-03581" class="html-bibr">162</a>]. 2024, AMERICAN CHEMICAL SOCIETY”.</p>
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19 pages, 4427 KiB  
Article
Reduction of Trinitrobenzene to Amines with Molecular Hydrogen over Chrysocolla-like Catalysts
by Olga A. Kirichenko, Elena V. Shuvalova, Gennady I. Kapustin, Nikolay A. Davshan, Igor V. Mishin and Leonid M. Kustov
Catalysts 2024, 14(10), 686; https://doi.org/10.3390/catal14100686 - 2 Oct 2024
Viewed by 855
Abstract
The cheap non-noble Cu–SiO2-based nanocatalysts are under intensive study in different reactions resulting in useful chemicals, yet their application in environment protection is poorly studied. In the present work, the influence of the Cu loading (3–15 wt%) on the catalytic behavior [...] Read more.
The cheap non-noble Cu–SiO2-based nanocatalysts are under intensive study in different reactions resulting in useful chemicals, yet their application in environment protection is poorly studied. In the present work, the influence of the Cu loading (3–15 wt%) on the catalytic behavior of Cu/SiO2 materials was first precisely studied in the hydrogenation of hazardous trinitrobenzene to valuable aromatic amines with molecular hydrogen. The catalysts have been synthesized by the method of deposition–precipitation using urea. The catalyst characterization by XRD, TPR-H2, SEM, TEM, and N2 adsorption methods confirmed that they include nanoparticles of the micro-mesoporous chrysocolla-like phase supported in the mesopores of a commercial SiO2 carrier, as well as revealed formation of the highly dispersed CuO phase in the sample with the highest Cu loading. Variation in reaction conditions showed the optimal ones (170 °C, 1.3 MPa H2) resulting in complete trinitrobenzene conversion with a triaminobenzene yield of 65% for the catalyst with a 15% Cu loading, and the best yield of 82% was obtained over the catalyst with 10% Cu calcined at 600 °C. The results show the potential of Cu phyllosilicate-based catalysts for the utilization of trinitroaromatic compounds via catalytic hydrogenation to amines and their possible applications in a remediation treatment system. Full article
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<p>Dependence of the TNB conversion (X) and selectivity to individual amines (S<sub>i</sub>) versus the reaction time for the sample 8Cu–300 at different reaction conditions: (<b>a</b>) T = 150 °C, P = 1.3 MPa; (<b>b</b>) T = 170 °C, P = 1.3 MPa; (<b>c</b>) T = 170 °C, P = 0.5 MPa.</p>
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<p>Dependence of the TNB conversion (X) and selectivity to individual amines (S<sub>i</sub>) versus the reaction time for the sample 10Cu-T after different thermal treatments: (<b>a</b>) drying at 110 °C; (<b>b</b>) calcination at 300 °C; (<b>c</b>) calcination at 600 °C. Reaction conditions: T = 170 °C, P = 1.3 MPa.</p>
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<p>Dependence of the TNB conversion (X) and selectivity to individual amines (S<sub>i</sub>) versus the reaction time for the samples calcined at 300 °C with a different Cu loading: (<b>a</b>) 3%; (<b>b</b>) 6%; (<b>c</b>) 9%; (<b>d</b>) 15%. Reaction conditions: T = 170 °C, P = 1.3 MPa.</p>
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<p>XRD patterns of the catalysts 15Cu-300 calcined at 300 °C, and 10Cu-600 calcined at 600 °C.</p>
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<p>SEM image at low magnification and SEM-EDX maps of element distribution for the catalyst 10Cu-110.</p>
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<p>SEM image at high magnification (<b>a</b>) and TEM images (<b>b</b>,<b>c</b>) of the sample 10Cu-110.</p>
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<p>SEM image at high magnification (<b>a</b>) and TEM images (<b>b</b>–<b>d</b>) of the sample 10Cu-300.</p>
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<p>The mesopore size distribution curves (<b>a</b>,<b>b</b>) and the micropore size distribution curves (<b>c</b>,<b>d</b>) for the Cu<sub>2</sub>Si<sub>2</sub>O<sub>5</sub>(OH)<sub>2</sub>/SiO<sub>2</sub> catalysts differed in Cu loading (<b>a</b>,<b>c</b>) and in the temperature of thermal treatment (<b>b</b>,<b>d</b>).</p>
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<p>TPR profiles of the Cu<sub>2</sub>Si<sub>2</sub>O<sub>5</sub>(OH)<sub>2</sub>/SiO<sub>2</sub> catalysts dried and calcined at different temperatures (<b>a</b>) and differed in Cu loading (<b>b</b>), as well as CuO supported on silica by the deposition-precipitation procedure using urea thermal hydrolysis (8Cu/SiO<sub>2</sub>-DP).</p>
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<p>Scheme of possible catalyst transformations under reaction conditions and the associated catalytic formation of amines.</p>
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<p>Simplified scheme of 1,3,5-trinitrobenzene hydrogenation to amines.</p>
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12 pages, 3233 KiB  
Article
Development of Robust CuNi Bimetallic Catalysts for Selective Hydrogenation of Furfural to Furfuryl Alcohol under Mild Conditions
by Deqin He, Zheng Liang, Juwen Gu, Xuechun Sang, Yujia Liu and Songbai Qiu
Catalysts 2024, 14(10), 683; https://doi.org/10.3390/catal14100683 - 2 Oct 2024
Viewed by 1203
Abstract
Furfuryl alcohol represents a pivotal intermediate in the high-value utilization of renewable furfural, derived from agricultural residues. The industrial-scale hydrogenation of furfural to furfuryl alcohol typically employs Cu-based catalysts, but their limited catalytic activity necessitates high-temperature and high-pressure conditions. Here, we develop robust [...] Read more.
