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15 pages, 5711 KiB  
Article
Engineering Nonvolatile Polarization in 2D α-In2Se3/α-Ga2Se3 Ferroelectric Junctions
by Peipei Li, Delin Kong, Jin Yang, Shuyu Cui, Qi Chen, Yue Liu, Ziheng He, Feng Liu, Yingying Xu, Huiyun Wei, Xinhe Zheng and Mingzeng Peng
Nanomaterials 2025, 15(3), 163; https://doi.org/10.3390/nano15030163 - 22 Jan 2025
Abstract
The advent of two-dimensional (2D) ferroelectrics offers a new paradigm for device miniaturization and multifunctionality. Recently, 2D α-In2Se3 and related III–VI compound ferroelectrics manifest room-temperature ferroelectricity and exhibit reversible spontaneous polarization even at the monolayer limit. Here, we employ first-principles [...] Read more.
The advent of two-dimensional (2D) ferroelectrics offers a new paradigm for device miniaturization and multifunctionality. Recently, 2D α-In2Se3 and related III–VI compound ferroelectrics manifest room-temperature ferroelectricity and exhibit reversible spontaneous polarization even at the monolayer limit. Here, we employ first-principles calculations to investigate group-III selenide van der Waals (vdW) heterojunctions built up by 2D α-In2Se3 and α-Ga2Se3 ferroelectric (FE) semiconductors, including structural stability, electrostatic potential, interfacial charge transfer, and electronic band structures. When the FE polarization directions of α-In2Se3 and α-Ga2Se3 are parallel, both the α-In2Se3/α-Ga2Se3 P↑↑ (UU) and α-In2Se3/α-Ga2Se3 P↓↓ (NN) configurations possess strong built-in electric fields and hence induce electron–hole separation, resulting in carrier depletion at the α-In2Se3/α-Ga2Se3 heterointerfaces. Conversely, when they are antiparallel, the α-In2Se3/α-Ga2Se3 P↓↑ (NU) and α-In2Se3/α-Ga2Se3 P↑↓ (UN) configurations demonstrate the switchable electron and hole accumulation at the 2D ferroelectric interfaces, respectively. The nonvolatile characteristic of ferroelectric polarization presents an innovative approach to achieving tunable n-type and p-type conductive channels for ferroelectric field-effect transistors (FeFETs). In addition, in-plane biaxial strain modulation has successfully modulated the band alignments of the α-In2Se3/α-Ga2Se3 ferroelectric heterostructures, inducing a type III–II–III transition in UU and NN, and a type I–II–I transition in UN and NU, respectively. Our findings highlight the great potential of 2D group-III selenides and ferroelectric vdW heterostructures to harness nonvolatile spontaneous polarization for next-generation electronics, nonvolatile optoelectronic memories, sensors, and neuromorphic computing. Full article
(This article belongs to the Special Issue Advanced 2D Materials for Emerging Application)
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Figure 1

Figure 1
<p>Two-dimensional α-In<sub>2</sub>Se<sub>3</sub>/α-Ga<sub>2</sub>Se<sub>3</sub> ferroelectric heterostructures: top views of (<b>a</b>) UU and UN, (<b>b</b>) NU and NN, and side views of (<b>c</b>) UU, (<b>d</b>) NU, (<b>e</b>) UN, and (<b>f</b>) NN. The purple, orange, and green represent In, Ga, and Se atoms, respectively. The black arrow represents the direction of polarization.</p>
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<p>Projected band structures and DOS of (<b>a</b>) α-In<sub>2</sub>Se<sub>3</sub> and (<b>b</b>) α-Ga<sub>2</sub>Se<sub>3</sub>. The Fermi level is set as 0.</p>
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<p>Projected band structures and DOS of the α-In<sub>2</sub>Se<sub>3</sub>/α-Ga<sub>2</sub>Se<sub>3</sub> heterojunctions in (<b>a</b>) UU, (<b>b</b>) UN, (<b>c</b>) NU, and (<b>d</b>) NN. The blue and red marks represent the contributions of the α-In<sub>2</sub>Se<sub>3</sub> and α-Ga<sub>2</sub>Se<sub>3</sub> layers on projected band structures and projected density of states, respectively. The Fermi level is set as 0.</p>
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<p>Planar average electrostatic potentials of (<b>a</b>) α-In<sub>2</sub>Se<sub>3</sub>, (<b>b</b>) α-Ga<sub>2</sub>Se<sub>3</sub>, (<b>c</b>) NN, (<b>d</b>) UN, (<b>e</b>) NU, and (<b>f</b>) NN configurations heterostructures along the z-direction, respectively. In (<b>c</b>–<b>f</b>), the blue and red parts of the electrostatic potential curves correspond to α-In<sub>2</sub>Se<sub>3</sub> and α-Ga<sub>2</sub>Se<sub>3</sub>, respectively. The arrows indicate the directions of the polarization-induced electric fields. The vacuum levels and Fermi levels are marked with blue, red, and gray dashed lines, respectively.</p>
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<p>Plane-averaged charge density differences of the α-In<sub>2</sub>Se<sub>3</sub>/α-Ga<sub>2</sub>Se<sub>3</sub> heterostructures in (<b>a</b>) UU, (<b>b</b>) UN, (<b>c</b>) NU, and (<b>d</b>) NN configurations along the z-direction, respectively. The red dashed lines represent the α-In<sub>2</sub>Se<sub>3</sub>/α-Ga<sub>2</sub>Se<sub>3</sub> interface boundaries.</p>
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<p>(<b>a</b>) The n-channel and (<b>b</b>) p-channel FeFET devices based on NU and UN configurations, respectively. The red arrow represents that the FeFET channel current flows in opposite directions. The “P” represents the ferroelectric polarization directions controlled by the top and bottom gates.</p>
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<p>Projected band structures of (<b>a</b>) UU, (<b>b</b>) UN, (<b>c</b>) NU, and (<b>d</b>) NN heterostructures under biaxial strains of −10%, −6%, 0%, 6%, and 10%, in which the blue and red colors indicate the contributions of α-In<sub>2</sub>Se<sub>3</sub> and α-Ga<sub>2</sub>Se<sub>3</sub>, respectively.</p>
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<p>CBM and VBM values of α-In<sub>2</sub>Se<sub>3</sub>/α-Ga<sub>2</sub>Se<sub>3</sub> heterostructures in (<b>a</b>) UU, (<b>b</b>) UN, (<b>c</b>) NU, and (<b>d</b>) NN configurations as a function of the in-plane biaxial strain, respectively. The red (blue) solid lines and dashed lines represent the CBMs and VBMs of α-Ga<sub>2</sub>Se<sub>3</sub> (α-In<sub>2</sub>Se<sub>3</sub>), respectively. The orange, gray, and light green regions denote type-I, type-II, and type-III band structures, respectively.</p>
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16 pages, 2852 KiB  
Article
Smart Zinc-Based Coatings with Chitosan–Alginate Nanocontainers Loaded with ZnO and Caffeine for Corrosion Protection of Mild Steel
by Kamelia Kamburova, Nelly Boshkova, Tsetska Radeva and Nikolai Boshkov
Metals 2025, 15(1), 65; https://doi.org/10.3390/met15010065 - 13 Jan 2025
Viewed by 444
Abstract
The development of environmentally friendly materials is a subject of increasing interest in corrosion protection research. The objective of the present investigation is to propose the preparation procedure of chitosan–alginate (CHI/ALG) nanocontainers loaded with zinc oxide (ZnO) nanoparticles or combining ZnO nanoparticles with [...] Read more.
The development of environmentally friendly materials is a subject of increasing interest in corrosion protection research. The objective of the present investigation is to propose the preparation procedure of chitosan–alginate (CHI/ALG) nanocontainers loaded with zinc oxide (ZnO) nanoparticles or combining ZnO nanoparticles with corrosion inhibitor caffeine (CAF), both suitable for incorporation into the matrix of ordinary zinc coatings on mild steel substrates. The nanocontainers were synthesized through spontaneous polysaccharide complexation in the presence of ZnO nanoparticles and CAF using a cross-linking agent, namely tripolyphosphate (TPP). Dynamic light scattering and laser Doppler velocimetry measurements are used for evaluation of the size distribution and zeta potentials of the nanocontainers, both loaded or unloaded with CAF. Using UV-spectroscopy, entrapment efficiency and release amounts of CAF are quantitatively evaluated. The nanocontainers thus obtained were incorporated into the matrices of ordinary zinc coatings via co-electrodeposition with zinc from zinc sulfate solution, aiming to improve the corrosion protection of steel in corrosive environments containing chloride ions. The surface morphology and elemental composition of the electrodeposited hybrid coatings before and after treating in the model corrosive medium of 3.5% NaCl is studied by scanning electron microscopy (SEM). The cyclic voltammetry method (CVA) is applied to characterize the cathodic (electrodeposition) and anodic (dissolution) processes. The protective characteristics of the hybrid coatings are investigated by application of potentiodynamic polarization (PDP) curves and polarization resistance (Rp) measurements after a time interval of 40 days. The obtained results indicate that both hybrid coating types could prolong the life time of mild steel in aggressive Cl ion-containing solution, combining the protection effect of sacrificial zinc with barrier (ZnO) and active (CAF) protective effects. Full article
(This article belongs to the Special Issue Advances in Corrosion and Failure Analysis of Metallic Materials)
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Figure 1
<p>Hydrodynamic diameter (d) distributions and zeta potential values for the CHI/ALG nanocontainers with ZnO nanoparticles unloaded (1) and loaded (2) with CAF.</p>
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<p>Cumulative release of CAF from ZnO/CHI/ALG-CAF nanocontainers in 3.5% NaCl solutions at pH 7, pH 5, and pH 4. Inset: Calibration curve for CAF. Error: ~5% (±1–2 mg/L).</p>
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<p>SEM micrographs of hybrid zinc coatings containing ZnO/CHI/ALG nanocontainers unloaded with CAF before (<b>A</b>) and after (<b>B</b>) 40 days’ immersion in 3.5% NaCl solution.</p>
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<p>SEM micrographs and EDS spectra for hybrid zinc coatings with ZnO/CHI/ALG nanocontainers loaded with CAF before (<b>A.1</b>,<b>A.2</b>) and after (<b>B.1</b>,<b>B.2</b>) 40 days’ immersion in 3.5% NaCl solution.</p>
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<p>CVA investigations of ordinary zinc (Zn) and of both types of hybrid coatings with nanocontainers unloaded (ZnO) and loaded with CAF (ZnO CAF).</p>
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<p>PD polarization curves of the coatings in the test medium of 3.5% NaCl solution. Reference electrode–saturated calomel electrode.</p>
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<p>Polarization resistance measurements of the zinc (1) and both hybrid coatings without CAF (2) and with CAF (3) in a 3.5% NaCl solution. Error of ±10%.</p>
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<p>Water contact angle measurements for both hybrid zinc coatings.</p>
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<p>Preparation of the ZnO/CHI/ALG nanocontainers loaded with corrosion inhibitor CAF, followed by electrodeposition of hybrid zinc coating on steel substrate.</p>
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15 pages, 4723 KiB  
Article
A Modified Regular Perturbation Model for the Single-Span Fiber Transmission Using Learnable Methods
by Shuhong He, Zhongya Li, Sizhe Xing, An Yan, Yingjun Zhou, Jianyang Shi, Chao Shen, Ziwei Li, Zhixue He, Wei Chen, Nan Chi and Junwen Zhang
Photonics 2024, 11(12), 1178; https://doi.org/10.3390/photonics11121178 - 14 Dec 2024
Viewed by 647
Abstract
In fiber optic communication systems, the dispersion and nonlinear interaction of optical signals are critical to modeling fiber optic communication, and the regular perturbation (RP) model is a simplified modeling method composed of parallel branches, which has obvious advantages in deep learning backpropagation. [...] Read more.
In fiber optic communication systems, the dispersion and nonlinear interaction of optical signals are critical to modeling fiber optic communication, and the regular perturbation (RP) model is a simplified modeling method composed of parallel branches, which has obvious advantages in deep learning backpropagation. In this paper, we propose a simplified single-mode fiber signal transmission model based on the RP model, which significantly improves the fitting accuracy of the model for dispersion and nonlinear interactions at the same complexity by adding trainable parameters to the standard RP model. We explain in the paper that this improvement is applicable to dual-polarization systems and still effective under the conditions of large launch power, without dispersion management, and containing amplified spontaneous emission (ASE) noise. The model uses the standard split-step Fourier method (SSFM) to generate labels and updates parameters through gradient descent method. When transmitting a dual-polarization signal with a launch power of 13 dBm, the modified regular perturbation (MRP) model proposed in the paper can reduce the fitting errors by more than 75% compared to the standard RP model after transmitting through a 120 km standard single-mode fiber. Full article
(This article belongs to the Special Issue Machine Learning Applied to Optical Communication Systems)
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<p>Conceptual diagram of the single-span fiber communication system.</p>
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<p>Conceptual diagram of MRP model structure and training method.</p>
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<p>Complexity of MRP models under different conditions: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>s</mi> <mi>t</mi> <mi>e</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math> and (<b>b</b>) FFT length.</p>
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<p>The setup of the system simulated by the MRP model.</p>
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<p>RMSE (<b>a</b>) and EVM (<b>b</b>) of the MRP and RP models under different branch numbers.</p>
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<p>Constellation diagrams comparison between SSFM and (<b>a</b>) MRP or (<b>b</b>) RP models.</p>
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<p>Comparison of waveforms and MSE between SSFM and RP or MRP models.</p>
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<p>RMSE of the MRP and RP models under a different number of branches.</p>
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<p>EVM of the MRP and RP models (<b>a</b>) w/o and (<b>b</b>) w/ ASE noise under a different number of branches, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>s</mi> <mi>t</mi> <mi>e</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math> (initial SNR = 37 dB, AWGN power = 0.01 mW).</p>
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<p>(<b>a</b>) RMSE and (<b>b</b>) EVM of the MRP and RP models under different fiber lengths (launch power = 13 dBm, MRP branches number <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>s</mi> <mi>t</mi> <mi>e</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math> = 20).</p>
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<p>(<b>a</b>) RMSE and (<b>b</b>) EVM of the MRP and RP models under different roll-off factors (fiber length = 120 km, launch power = 13 dBm, branches number <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>s</mi> <mi>t</mi> <mi>e</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math> = 20).</p>
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<p>Computational complexity and ΔEVM of the SSFM and MRP or RP models (fiber length = 120 km, launch power = 13 dBm, sequence length <span class="html-italic">S =</span> 8192).</p>
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19 pages, 4317 KiB  
Article
Comparison of Interactions Between Soy Protein Isolate and Three Folate Molecules: Effect on the Stabilization, Degradation, and Oxidization of Folates and Protein
by Linlin He, Yuqian Yan, Dandan Song, Shuangfeng Li, Yanna Zhao, Zhuang Ding and Zhengping Wang
Foods 2024, 13(24), 4033; https://doi.org/10.3390/foods13244033 - 13 Dec 2024
Viewed by 716
Abstract
This study selected three approved folate sources—folic acid (FA), L-5-methyltetrahydrofolate (MTFA), and calcium 5-methyltetrahydrofolate (CMTFA)—to explore their interaction mechanisms with soy protein isolate (SPI) through spectrofluorometric analysis and molecular docking simulations. We investigated how these interactions influence the structural and physicochemical stability of [...] Read more.
This study selected three approved folate sources—folic acid (FA), L-5-methyltetrahydrofolate (MTFA), and calcium 5-methyltetrahydrofolate (CMTFA)—to explore their interaction mechanisms with soy protein isolate (SPI) through spectrofluorometric analysis and molecular docking simulations. We investigated how these interactions influence the structural and physicochemical stability of folates and SPI. Three folates spontaneously bound to SPI, forming complexes, resulting in a decrease of approximately 30 kJ·mol−1 in Gibbs free energy and an association constant (Ka) of 105 L·mol−1. The thermodynamic parameters and molecular docking study revealed the unique binding mechanisms of FA and MTFA with SPI. FA’s planar pteridine ring and conjugated double bonds facilitate hydrophobic interactions, whereas MTFA’s reduced ring structure and additional polar groups strengthen hydrogen bonding. Although the formation of SPI–folate complexes did not result in substantial alterations to the SPI structure, their binding has the potential to enhance both the physical and thermal stability of the protein by stabilizing its conformation. Notably, compared with free FA, the FA-SPI complexes significantly enhanced FA’s stability, exhibiting 71.1 ± 1.2% stability under light conditions after 9 days and 63.2 ± 2.6% stability in the dark after 60 days. In contrast, no similar effect was observed for MTFA. This discrepancy can be ascribed to the distinct degradation pathways of the Fa and MTFA molecules. This study offers both theoretical and experimental insights into the development of folate-loaded delivery systems utilizing SPI as a matrix. Full article
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Figure 1
<p>Fluorescence emission spectra of 5 g/L soy protein isolation (SPI) in the presence of folic acid (FA), (<b>A</b>) L-5-methyltetrahydrofolate (MTFA), (<b>B</b>) and MTFA with 25 μM calcium ion (Ca<sup>2+</sup>) (<b>C</b>) at different concentrations ranging from 0 to 25 μM (T = 298.2 K, pH = 7.4, λex = 280 nm). (<b>D</b>–<b>F</b>) Stern–Volmer plots of the interactions between SPI and three folate molecules at 298.2–308.2 K.</p>
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<p>(<b>A</b>) Surface hydrophobicity (H<sub>0</sub>) of soy protein isolation (SPI) and SPI–folate complexes. (<b>B</b>) Linearity curve plotting the relative fluorescence intensity of the ANS probe with concentrations from 0 to 1.5 g/L of the SPI and SPI–folate complexes. FA, MTFA, and CMTFA denote folic acid, L-5-methyltetrahydrofolate, and calcium L-5-methyltetrahydrofolate, respectively.</p>
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<p>(<b>A</b>) Fourier transform infrared (FTIR) spectra and (<b>B</b>) curve-fitted analysis of the amide I region (1600–1700 cm<sup>−1</sup>) for soy protein isolate (SPI) and SPI–folate complexes. The table in (<b>B</b>) summarizes protein secondary structure contents derived from integrating the area under each band in the curve-fitting analysis.</p>
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<p>Binding model diagram of folic acid (FA) and L-5-methyltetrahydrofolate (MTFA) to soy β-conglycinin (7S globulins) (<b>A</b>,<b>C</b>) and glycinin (11S globulins) (<b>B</b>,<b>D</b>). The left and right images show the binding site and the interactions between folates and surrounding amino acid residues, respectively.</p>
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<p>Differential scanning calorimetry (DSC) thermograms of soy protein isolation (SPI) (<b>A</b>) and three SPI–folate complexes (<b>B</b>–<b>D</b>).</p>
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<p>Stability of folic acid (FA) (<b>A</b>,<b>D</b>), L-5-methyltetrahydrofolate (MTFA) (<b>B</b>,<b>E</b>), and calcium L-5-methyltetrahydrofolate (CMTFA) (<b>C</b>,<b>F</b>) in the absence and presence of soy protein isolation (SPI) under light and dark conditions at 25 °C.</p>
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<p>Liquid chromatography chromatograms of the soy protein isolation (SPI)-L-5-methyltetrahydrofolate (MTFA) complexes after 3 days of light (<b>A</b>) and 7 days of dark storage (<b>B</b>) at 25 °C. (<b>C</b>) Degradation products of MTFA during storage and their corresponding <span class="html-italic">m/z</span> values determined via mass spectrometry.</p>
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<p>(<b>A</b>) Variation in sulfhydryl content of SPI and SPI-FA complexes over 9 days under light exposure and 60 days under dark storage conditions. (<b>B</b>) Variation in sulfhydryl content of SPI and SPI-MTFA/CMTFA complexes over 3 days under light exposure and 7 days under dark storage conditions.</p>
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26 pages, 596 KiB  
Review
Axial-Vector and Tensor Spin Polarization and Chiral Restoration in Quark Matter
by Tomoyuki Maruyama and Toshitaka Tatsumi
Symmetry 2024, 16(12), 1642; https://doi.org/10.3390/sym16121642 - 11 Dec 2024
Viewed by 569
Abstract
We study spontaneous the spin polarization of quark matter with flavor SU(2) symmetry at zero temperature in the NJL model. In a relativistic framework, there are two types of spin–spin interactions: axial vector (AV) and tensor (T), which accordingly [...] Read more.