Furfuryl alcohol represents a pivotal intermediate in the high-value utilization of renewable furfural, derived from agricultural residues. The industrial-scale hydrogenation of furfural to furfuryl alcohol typically employs Cu-based catalysts, but their limited catalytic activity necessitates high-temperature and high-pressure conditions. Here, we develop robust CuNi bimetallic catalysts through direct calcination of dried sol–gel precursors under H2 atmosphere, enabling the complete conversion of furfural to furfuryl alcohol under mild conditions. By adjusting the calcination atmosphere and introducing small amounts of Ni, we achieve the formation of highly dispersed, ultrasmall Cu nanoparticles, resulting in a significant enhancement of the catalytic activity. The optimized 0.5%Ni-10%Cu/SiO2-CA(H2) catalyst demonstrates superior catalytic performance, achieving 99.4% of furfural conversion and 99.9% of furfuryl alcohol selectivity, respectively, at 55 °C under 2 MPa H2, outperforming previously reported Cu-based catalysts. The excellent performance of CuNi bimetallic catalysts can be attributed to the highly dispersed Cu nanoparticles and the synergistic effect between Cu and Ni for H2 activation. This research contributes to the rational design of Cu-based catalysts for the selective hydrogenation of furfural. Full article
(This article belongs to the Special Issue Catalytic Conversion of Biomass to Chemicals)
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<p>Schematic illustration for the synthesis of 0.5%Ni-10%Cu/SiO<sub>2</sub>-CA.</p>
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<p>XRD patterns of (<b>a</b>) 10%Cu/SiO<sub>2</sub>-CA and (<b>b</b>) 0.5%Ni-10%Cu/SiO<sub>2</sub>-CA calcined in different atmospheres.</p>
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<p>HAADF-STEM images (<b>a</b>,<b>b</b>) and element mapping images (<b>c</b>–<b>e</b>) of 0.5%Ni-10%Cu/SiO<sub>2</sub>-CA(H<sub>2</sub>) catalyst.</p>
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<p>In situ XPS spectra of typical samples: (<b>a</b>) full—scan; (<b>b</b>) Ni 2p; (<b>c</b>) Cu 2p; (<b>d</b>) Cu LMM AES.</p>
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<p>Catalytic performance of 10%Cu/SiO<sub>2</sub>-CA with different influencing factors: (<b>a</b>) different heat treatments (55 °C, 4 MPa H<sub>2</sub>), (<b>b</b>) different reaction temperatures (4 MPa H<sub>2</sub>), and (<b>c</b>) different reaction pressures (55 °C). Catalytic performance of x%Ni-10%Cu/SiO<sub>2</sub>-CA(H<sub>2</sub>) with different influencing factors: (<b>d</b>) different Ni contents (55 °C, 4 MPa H<sub>2</sub>), (<b>e</b>) different reaction temperatures (4 MPa H<sub>2</sub>), and (<b>f</b>) different reaction pressures (55 °C). Reaction conditions: 0.32 g furfural, 2.88 g isopropanol, 0.089 g catalyst, 2 h.</p>
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<p>(<b>a</b>,<b>b</b>) H<sub>2</sub>-TPR and (<b>c</b>,<b>d</b>) H<sub>2</sub>-TPD profiles of typical samples.</p>
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<p>Furfural-TPD/MS profiles over the 10%Cu/SiO<sub>2</sub>-CA and 0.5%Ni-10%Cu/SiO<sub>2</sub>-CA catalysts.</p>
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16 pages, 9304 KiB  
Article
Novel Synthesis Route of Plasmonic CuS Quantum Dots as Efficient Co-Catalysts to TiO2/Ti for Light-Assisted Water Splitting
by Larissa Chaperman, Samiha Chaguetmi, Bingbing Deng, Sarra Gam-Derrouich, Sophie Nowak, Fayna Mammeri and Souad Ammar
Nanomaterials 2024, 14(19), 1581; https://doi.org/10.3390/nano14191581 - 30 Sep 2024
Cited by 1 | Viewed by 913
Abstract
Self-doped CuS nanoparticles (NPs) were successfully synthesized via microwave-assisted polyol process to act as co-catalysts to TiO2 nanofiber (NF)-based photoanodes to achieve higher photocurrents on visible light-assisted water electrolysis. The strategy adopted to perform the copper cation sulfidation in polyol allowed us [...] Read more.