We study spontaneous the spin polarization of quark matter with flavor SU(2) symmetry at zero temperature in the NJL model. In a relativistic framework, there are two types of spin–spin interactions: axial vector (AV) and tensor (T), which accordingly give rise to different types of spin-polarized materials. When the spin–spin interaction is sufficiently strong, the spin-polarized phase emerges within a specific density region. As the spin–spin interaction becomes stronger, this phase extends over a higher-density region beyond the critical density of chiral restoration in normal quark matter. We show that the spin-polarized phase leads to another kind of spontaneous chiral symmetry breaking phase. Full article
(This article belongs to the Special Issue Chiral Symmetry, and Restoration in Nuclear Dense Matter)
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Figure 1
<p>Contour plots of the single-particle energy minus its minimum value in the <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>y</mi> </msub> <mo>−</mo> <msub> <mi>p</mi> <mi>z</mi> </msub> </mrow> </semantics></math> plane (<math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>) when <math display="inline"><semantics> <mrow> <msub> <mi>U</mi> <mi>A</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> (<b>a</b>), <math display="inline"><semantics> <mrow> <msub> <mi>U</mi> <mi>A</mi> </msub> <mo>=</mo> <mn>1.4</mn> <msub> <mi>M</mi> <mi>q</mi> </msub> </mrow> </semantics></math> (<b>b</b>), and <math display="inline"><semantics> <mrow> <msub> <mi>U</mi> <mi>A</mi> </msub> <mo>=</mo> <mn>2.8</mn> <msub> <mi>M</mi> <mi>q</mi> </msub> </mrow> </semantics></math> (<b>c</b>). The red solid and blue dashed lines show the results for the major spin states (<math display="inline"><semantics> <mrow> <mi>s</mi> <mo>=</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>) and that for the minor spin states (<math display="inline"><semantics> <mrow> <mi>s</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>).</p>
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<p>The energy constant surfaces for <math display="inline"><semantics> <mrow> <mi>e</mi> <mo>=</mo> <mn>2.5</mn> <msub> <mi>M</mi> <mi>q</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>=</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>, when <math display="inline"><semantics> <mrow> <msub> <mi>U</mi> <mi>A</mi> </msub> <mo>=</mo> <mn>2.8</mn> <msub> <mi>M</mi> <mi>q</mi> </msub> </mrow> </semantics></math> (<b>a</b>) and when <math display="inline"><semantics> <mrow> <msub> <mi>U</mi> <mi>T</mi> </msub> <mo>=</mo> <mn>2.8</mn> <msub> <mi>M</mi> <mi>q</mi> </msub> </mrow> </semantics></math> (<b>b</b>).</p>
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<p>AV-type spin polarization characteristics with PM1. In upper panels (<b>a</b>,<b>c</b>,<b>e</b>), the tensor densities are normalized to the normal nuclear density. In lower panels (<b>b</b>,<b>d</b>,<b>f</b>), the dynamical quark mass is normalized by the nucleon mass for the spin-polarized (red solid lines) and spin-saturated (blue dashed line) phases. Results for <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>A</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>1.25</mn> </mrow> </semantics></math> (<b>a</b>,<b>b</b>), <math display="inline"><semantics> <mrow> <mo>=</mo> <mo>−</mo> <mn>1.5</mn> </mrow> </semantics></math> (<b>c</b>,<b>d</b>), and <math display="inline"><semantics> <mrow> <mo>=</mo> <mo>−</mo> <mn>1.75</mn> </mrow> </semantics></math> (<b>e</b>,<b>f</b>) are shown in the left, middle, and right panels, respectively.</p>
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<p>AV-type spin polarization characteristics with PM2. In upper panels (<b>a</b>,<b>c</b>,<b>e</b>), the AV densities are normalized to the normal nuclear density. In lower panels (<b>b</b>,<b>d</b>,<b>f</b>), the dynamical quark mass is normalized by the nucleon mass, shown for the spin-polarized (red solid lines) and spin-saturated (blue dashed line) phases. Results for <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>A</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>1.1</mn> </mrow> </semantics></math> (<b>a</b>,<b>b</b>), <math display="inline"><semantics> <mrow> <mo>=</mo> <mo>−</mo> <mn>1.25</mn> </mrow> </semantics></math> (<b>c</b>,<b>d</b>), and <math display="inline"><semantics> <mrow> <mo>=</mo> <mo>−</mo> <mn>1.75</mn> </mrow> </semantics></math> (<b>e</b>,<b>f</b>) are shown in the left, middle, and right panels, respectively.</p>
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<p>T-type spin polarization characteristics with PM1. In upper panels (<b>a</b>,<b>c</b>,<b>e</b>), the tensor densities are normalized to the normal nuclear density. The solid and dotted lines correspond to the results in the massive quark and massless quark phases, respectively. In lower panels (<b>b</b>,<b>d</b>,<b>f</b>), the dynamical quark mass is normalized by the nucleon mass, shown for the spin-polarized (red solid lines) and spin-saturated (blue dashed line) phases. The left, middle, and right panels present the results for <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>T</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>1.25</mn> <msub> <mi>G</mi> <mi>s</mi> </msub> </mrow> </semantics></math> (<b>a</b>,<b>b</b>), <math display="inline"><semantics> <mrow> <mo>=</mo> <mo>−</mo> <mn>1.5</mn> <msub> <mi>G</mi> <mi>s</mi> </msub> </mrow> </semantics></math> (<b>c</b>,<b>d</b>), and <math display="inline"><semantics> <mrow> <mo>=</mo> <mo>−</mo> <mn>1.75</mn> <msub> <mi>G</mi> <mi>s</mi> </msub> </mrow> </semantics></math> (<b>e</b>,<b>f</b>), respectively.</p>
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<p>T-type spin polarization characteristics with PM2. In upper panels (<b>a</b>,<b>c</b>,<b>e</b>), the tensor densities are normalized to the normal nuclear density. The solid and dotted lines correspond to the results in the massive quark and massless quark phases, respectively. In lower panels (<b>b</b>,<b>d</b>,<b>f</b>), the dynamical quark mass is normalized by the nucleon mass for the spin-polarized (red solid lines) and spin-saturated (blue dashed line) phases. The left, middle, and right panels show the results for <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>T</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>0.9</mn> <msub> <mi>G</mi> <mi>s</mi> </msub> </mrow> </semantics></math> (<b>a</b>,<b>b</b>), <math display="inline"><semantics> <mrow> <mo>=</mo> <mo>−</mo> <mn>1.25</mn> <msub> <mi>G</mi> <mi>s</mi> </msub> </mrow> </semantics></math> (<b>c</b>,<b>d</b>), and <math display="inline"><semantics> <mrow> <mo>=</mo> <mo>−</mo> <mn>1.5</mn> <msub> <mi>G</mi> <mi>s</mi> </msub> </mrow> </semantics></math> (<b>e</b>,<b>f</b>), respectively.</p>
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<p>Spin polarization ratio with PM1. The upper panels show the results for the AV type when <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>A</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>1.25</mn> <msub> <mi>G</mi> <mi>s</mi> </msub> </mrow> </semantics></math> (<b>a</b>), <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>A</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>1.5</mn> <msub> <mi>G</mi> <mi>s</mi> </msub> </mrow> </semantics></math> (<b>b</b>), and <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>A</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>1.75</mn> <msub> <mi>G</mi> <mi>s</mi> </msub> </mrow> </semantics></math> (<b>c</b>). Rhe lower panels show the results for the AV type when <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>T</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>1.25</mn> <msub> <mi>G</mi> <mi>s</mi> </msub> </mrow> </semantics></math> (<b>d</b>), <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>T</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>1.5</mn> <msub> <mi>G</mi> <mi>s</mi> </msub> </mrow> </semantics></math> (<b>e</b>), and <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>A</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>1.75</mn> <msub> <mi>G</mi> <mi>s</mi> </msub> </mrow> </semantics></math> (<b>f</b>). In the lower panel, the solid and dotted lines represent the results in the massive and massless spin-polarized phases, respectively.</p>
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<p>Tensor density in the AV-type spin-polarized phase with <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>A</mi> </msub> <mo>/</mo> <msub> <mi>G</mi> <mi>s</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>1.25</mn> </mrow> </semantics></math> (dotted line), <math display="inline"><semantics> <mrow> <mo>=</mo> <mo>−</mo> <mn>1.5</mn> </mrow> </semantics></math> (solid line), and <math display="inline"><semantics> <mrow> <mo>=</mo> <mo>−</mo> <mn>1.75</mn> </mrow> </semantics></math> (dashed line) in the PM1 parameter sets.</p>
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16 pages, 10842 KiB  
Article
Dynamic Variation in the Semiconductive Tendency of the Passive Film on Duplex Stainless Steel in Corrosion Environments
by Seung-Heon Choi, Young-Ran Yoo and Young-Sik Kim
Materials 2024, 17(23), 5963; https://doi.org/10.3390/ma17235963 - 5 Dec 2024
Viewed by 508
Abstract
Duplex stainless steels, known for their excellent corrosion resistance, are employed in a variety of chloride solutions—acidic, neutral, and alkaline—due to a stable passive film that forms on their surfaces. This study involved polarization tests, EIS (Electrochemical Impedance Spectroscopy) measurements, Mott–Schottky plots, and [...] Read more.
Duplex stainless steels, known for their excellent corrosion resistance, are employed in a variety of chloride solutions—acidic, neutral, and alkaline—due to a stable passive film that forms on their surfaces. This study involved polarization tests, EIS (Electrochemical Impedance Spectroscopy) measurements, Mott–Schottky plots, and XPS (X-Ray Photoelectron Spectroscopy) analyses in both static and dynamic conditions across acidic (1NaCl + 0.1N HCl, pH 1.0), neutral (1N NaCl, pH 6.7), and alkaline (1N NaCl + 0.1N NaOH, pH 13.2) chloride solutions to confirm that duplex stainless steels exhibit similar passivation behavior (0.79 μA/cm2 > ip > 0.2 μA/cm2 and 590 kΩ·cm2 < Rp < 651 kΩ·cm2). Regardless of the pH of the solution, p-type and n-type semiconductive properties were observed, but the balance of the semiconductive tendencies was different. Comparing passive films formed under dynamic conditions, through real-time HCl injection into a neutral chloride solution, with those formed under static conditions, revealed that both conditions yield similar structural and property characteristics in the films, as well as comparable electrochemical behaviors. These findings suggest that the passive film on the stainless steel surface adjusts to the environment and can be spontaneously repassivated in response to environmental changes. Full article
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Figure 1
<p>Optical microstructure and EBSD analysis of duplex stainless steel: (<b>a</b>) OM (×200), (<b>b</b>) band contrast (×700), and (<b>c</b>) phase color (×700).</p>
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<p>Randles model [<a href="#B40-materials-17-05963" class="html-bibr">40</a>].</p>
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<p>Effect of corrosive environments on the polarization curves of duplex stainless steel.</p>
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<p>EIS results in the passive film of duplex stainless steel formed at −100 mV (SCE) under various conditions: (<b>a</b>) Bode plot, (<b>b</b>) Nyquist plot, and (<b>c</b>) polarization resistance by CPE model.</p>
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<p>Effect of corrosive environment on the Mott–Schottky behavior of passive film on duplex stainless steel formed at −100 mV (SCE): (<b>a</b>) Mott–Schottky plot, (<b>b</b>) pH vs. total defect density, and (<b>c</b>) pH vs. p-type or n-type semiconductive tendency.</p>
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<p>(<b>a</b>,<b>a’</b>,<b>a”</b>) Fe 2p<sub>3/2</sub>, (<b>b</b>,<b>b’</b>,<b>b”</b>) Cr 2p<sub>3/2</sub>, (<b>c</b>,<b>c’</b>,<b>c”</b>) Ni 2p<sub>3/2</sub>, (<b>d</b>,<b>d’</b>,<b>d”</b>) Mo (3d<sub>5/2</sub> + 3d<sub>3/2</sub>), and (<b>e</b>,<b>e’</b>,<b>e”</b>) O 1s elemental composition of outer and inner layers of passive films under (<b>a</b>–<b>e</b>) acidic, (<b>a’</b>–<b>e’</b>) neutral, and (<b>a”</b>–<b>e”</b>) alkaline chloride solution.</p>
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<p>[M-O]/(M + [M-O]) ratio and Cr<sub>2</sub>O<sub>3</sub>/Cr(OH)<sub>3</sub> ratio for the outer and inner layers of the passive film formed in the acidic, neutral, and alkaline chloride environments ([M-O] means metal-oxide): (<b>a</b>) ratio of [M-O]/(M + [M-O]) and (<b>b</b>) ratio of Cr<sub>2</sub>O<sub>3</sub>/Cr(OH)<sub>3</sub>.</p>
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<p>Effect of static and dynamic solution conditions on the polarization behavior of duplex stainless steel.</p>
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<p>Effect of static and dynamic solution conditions on the AC impedance of duplex stainless steel formed at −100 mV (SCE): (<b>a</b>) Bode plot, (<b>b</b>) Nyquist plot, and (<b>c</b>) polarization resistance by CPE model.</p>
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<p>Effect of static and dynamic solution conditions on the Mott–Schottky characteristics of the passive film formed on duplex stainless steel at −100 mV (SCE): (<b>a</b>) Mott–Schottky plot, (<b>b</b>) pH vs. total defect density, and (<b>c</b>) pH vs. p-type or n-type semiconductive tendency.</p>
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<p>(<b>a</b>,<b>a’</b>) Fe 2p<sub>3/2</sub>, (<b>b</b>,<b>b’</b>) Cr 2p<sub>3/2</sub>, (<b>c</b>,<b>c’</b>) Ni 2p<sub>3/2</sub>, (<b>d</b>,<b>d’</b>) Mo (3d<sub>5/2</sub> + 3d<sub>3/2</sub>), and (<b>e</b>,<b>e’</b>) O 1s elemental composition of outer and inner layers of passive films under (<b>a</b>–<b>e</b>) static condition and (<b>a’</b>–<b>e’</b>) dynamic condition.</p>
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<p>Comparison of [M-O]/(M + [M-O]) and Cr<sub>2</sub>O<sub>3</sub>/Cr(OH)<sub>3</sub> ratios for outer and inner layers of passive films formed under static and dynamic conditions: (<b>a</b>) ratio of [M-O]/(M + [M-O]) and (<b>b</b>) ratio of Cr<sub>2</sub>O<sub>3</sub>/Cr(OH)<sub>3</sub>.</p>
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15 pages, 4134 KiB  
Article
Nanostructured Hydrogels of Carboxylated Cellulose Nanocrystals Crosslinked by Calcium Ions
by Alexander S. Ospennikov, Yuri M. Chesnokov, Andrey V. Shibaev, Boris V. Lokshin and Olga E. Philippova
Gels 2024, 10(12), 777; https://doi.org/10.3390/gels10120777 - 28 Nov 2024
Viewed by 754
Abstract
Bio-based eco-friendly cellulose nanocrystals (CNCs) gain an increasing interest for diverse applications. We report the results of an investigation of hydrogels spontaneously formed by the self-assembly of carboxylated CNCs in the presence of CaCl2 using several complementary techniques: rheometry, isothermal titration calorimetry, [...] Read more.
Bio-based eco-friendly cellulose nanocrystals (CNCs) gain an increasing interest for diverse applications. We report the results of an investigation of hydrogels spontaneously formed by the self-assembly of carboxylated CNCs in the presence of CaCl2 using several complementary techniques: rheometry, isothermal titration calorimetry, FTIR-spectroscopy, cryo-electron microscopy, cryo-electron tomography, and polarized optical microscopy. Increasing CaCl2 concentration was shown to induce a strong increase in the storage modulus of CNC hydrogels accompanied by the growth of CNC aggregates included in the network. Comparison of the rheological data at the same ionic strength provided by NaCl and CaCl2 shows much higher dynamic moduli in the presence of CaCl2, which implies that calcium cations not only screen the repulsion between similarly charged nanocrystals favoring their self-assembly, but also crosslink the polyanionic nanocrystals. Crosslinking is endothermic and driven by increasing entropy, which is most likely due to the release of water molecules surrounding the interacting COO and Ca2+ ions. The hydrogels can be easily destroyed by increasing the shear rate because of the alignment of rodlike nanocrystals along the direction of flow and then quickly recover up to 90% of their viscosity in 15 s, when the shear rate is decreased. Full article
(This article belongs to the Special Issue Advances in Cellulose-Based Hydrogels (3rd Edition))
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<p>(<b>a</b>) Frequency dependencies of storage G′ (filled symbols) and loss G″ (open symbols) moduli for suspensions containing 3 wt% CNCs and different concentrations of CaCl<sub>2</sub>; (<b>b</b>) frequency dependencies of storage G′ (filled) and loss G″ (open) moduli for 3 wt% suspensions of CNCs with 50 mM CaCl<sub>2</sub> (circles) and with 150 mM NaCl (triangles), providing the same ionic strength.</p>
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<p>Storage modulus G′ (circles) and loss modulus G″ (squares) at the oscillatory frequency of 1 rad/s as a function of CaCl<sub>2</sub> concentration.</p>
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<p>(<b>a</b>) Viscosity recovery after periodic variation of shear rate (50 s<sup>−1</sup> for 60 s, 0.1 s<sup>−1</sup> for 60 s, etc.) for suspensions containing 3 wt% CNCs and 36 mM (green) and 72 mM (red) of CaCl<sub>2</sub>; (<b>b</b>) fitting of the viscosity recovery with the exponential function for the second cycle of periodic variation of shear rate for 3 wt% CNC suspensions with 36 mM (green) and 72 mM (red) of CaCl<sub>2</sub>. In the formula, η is the apparent viscosity, t is time, τ is the recovery time, a,b are coefficients.</p>
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<p>(<b>a</b>–<b>d</b>) Cryo-EM images of 3 wt% CNC suspensions before (<b>a</b>,<b>c</b>) and after addition of 50 mM CaCl<sub>2</sub> (<b>b</b>,<b>d</b>) for thinner (<b>a</b>,<b>b</b>) and thicker (<b>c</b>,<b>d</b>) samples. Some domains containing CNCs oriented parallel to each other are marked by ovals. Bundles are marked by yellow arrows, fibrillar-like aggregates are marked by red arrows.</p>
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<p>(<b>a</b>,<b>b</b>) Three-dimensional surface-rendered views of the arrangement of CNCs inside the network before (<b>a</b>) and after addition of 50 mM CaCl<sub>2</sub> (<b>b</b>) obtained from cryo-ET. The bundle is indicated by a yellow arrow, and a fragment of the fibrillar-like aggregate is marked by the red oval.</p>
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<p>Histograms of distribution of the length (<b>a</b>) and thickness (<b>b</b>) of individual CNCs and their aggregates in 3 wt% CNC suspensions before (green) and after (red) addition of 50 mM CaCl<sub>2</sub>.</p>
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<p>ITC titration curve of 3 wt% CNC dispersion with CaCl<sub>2</sub> at pH 6.5.</p>
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<p>(<b>a</b>) ATR-FTIR spectra of CNCs without salt (black) and with 72 mM CaCl<sub>2</sub> (red) in the dried state. The spectra are offset in the <span class="html-italic">y</span>-axis for viewing clarity. (<b>b</b>) ATR-FTIR spectra of 6 wt% suspensions of CNCs without salt (black) and with 72 mM CaCl<sub>2</sub> (red) in water. The spectra are offset in the <span class="html-italic">y</span>-axis for viewing clarity.</p>
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<p>(<b>a</b>–<b>c</b>) Polarized optical microscopy images of CNC suspensions with different concentrations of nanocrystals: 2.5 wt% (<b>a</b>), 3 wt% (<b>b</b>) and 4 wt% (<b>c</b>); (<b>d</b>–<b>f</b>) polarized optical microscopy images of 3 wt% aqueous suspensions of CNCs with different concentrations of added CaCl<sub>2</sub>: 9 mM (<b>d</b>), 18 mM (<b>e</b>) and 36 mM (<b>f</b>).</p>
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<p>(<b>a</b>–<b>c</b>) Polarized optical microscopy images of CNC suspensions with different concentrations of nanocrystals: 2.5 wt% (<b>a</b>), 3 wt% (<b>b</b>) and 4 wt% (<b>c</b>); (<b>d</b>–<b>f</b>) polarized optical microscopy images of 3 wt% aqueous suspensions of CNCs with different concentrations of added CaCl<sub>2</sub>: 9 mM (<b>d</b>), 18 mM (<b>e</b>) and 36 mM (<b>f</b>).</p>
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16 pages, 9727 KiB  
Article
Teratoma Development in 129.MOLF-Chr19 Mice Elicits Two Waves of Immune Cell Infiltration
by Lucas Klaus, Sybille D. Reichardt, Maria Neif, Lutz Walter, Fabian A. Gayer and Holger M. Reichardt
Int. J. Mol. Sci. 2024, 25(23), 12750; https://doi.org/10.3390/ijms252312750 - 27 Nov 2024
Viewed by 674
Abstract
Teratomas are a highly differentiated type of testicular germ cell tumors (TGCTs), the most common type of solid cancer in young men. Prominent inflammatory infiltrates are a hallmark of TGCTs, although their compositions and dynamics in teratomas remain elusive. Here, we reached out [...] Read more.