Self-doped CuS nanoparticles (NPs) were successfully synthesized via microwave-assisted polyol process to act as co-catalysts to TiO2 nanofiber (NF)-based photoanodes to achieve higher photocurrents on visible light-assisted water electrolysis. The strategy adopted to perform the copper cation sulfidation in polyol allowed us to overcome the challenges associated with the copper cation reactivity and particle size control. The impregnation of the CuS NPs on TiO2 NFs synthesized via hydrothermal corrosion of a metallic Ti support resulted in composites with increased visible and near-infrared light absorption compared to the pristine support. This allows an improved overall efficiency of water oxidation (and consequently hydrogen generation at the Pt counter electrode) in passive electrolyte (pH = 7) even at 0 V bias. These low-cost and easy-to-achieve composite materials represent a promising alternative to those involving highly toxic co-catalysts. Full article
(This article belongs to the Special Issue Photofunctional Nanomaterials and Nanostructures)
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<p>Home-made single-compartment quartz PEC cell, working in a classic 3-electrode configuration, using Ag/AgCl RE and Pt CE.</p>
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<p>XRD patterns of as-prepared CuS-TiO<sub>2</sub>/Ti, TiO<sub>2</sub>/Ti, and CuS. The peak positions of TiO<sub>2</sub> (anatase), Ti (α), and CuS (covellite) references are given for information.</p>
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<p>Top view SEM micrographs of TiO<sub>2</sub>/Ti (<b>a</b>) before and (<b>b</b>) after CuS impregnation, highlighting the presence of an additional contrast at some TiO<sub>2</sub> fiber nodes.</p>
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<p>SEM-EDX analysis of the CuS-TiO<sub>2</sub>/Ti sample: (<b>a</b>) Z-contrasting top view SEM micrograph highlighting a TiO<sub>2</sub> fiber noddle on which an assembly of CuS particles is aggregated, (<b>b</b>) EDS chemical mapping confirming the copper and sulfur element co-concentration in the selected area, in agreement with the presence of CuS particles.</p>
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<p>(<b>a</b>) Survey XPS spectra of CuS-TiO<sub>2</sub>/Ti (brown line), TiO<sub>2</sub>/Ti (black line), and CuS (blue-green line). (<b>b</b>) Cu 2p and S 1s XPS high-resolution spectra of CuS-TiO<sub>2</sub>/Ti (brown line).</p>
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<p>UV-Vis-NIR absorption spectra of (<b>a</b>) CuS-TiO<sub>2</sub>/Ti and TiO<sub>2</sub>/Ti recorded in total reflectance mode compared to (<b>b</b>) that of pristine CuS recorded in transmission. (<b>c</b>,<b>d</b>) The Tauc plots inferred from the previous data are given for band-gap determination. The lamp change from UV to visible range during spectra acquisition proceeded at 320 nm.</p>
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<p>(<b>a</b>) Linear sweep voltammetry (10 mV.s<sup>−1</sup>) and (<b>b</b>) chronoamperometry of TiO<sub>2</sub>/Ti (black line) are CuS-TiO<sub>2</sub>/Ti (blue-green line) in a passive Na<sub>2</sub>SO<sub>4</sub> (0.5 M) electrolyte.</p>
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<p>General scheme of the energy band diagram of bulk TiO<sub>2</sub> anatase and CuS covellite versus the normal hydrogen electrode (NHE), highlighting the reaction of their VB holes with water molecules to produce O<sub>2</sub> and the collection of their CB electrons for their transfer to the external circuit in a standard PEC cell. To build this diagram, band gap energies and band positions versus NHE of anatase TiO<sub>2</sub> and covellite CuS were inferred from [<a href="#B54-nanomaterials-14-01581" class="html-bibr">54</a>,<a href="#B55-nanomaterials-14-01581" class="html-bibr">55</a>], respectively.</p>
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<p>Linear sweep voltammetry (10 mV.s<sup>−1</sup>) of CuS-TiO<sub>2</sub>/Ti (blue-green line), CdS-TiO<sub>2</sub>/Ti (green line), and TiO<sub>2</sub>/Ti (black line) in a passive Na<sub>2</sub>SO<sub>4</sub> (0.5 M) electrolyte, focusing on the 0 to 1.5 V bias range.</p>
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