Teratomas are a highly differentiated type of testicular germ cell tumors (TGCTs), the most common type of solid cancer in young men. Prominent inflammatory infiltrates are a hallmark of TGCTs, although their compositions and dynamics in teratomas remain elusive. Here, we reached out to characterize the infiltrating immune cells and their activation and polarization state by using high-throughput gene expression analysis of 129.MOLF-Chr19 mice that spontaneously develop testicular teratomas. We showed that inconspicuous testes without any apparent alterations in size or morphology can be clustered into three groups based on their expression of stemness and immune genes, supporting a model in which initial oncogenic transformation elicits a first wave of T-cell infiltration. Moderately and severely enlarged tumorous testes then displayed a progressive infiltration with T cells, monocytes/macrophages, and B cells. Importantly, T cells seem to adopt an inactive state caused by an overexpression of immune checkpoint molecules and the polarization of monocytes/macrophages to an anti-inflammatory phenotype. Our findings are supported by the analysis of metabolic gene expression, which unveiled alterations indicative of tumor growth and immune cell infiltration. Collectively, testicular teratomas, at least in mice, are characterized by a diverse inflammatory infiltrate containing T cells that putatively become inactivated, allowing the tumors to further grow. We believe that these findings may provide a rationale for the development of new immunomodulatory therapies for TGCTs. Full article
(This article belongs to the Special Issue Current Research on Cancer Biology and Therapeutics: Third Edition)
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<p>Expression of genes related to neoplastic transformation and immunity in M19 testes at different stages of tumorigenesis. Gene expression of (<b>A</b>) <span class="html-italic">Pou5f1</span> and <span class="html-italic">Myc</span>, (<b>B</b>) <span class="html-italic">Cd3e</span> and <span class="html-italic">Adgre1</span>, and (<b>C</b>) <span class="html-italic">Tnfa</span> and <span class="html-italic">Il6</span> were determined in testes of 129Sv wildtype mice and the three groups of M19 mice using high-throughput Fluidigm<sup>®</sup> Dynamic Arrays and RT-qPCR (N = 10/26/17/21). Expression of the housekeeping gene <span class="html-italic">Rn18s</span> was used for normalization, and mRNA levels in wildtype testes were set to 1. All values are depicted as scatter dot plots with bars representing the mean ± SEM; each dot corresponds to an individual testis. Statistical analysis was performed using a Kruskal–Wallis test followed by Dunn’s multiple comparison test. Levels of significance: *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001; n.s.: <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>Bioinformatic and histological analysis of inconspicuous testes of M19 mice. (<b>A</b>) <span class="html-italic">K-Means Clustering</span> of inconspicuous testes into three groups (Clusters 1–3) was achieved based on the relative expression levels of <span class="html-italic">Pou5f1</span> and <span class="html-italic">Cd3e</span> as determined by using high-throughput Fluidigm<sup>®</sup> Dynamic Arrays and RT-qPCR (N = 26). Z-Score normalization was used to calculate the relative expression of <span class="html-italic">Pou5f1</span> and <span class="html-italic">Cd3e</span>. (<b>B</b>) Overview and magnifications of an H&amp;E-stained section of an exemplified inconspicuous testis from an M19 mouse (N = 3). Small areas representing tissue differentiations like cartilage (CA) and neuroparenchyma-like tissue (NP) distributed amongst healthy seminiferous tubules with maturing spermatogenesis are depicted in the inlets. The scale bar (2 mm) refers to the overview microphotograph.</p>
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<p>Expression of genes related to tumorigenesis and T cell function in inconspicuous testes of M19 mice. Expression levels of (<b>A</b>) <span class="html-italic">Pou5f1</span> and <span class="html-italic">Myc</span>, (<b>B</b>) <span class="html-italic">Cd3e, Il2, Ifng,</span> and <span class="html-italic">Grmb</span>, and (<b>C</b>) <span class="html-italic">Pd1</span> and <span class="html-italic">Pdl1</span> were determined in the three clusters of inconspicuous testes by high-throughput Fluidigm<sup>®</sup> Dynamic Arrays and RT-qPCR (N = 5–8). <span class="html-italic">Rn18s</span> expression was employed for normalization, and mRNA levels in testes of “Cluster 1” were set to 1. All values are depicted as scatter dot plots with bars representing the mean ± SEM; each dot corresponds to an individual testis. Statistical analysis was performed using a Kruskal–Wallis test followed by Dunn’s multiple comparison test. Levels of significance: *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001; n.s.: <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>Expression of genes related to monocytes/macrophages and B cells in inconspicuous testes of M19 mice. Expression levels of (<b>A</b>) <span class="html-italic">Adgre1</span> and <span class="html-italic">Cd19</span>, and (<b>B</b>) <span class="html-italic">Cd206</span>, <span class="html-italic">Arg1</span>, <span class="html-italic">H2aa</span>, and <span class="html-italic">Cd163</span> were determined in the three clusters of inconspicuous testes by high-throughput Fluidigm<sup>®</sup> Dynamic Arrays and RT-qPCR (N = 5–8). <span class="html-italic">Rn18s</span> expression was used for normalization, and mRNA levels in testes of “Cluster 1” were set to 1. Values are depicted as scatter dot plots with bars representing the mean ± SEM; each dot corresponds to an individual testis. Statistical analysis was performed using a Kruskal–Wallis test followed by Dunn’s multiple comparison test. Levels of significance: *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; n.s.: <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>T cell gene signature of overt teratomas in M19 mice. Expression levels of (<b>A</b>) <span class="html-italic">Il2</span>, <span class="html-italic">Cd25</span>, <span class="html-italic">Ifng</span>, and <span class="html-italic">Grzb</span>, and (<b>B</b>) <span class="html-italic">Pd1</span> and <span class="html-italic">Pdl1</span> were determined in testes of 129Sv wildtype mice and M19 testes with moderate or severe signs of teratogenesis using high-throughput Fluidigm<sup>®</sup> Dynamic Arrays and RT-qPCR (N = 6–17). <span class="html-italic">Rn18s</span> expression was used for normalization, and mRNA levels in wildtype testes were set to 1. Values are depicted as scatter dot plots with bars representing the mean ± SEM; each dot corresponds to an individual testis. Statistical analysis was performed using a Kruskal–Wallis test followed by Dunn’s multiple comparison test. Levels of significance: *: <span class="html-italic">p</span> &lt; 0.05; n.s.: <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>Monocyte/macrophage gene signature of overt teratomas in M19 mice. Expression levels of <span class="html-italic">H2aa</span>, <span class="html-italic">Cx3cr1</span>, <span class="html-italic">Mmp9</span>, <span class="html-italic">Arg1</span>, <span class="html-italic">Cd163</span>, and <span class="html-italic">Cd206</span> were determined in testes of 129Sv wildtype mice and M19 testes with moderate or severe signs of teratogenesis using high-throughput Fluidigm<sup>®</sup> Dynamic Arrays and RT-qPCR (N = 6–17). <span class="html-italic">Rn18s</span> expression was used for normalization, and mRNA levels in wildtype testes were set to 1. Values are depicted as scatter dot plots with bars representing the mean ± SEM; each dot corresponds to an individual testis. Statistical analysis was performed using a Kruskal–Wallis test followed by Dunn’s multiple comparison test. Levels of significance: *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001; n.s.: <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>Immunohistochemical analysis of T cells. (<b>A</b>–<b>C</b>) T cells were visualized in paraffin sections prepared from an exemplary overt teratoma of M19 mice (N = 3) using a primary anti-CD3 antibody in combination with a polymeric secondary antibody, DAB as a substrate, and hematoxylin for counterstaining. Representative photomicrographs of tissue differentations found in the teratoma acquired at 20× magnification are depicted. (<b>A</b>) T cells were located at the border of tumerous tissue, (<b>B</b>) in differentiated skeletal muscle, and (<b>C</b>) neuroparenchyma-like tissue (indicated by arrows). (<b>D</b>) T cells were visualized in paraffin sections of a testis obtained from a 129Sv wildtype mouse (N = 3) and located in the interstitial space (indicated by arrows). Positive and negative staining controls are depicted in <a href="#app1-ijms-25-12750" class="html-app">Supplementary Figure S1</a>. Scale bar = 200 µm in all panels.</p>
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<p>Metabolic gene signature of M19 testes at different stages of teratogenesis. Expression levels of <span class="html-italic">Glut1</span>, <span class="html-italic">Ldha</span>, <span class="html-italic">Pfkfb3</span>, <span class="html-italic">Glut3</span>, <span class="html-italic">Aldoa</span>, and <span class="html-italic">Hk2</span> were determined in testes of 129Sv wildtype mice, inconspicuous testes of M19 mice, and M19 testes with moderate or severe signs of teratogenesis by high-throughput Fluidigm<sup>®</sup> Dynamic Arrays and RT-qPCR (N = 7–21). <span class="html-italic">Rn18s</span> expression was used for normalization, and mRNA levels in wildtype testes were set to 1. Values are depicted as scatter dot plots with bars representing the mean ± SEM; each dot corresponds to an individual testis. Statistical analysis was performed using a Kruskal–Wallis test followed by Dunn’s multiple comparison test. Levels of significance: *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; n.s.: <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>PCA followed by <span class="html-italic">K-Means clustering</span> of gene expression during teratogenesis in M19 mice. (<b>A</b>) Clustering of testis samples based on <span class="html-italic">Ifng</span>, <span class="html-italic">Grzb</span>, <span class="html-italic">Pd1</span>, and <span class="html-italic">Pdl1</span> expression. (<b>B</b>) Clustering of testis samples based on <span class="html-italic">Glut1</span>, <span class="html-italic">Ldha</span>, <span class="html-italic">Pfkfb3</span>, <span class="html-italic">Glut3</span>, and <span class="html-italic">Aldoa</span> expression. Z-Score normalization was used to calculate relative expression levels. The colors refer to the two/three separate clusters that were identified by unsupervised <span class="html-italic">K-Means clustering</span>. Testes from 129Sv wildtype mice and inconspicuous testes of M19 mice are displayed as dots, and moderately and severely tumorous testes from M19 mice are displayed as cross marks. The vectors in the inserts indicate the contribution of each analyzed gene to both principal components.</p>
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25 pages, 3366 KiB  
Article
Spontaneous Symmetry Breaking, Group Decision-Making, and Beyond: 1. Echo Chambers and Random Polarization
by Serge Galam
Symmetry 2024, 16(12), 1566; https://doi.org/10.3390/sym16121566 - 22 Nov 2024
Viewed by 1044
Abstract
Starting from a symmetrical multiple-choice individual, I build a sociophysics model of decision-making. Reducing the choices to two and interactions to pairs recovers the Ising model from physics at zero temperature. The associated equilibrium state results from a spontaneous symmetry breaking, with the [...] Read more.
Starting from a symmetrical multiple-choice individual, I build a sociophysics model of decision-making. Reducing the choices to two and interactions to pairs recovers the Ising model from physics at zero temperature. The associated equilibrium state results from a spontaneous symmetry breaking, with the whole group sharing a unique choice, which is selected at random. However, my focus departs from physics, which aims at identifying the true equilibrium state, discarding any possible impact of the initial conditions, the size of the sample, and the update algorithm used. Memory of past history is erased. In contrast, I claim that dealing with a social system, the history of the system must be taken into account in identifying the relevant social equilibrium state, which is always biased by its history. Accordingly, using Monte Carlo simulations, I explore the spectrum of non-universal equilibrium states of the Ising model at zero temperature. In particular, I show that different initial conditions with the same value of the order parameter lead to different equilibrium states. The same applies for different sizes and different update algorithms. The results indicate that in the presence of a social network composed of agents sharing different initial opinions, it is their interactions that lead them to share a unique choice and not their mere membership in the network. This finding sheds a new light on the emergence of echo chambers, which appear to be the end of a dynamical process of opinion update and not its beginning with a preferential attachment. Furthermore, polarization is obtained as a side effect of the random selection of the respective unanimous choices of the various echo chambers within a social community. The study points to social media exchange algorithms, which are purely technical levers independent of the issue and opinions at stake, to tackle polarization by either hindering or accelerating the completion of symmetry breaking between agents. Full article
(This article belongs to the Section Physics)
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Figure 1
<p>Results of three simulations using a random update. Sub-cases (<b>a</b>–<b>c</b>) represent three different distributions (Seed = 10, 70, 50) of spins <math display="inline"><semantics> <mrow> <mo>±</mo> <mn>1</mn> </mrow> </semantics></math> (450 <math display="inline"><semantics> <mrow> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math> in red, 450 <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> in blue) with the same initial value zero for their respective order parameters. Sub-case (<b>a</b>) shows a full symmetry breaking along <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>, which is achieved after about 150 Monte Carlo steps (Seed = 10). Sub-case (<b>b</b>) shows a full symmetry breaking along <math display="inline"><semantics> <mrow> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math> after less than 100 Monte Carlo steps (Seed = 70). Sub-case (<b>c</b>) shows a full symmetry breaking along <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> after about 750 Monte Carlo steps (Seed = 50). However, in this case, the order parameter has been positive during almost 500 Monte Carlo first steps before starting to turn negative to eventually reach a full negative symmetry breaking. Sub-cases (<b>d</b>–<b>f</b>) show the respective initial distribution of the three samples with zero order parameter associated with (<b>a</b>–<b>c</b>). Sub-cases (<b>g</b>,<b>j</b>), (<b>h</b>,<b>k</b>), (<b>i</b>,<b>l</b>) show related intermediate snapshots toward full symmetry breaking for the three samples (<b>d</b>–<b>f</b>).</p>
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<p>Results of two simulations using a random update with initial distributions of spins (Seed = 40, 90) different than in <a href="#symmetry-16-01566-f001" class="html-fig">Figure 1</a> (Seed = 10, 50, 70). However, contrary to <a href="#symmetry-16-01566-f001" class="html-fig">Figure 1</a>, these two distributions lead to final states with no full symmetry breaking as exhibited in sub-cases (<b>a</b>,<b>c</b>). Indeed two domains of opposite distributions are found in the final equilibrium state as seen in the sub-cases (<b>b</b>,<b>d</b>). In both sub-cases, the domains are of different sizes (magnetization −0.0667 versus 0.267).</p>
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<p>Results of simulations using a random update with initial distributions of spins (Seed = 10) in sub-cases (<b>a</b>,<b>b</b>) and (Seed = 40) in sub-cases (<b>c</b>,<b>d</b>). While sub-cases (<b>a</b>,<b>c</b>) are identical to sub-cases a in <a href="#symmetry-16-01566-f001" class="html-fig">Figure 1</a> (Seed = 10) and <a href="#symmetry-16-01566-f002" class="html-fig">Figure 2</a> (Seed = 40), sub-cases (<b>b</b>,<b>d</b>) do not include Periodic Boundary Conditions (PBCs). The related results are very different, with respectiively a full symmetry breaking along <math display="inline"><semantics> <mrow> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math> instead of <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> after about 400 Monte Carlo steps instead of 180 and two coexisting domains of different sizes (magnetization −0.533) instead of (magnetization −0.0667) after about 300 Monte Carlo steps instead of 150.</p>
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<p>Results of three simulations in sub-cases (<b>a</b>–<b>c</b>) with identical size but different initial conditions (Seed = 10, 70, 50) as in <a href="#symmetry-16-01566-f001" class="html-fig">Figure 1</a> but using sequential update instead of random update. The sequential update leads to very different results from <a href="#symmetry-16-01566-f001" class="html-fig">Figure 1</a>, with, respectively, a full symmetry breaking along <math display="inline"><semantics> <mrow> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math> instead of <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> after about only 15 Monte Carlo steps instead of 180, a full symmetry breaking along <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> instead of <math display="inline"><semantics> <mrow> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math> after about only 10 Monte Carlo steps instead of 90, and two coexisting domains of different sizes (magnetization 0.0933) instead of a full symmetry breaking along <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> (magnetization −1) after about 20 Monte Carlo steps instead of about 700. Sub-cases (<b>d</b>,<b>g</b>,<b>j</b>) show respectively the initial distribution of spins for Seed = 10 with zero order parameter and two intermediate snapshots after 3 and 9 Monte Carlo steps respectively. Sub-cases (<b>e</b>,<b>h</b>,<b>k</b>) show respectively the initial distribution of spins for Seed = 70 with zero order parameter and two intermediate snapshots after 5 and 10 Monte Carlo steps respectively. Sub-cases (<b>f</b>,<b>i</b>,<b>l</b>) show respectively the initial distribution of spins for Seed = 50 with zero order parameter and two intermediate snapshots after 9 and 18 Monte Carlo steps respectively.</p>
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<p>Results of two simulations in sub-cases (<b>a</b>,<b>d</b>) with different initial distributions of spins (Seed = 10, 70) using simultaneous update. The system gets trapped very quickly after only a few Monte Carlo steps, as seen in both cases with periodic shift between two fixed configurations. Sub-cases (<b>b</b>,<b>c</b>) show two snapshots after 7 and 8 Monte Carlo steps for Seed = 10. Sub-cases (<b>e</b>,<b>f</b>) show two snapshots after 9 and 10 Monte Carlo steps for Seed = 70.</p>
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<p>Results of a two-step simultaneous update, denoted checkerboard update. All sites of each sub-lattice are updated simultaneously one after the other sequentially. Three simulations (sub-cases <b>a</b>–<b>c</b>) are performed with identical initial conditions (Seed = 10, 50, 70) as in <a href="#symmetry-16-01566-f001" class="html-fig">Figure 1</a> but using checkerboard update instead of random update. The checkerboard update leads to very different results from <a href="#symmetry-16-01566-f001" class="html-fig">Figure 1</a>, with, respectively, a full symmetry breaking unchanged along <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> but now after about only 15 Monte Carlo steps instead of 180, two coexisting domains of different sizes (magnetization 0.253) instead of a full symmetry breaking along <math display="inline"><semantics> <mrow> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math> after about only 15 Monte Carlo steps instead of 90, and two coexisting domains of different sizes (magnetization 0.142) instead of a full symmetry breaking along <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> (magnetization −1) after about 20 Monte Carlo steps instead of about 700. Sub-cases (<b>d</b>–<b>f</b>) exhibit the same simulations as in sub-cases (<b>a</b>–<b>c</b>) but without Periodic Boundary Conditions (PBCs). The associated results are slightly different, with, respectively, still a full symmetry breaking along <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> but with about 20 Monte Carlo steps instead of 15, a full symmetry breaking along <math display="inline"><semantics> <mrow> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math> instead of two coexisting domains with similar numbers of Monte Carlo steps, and still two coexisting domains of different sizes with magnetization 0.133 instead of magnetization 0.142. Sub-cases (<b>g</b>,<b>j</b>) show intermediate snapshots of sub-case d after 10 and 15 Monte Carlo steps. Sub-cases (<b>h</b>,<b>k</b>) show intermediate snapshots of sub-case e after 10 and 15 Monte Carlo steps. Sub-cases (<b>i</b>,<b>l</b>) show intermediate snapshots of sub-case f after 5 and 10 Monte Carlo steps.</p>
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<p>Results of Monte Carlo simulations for initial respective conditions <span class="html-italic">p</span> = 0.47 (<b>a</b>), 0.52 (<b>b</b>), 0.53 (<b>c</b>) with Periodic Boundary Conditions (PBCs). Sub-cases (<b>d</b>–<b>f</b>) show the results of the same Monte Carlo simulations but with no Periodic Boundary Conditions (no PBCs). Except for sub-case (<b>e</b>), the dynamics always ends up broken along <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>. The PBCs accelerate the process with fewer Monte Carlo steps than with no PBCs. Sub-cases (<b>g</b>–<b>i</b>) show the outcomes for <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> using different initial distributions of spins and no PBCs for (<b>g</b>) and PBCs for (<b>h</b>,<b>i</b>). The associated numbers of Monte Carlo steps differ.</p>
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<p>Results for a <math display="inline"><semantics> <mrow> <mn>40</mn> <mo>×</mo> <mn>40</mn> </mrow> </semantics></math> sample with Initial conditions <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.47</mn> </mrow> </semantics></math> (<b>a</b>–<b>d</b>) and <math display="inline"><semantics> <mrow> <mn>0.53</mn> </mrow> </semantics></math> (<b>e</b>–<b>h</b>). PBC are applied in (<b>a</b>,<b>b</b>,<b>e</b>,<b>f</b>) and not in (<b>c</b>,<b>d</b>,<b>g</b>,<b>h</b>). Domains coexistence is found in (<b>a</b>,<b>b</b>,<b>d</b>,<b>e</b>,<b>g</b>). Many more Monte Carlo steps are needed than for the sample <math display="inline"><semantics> <mrow> <mn>30</mn> <mo>×</mo> <mn>30</mn> </mrow> </semantics></math>.</p>
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18 pages, 7818 KiB  
Article
Adsorption Separation of Various Polar Dyes in Water by Oil Sludge-Based Porous Carbon
by Huanquan Cheng, Longgui Peng, Jia Liu, Cuiying Ma, Fangtao Hao, Bin Zheng and Jianye Yang
Appl. Sci. 2024, 14(16), 7283; https://doi.org/10.3390/app14167283 - 19 Aug 2024
Cited by 1 | Viewed by 923
Abstract
The pollution caused by printing and dyeing wastewater is increasingly severe, posing significant harm to aquatic plants and animals. In this study, porous carbon was synthesized via the high-temperature pyrolysis of light and heavy organic matter present in oily sludge, utilizing low oil [...] Read more.
The pollution caused by printing and dyeing wastewater is increasingly severe, posing significant harm to aquatic plants and animals. In this study, porous carbon was synthesized via the high-temperature pyrolysis of light and heavy organic matter present in oily sludge, utilizing low oil content sludge as the raw material and zinc chloride as a chemical activator. The results exhibited a significant increase in the specific surface area of the oily sludge-based porous carbon, from 4.95 m²/g to 10.95 m²/g. The effects of various parameters such as pH, amount of sorbent, dye concentration, temperature, and contact time on dye removal have been studied. The results showed that the oil sludge-based porous carbon exhibited high efficiency in removing Malachite Green from aqueous solutions, which has low polarity and remains consistently above 97%. The removal rate of Crystal Violet, which is more polar, was as low as 24.14%. The corresponding adsorption capacities were 33.41 mg/g for Malachite Green, 16.41 mg/g for Crystal Violet, and 13.56 mg/g for Methylene Blue. The adsorption capacity of OSC700 for three types of dyes was characterized by monolayer adsorption, primarily driven by chemical adsorption, with significant contributions from electrostatic and hydrophobic effects. The adsorption process was spontaneous, exothermic, and accompanied by an increase in entropy. For less polar substances, the adsorption on oily sludge-based porous carbon is primarily driven by aromatic functional groups on the carbon surface, hydrophobicity, π-π electron-donor-acceptor (π-π EDA) interactions, and surface hydrogen bond formation. In contrast, for more polar dyes, electrostatic and hydrophobic interactions dominate, with electrostatic adsorption being the predominant mechanism and minimal hydrogen bond formation during adsorption. Full article
(This article belongs to the Section Green Sustainable Science and Technology)
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Figure 1
<p>TG profiles of OS (<b>a</b>), XRD profiles of OS, NOSC and OSC700 (<b>b</b>), FT-IR profiles of OS and OSC700 (<b>c</b>).</p>
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<p>N<sub>2</sub> adsorption-desorption curves (<b>a</b>) and BJH pore size distribution curves (<b>b</b>) of OSC500, OSC700 and OSC900.</p>
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<p>SEM images of OS (<b>a</b>–<b>c</b>), OSC500 (<b>d</b>–<b>f</b>), OSC700 (<b>g</b>–<b>i</b>), OSC900 (<b>j</b>–<b>l</b>).</p>
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<p>EDS image of OSC700 (<b>a</b>), corresponding to C element (<b>b</b>), O element (<b>c</b>), Si element (<b>d</b>), Zn element (<b>e</b>).</p>
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<p>XPS spectra of OS (<b>a</b>–<b>d</b>), OSC700 (<b>e</b>–<b>i</b>) and their fitting results.</p>
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<p>Adsorption capacity and removal efficiency of three organic dyes at different initial concentrations (<b>a</b>), time (<b>b</b>), agent dosage (<b>c</b>), temperature (<b>d</b>) and pH (<b>e</b>), and Zeta of OSC700 (<b>f</b>).</p>
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<p>Adsorption mechanism of organic dyes by OSC.</p>
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<p>Langmuir (<b>a</b>), Freundlich (<b>b</b>), PFO kinetic curves (<b>c</b>), PSO kinetic curves (<b>d</b>), particle diffusion adsorption kinetic curves (<b>e</b>), thermodynamic curves (<b>f</b>) models for OSC700 adsorption of MG, MB, CV.</p>
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<p>Infrared spectra of OSC700 before and after adsorption of MG, MB and CV.</p>
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19 pages, 1292 KiB  
Review
A Review on Canine and Human Soft Tissue Sarcomas: New Insights on Prognosis Factors and Treatment Measures
by Filippo Dell’Anno, Roberta Giugliano, Valeria Listorti and Elisabetta Razzuoli
Vet. Sci. 2024, 11(8), 362; https://doi.org/10.3390/vetsci11080362 - 10 Aug 2024
Viewed by 2900
Abstract
Soft tissue sarcomas (STSs) represent a diverse group of tumors arising from mesenchymal cells, affecting both humans and animals, including dogs. Although STSs represent a class of rare tumors, especially in humans, they pose significant clinical challenges due to their potential for local [...] Read more.
Soft tissue sarcomas (STSs) represent a diverse group of tumors arising from mesenchymal cells, affecting both humans and animals, including dogs. Although STSs represent a class of rare tumors, especially in humans, they pose significant clinical challenges due to their potential for local recurrence and distant metastasis. Dogs, as a model for human STSs, offer several advantages, including exposure to similar environmental risk factors, genetic diversity among breeds, and the spontaneous development of tumors. Furthermore, canine tumors closely mimic the heterogeneity and complexity of human tumors, making them valuable for research into disease progression and treatment effectiveness. Current treatment approaches for STSs in both dogs and humans primarily involve surgery, radiation therapy, and chemotherapy, with treatment decisions based on tumor characteristics and patient factors. However, the development of novel therapeutic strategies is essential, given the high failure rate of new drugs in clinical trials. To better design new tailored treatments, comprehension of the tumor microenvironment (TME) is fundamental, since it plays a crucial role in STS initiation and progression by modulating tumor behavior, promoting angiogenesis, and suppressing immune responses. Notably, TME features include cancer-associated fibroblasts (CAFs), extracellular matrix (ECM) alterations, and tumor-associated macrophages (TAMs) that, depending on their polarization state, can affect immune responses and thus the patient’s prognosis. In this review, new therapeutical approaches based on immunotherapy will be deeply explored as potential treatment options for both dogs and humans with STSs. In conclusion, this review provides an overview of the current understanding of STSs in dogs and humans, emphasizing the importance of the TME and potential treatment strategies. Full article
(This article belongs to the Special Issue Focus on Tumours in Pet Animals)
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<p>A resume of the available therapies for humans and dogs in the treatment of soft tissue sarcomas.</p>
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<p>Tumor microenvironment in human and canine soft tissue sarcomas. (<b>A</b>) Tumor-associated macrophages (TAMs) can undergo two different polarizations, M1 or M2, starting from M0 populations defined as non-activated macrophages. M0 populations differentiate into the M1 subtype after stimulation with granulocyte–monocyte colony-stimulating factor (GM-CSF), lipopolysaccharide (LPS), or interferon-gamma (IFNγ). M2 polarization is activated by M-CSF, IL-4, IL 10, and IL 13, and it induces a pro-tumoral environment. (<b>B</b>) M2 can release signaling molecules and growth factors such as basic fibroblast growth factor (bFGF), platelet-derived growth factor (PDGF), and vascular endothelial growth factor (VEGF). (<b>C</b>) The activation of the VEGF pathway is responsible for angiogenesis and tumor growth. (<b>D</b>) VEGF can be used as a prognostic marker to predict tumor recurrence or survival in STSs. Indeed, there is a positive correlation between increased VEGF expression and a higher tumor grade. Moreover, TAMs negatively regulate cytotoxic effector cells, such as CD8+ and natural killer (NK) cells, and they often express programmed death-ligand 1 (PD-L1)/L2.</p>
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<p>Immunotherapy approaches specific to treat canine and human soft tissue sarcomas. See <a href="#vetsci-11-00362-t001" class="html-table">Table 1</a> for a more detailed description.</p>
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24 pages, 4104 KiB  
Article
Performance Correction and Parameters Identification Considering Non-Uniform Electric Field in Cantilevered Piezoelectric Energy Harvesters
by Xianfeng Wang, Hui Liu, Huadong Zheng, Guoxiong Liu and Dan Xu
Sensors 2024, 24(15), 4943; https://doi.org/10.3390/s24154943 - 30 Jul 2024
Cited by 1 | Viewed by 845
Abstract
In the current electromechanical model of cantilevered piezoelectric energy harvesters, the assumption of uniform electric field strength within the piezoelectric layer is commonly made. This uniform electric field assumption seems reasonable since the piezoelectric layer looks like a parallel-plate capacitor. However, for a [...] Read more.
In the current electromechanical model of cantilevered piezoelectric energy harvesters, the assumption of uniform electric field strength within the piezoelectric layer is commonly made. This uniform electric field assumption seems reasonable since the piezoelectric layer looks like a parallel-plate capacitor. However, for a piezoelectric bender, the strain distribution along the thickness direction is not uniform, which means the internal electric field generated by the spontaneous polarization cannot be uniform. In the present study, a non-uniform electric field in the piezoelectric layer is resolved using electrostatic equilibrium equations. Based on these, the traditional distributed parameter electromechanical model is corrected and simplified to a practical single mode one. Compared with a traditional model adopting a uniform electric field, the bending stiffness term involved in the electromechanical governing equations is explicitly corrected. Through comparisons of predicted power output with two-dimensional finite element analysis, the results show that the present model can better predict the power output performance compared with the traditional model. It is found that the relative corrections to traditional model have nothing to do with the absolute dimensions of the harvesters, but only relate to three dimensionless parameters, i.e., the ratio of the elastic layer’s to the piezoelectric layer’s thickness; the ratio of the elastic modulus of the elastic layer to the piezoelectric layer; and the piezoelectric materials’ electromechanical coupling coefficient squared, k312. It is also found that the upper-limit relative corrections are only related to k312, i.e., the higher k312 is, the larger the upper-limit relative corrections will be. For a PZT-5 unimorph harvester, the relative corrections of bending stiffness and corresponding resonant frequency are up to 17.8% and 8.5%, respectively. An inverse problem to identify the material parameters based on experimentally obtained power output performance is also investigated. The results show that the accuracy of material parameters identification is improved when considering a non-uniform electric field. Full article
(This article belongs to the Special Issue Piezoelectric Energy Harvesting System)
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<p>Typical configurations of cantilevered piezoelectric energy harvesters.</p>
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<p>Comparison of power output FRF for sample harvesters.</p>
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<p>Comparison of power output FRF for sample harvesters.</p>
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<p>Impact of material electromechanical coupling strength.</p>
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<p>Bending stiffness correction factor <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>δ</mi> </mrow> <mrow> <mi>U</mi> </mrow> </msub> </mrow> </semantics></math> with respect to geometric ratio <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>n</mi> </mrow> <mrow> <mi>g</mi> </mrow> </msub> </mrow> </semantics></math> and elastic ratio <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>n</mi> </mrow> <mrow> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math> (PZT-5H unimorph harvesters).</p>
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<p>Bending stiffness correction factor <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>δ</mi> </mrow> <mrow> <mi>B</mi> </mrow> </msub> </mrow> </semantics></math> with respect to geometric ratio <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>n</mi> </mrow> <mrow> <mi>g</mi> </mrow> </msub> </mrow> </semantics></math> and elastic ratio <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>n</mi> </mrow> <mrow> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math> (PZT-5H bimorph harvesters).</p>
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<p>Maximum bending stiffness correction factors <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>δ</mi> </mrow> <mrow> <mi>U</mi> </mrow> <mrow> <mi>M</mi> <mi>a</mi> <mi>x</mi> </mrow> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>δ</mi> </mrow> <mrow> <mi>B</mi> </mrow> <mrow> <mi>M</mi> <mi>a</mi> <mi>x</mi> </mrow> </msubsup> </mrow> </semantics></math> with respect to <math display="inline"><semantics> <mrow> <msup> <mrow> <msub> <mrow> <mi>k</mi> </mrow> <mrow> <mn>31</mn> </mrow> </msub> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math>.</p>
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<p>Procedures to identify the Young modulus <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Y</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> of the substructure.</p>
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<p>Procedures to identify <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Y</mi> </mrow> <mrow> <mi>p</mi> </mrow> </msub> <mo>,</mo> <mo> </mo> <msub> <mrow> <mi>d</mi> </mrow> <mrow> <mn>31</mn> </mrow> </msub> <mo>,</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>ε</mi> </mrow> <mrow> <mn>33</mn> </mrow> <mrow> <mi>T</mi> </mrow> </msubsup> </mrow> </semantics></math>.</p>
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<p>Numerical experiment result and identified curve.</p>
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18 pages, 12149 KiB  
Article
Microstructures and Corrosion Behaviors of Non-Equiatomic Al0.32CrFeTi0.73(Ni1.50−xMox)(x = 0, 0.23) High-Entropy Alloy Coatings Prepared by the High-Velocity Oxygen Fuel Method
by Xiaoyong Shu, Hao Wang and Jianping Zhao
Coatings 2024, 14(7), 907; https://doi.org/10.3390/coatings14070907 - 20 Jul 2024
Cited by 1 | Viewed by 1051
Abstract
The non-equiatomic Al0.32CrFeTi0.73(Ni1.50−xMox) (x = 0, 0.23) high-entropy alloy (HEA) coatings were prepared by the high-velocity oxygen fuel (HVOF) method. The microstructures and corrosion behaviors of the HVOF-prepared coatings were investigated. The corrosion behaviors were [...] Read more.
The non-equiatomic Al0.32CrFeTi0.73(Ni1.50−xMox) (x = 0, 0.23) high-entropy alloy (HEA) coatings were prepared by the high-velocity oxygen fuel (HVOF) method. The microstructures and corrosion behaviors of the HVOF-prepared coatings were investigated. The corrosion behaviors were characterized by polarization, EIS and Mott-Schottky tests under a 3.5 wt.% sodium chloride aqueous solution open to air at room temperature. The Al0.32CrFeTi0.73Ni1.50 coating is a simple BCC single-phase solid solution structure compared with the corresponding poly-phase composite bulk. The structure of the Al0.32CrFeTi0.73Ni1.27Mo0.23 coating, combined with the introduction of the Mo element, means that the (Cr,Mo)-rich sigma phase precipitates out of the BCC solid solution matrix phase, thus forming Cr-depleted regions around the sigma phases. The solid solution of large atomic-size Mo element causes the lattice expansion of the BCC solid solution matrix phase. Micro-hole and micro-crack defects are formed on the surface of both coatings. The growth of both coatings’ passivation films is spontaneous. Both passivation films are stable and Cr2O3-rich, P-type, single-layer structures. The Al0.32CrFeTi0.73Ni1.50 coating has better corrosion resistance and much less pitting susceptibility than the corresponding bulk. The corrosion type of the Mo-free coating is mainly pitting, occurring in the coating’s surface defects. The Al0.32CrFeTi0.73Ni1.27Mo0.23 coating with the introduction of Mo element increases pitting susceptibility and deteriorates corrosion resistance compared with the Mo-free Al0.32CrFeTi0.73Ni1.50 coating. The corrosion type of the Mo-bearing coating is mainly pitting, occurring in the coating’s surface defects and Cr-depleted regions. Full article
(This article belongs to the Section Corrosion, Wear and Erosion)
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<p>Microstructure morphology of the prepared HEA powders.</p>
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<p>XRD patterns of the Mo0 and Mo23 (<b>a</b>) powders and (<b>b</b>) bulks.</p>
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<p>Schematic of the coating preparation process. Reprinted with permission from [<a href="#B14-coatings-14-00907" class="html-bibr">14</a>]. 2015. Elsevier.</p>
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<p>The HVOF-prepared, non-equiatomic HEA coatings.</p>
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<p>XRD patterns of the Mo0 bulk and Mo0 coating.</p>
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<p>Microstructural characterization of the Mo0 coating surface: (<b>a</b>) SEM image; (<b>b</b>–<b>f</b>) element mapping image of Al, Cr, Fe, Ti and Ni.</p>
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<p>SEM images of local details of the Mo0 coating.</p>
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<p>The potentiodynamic polarization curves of the Mo0 coating and the Mo0 bulk.</p>
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<p>Corrosion morphology of the Mo0 coating surface.</p>
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<p>EIS of the Mo0 coating and Mo0 bulk: (<b>a</b>) Nyquist plots; (<b>b</b>) Bode plots. Z: impedance; Z<sub>re</sub>: real part of impedance; Z<sub>im</sub>: imaginary part of impedance; f: frequency; −θ: phase angle.</p>
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<p>Equivalent electric circuit models used to fit the EIS data for (<b>a</b>) Mo0 bulk and (<b>b</b>) Mo0 coating. Rs is electrolyte resistance. Rsp is electrolyte resistance through the pits. Rf and Rct represent the passive film resistance and charge transfer resistance between the electrolyte and the matrix in the pits. CPE is the constant-phase element.</p>
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<p>Mott-Schottky plots of the Mo0 bulk and the Mo0 coating passivation films.</p>
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<p>XRD patterns of the Mo0 coating and Mo23 coating.</p>
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<p>The Sigma phases for the Mo23 coating.</p>
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<p>SEM images of Mo23 coating local details.</p>
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<p>The potentiodynamic polarization curves of the Mo0 coating and Mo23 coating.</p>
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<p>The potentiodynamic polarization curves of the Mo0 bulk and Mo23 bulk.</p>
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<p>Corrosion morphology of Mo23 coating surface.</p>
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<p>EIS of the Mo0 coating and Mo23 coating: (<b>a</b>) Nyquist plots; (<b>b</b>) Bode plots.</p>
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<p>Mott-Schottky plots of the Mo0 coating and Mo23 coating.</p>
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14 pages, 4280 KiB  
Article
Insights into Antisite Defect Complex Induced High Ferro-Piezoelectric Properties in KNbO3 Perovskite: First-Principles Study
by Bei Li, Yilun Zhang, Meng Wang, Xu Zhang, Xiaofeng Zhang and Kai Liu
Materials 2024, 17(14), 3442; https://doi.org/10.3390/ma17143442 - 11 Jul 2024
Viewed by 1150
Abstract
Improving ferro-piezoelectric properties of niobate-based perovskites is highly desirable for developing eco-friendly high-performance sensors and actuators. Although electro-strain coupling is usually obtained by constructing multiphase boundaries via complex chemical compositions, defect engineering can also create opportunities for novel property and functionality advancements. In [...] Read more.
Improving ferro-piezoelectric properties of niobate-based perovskites is highly desirable for developing eco-friendly high-performance sensors and actuators. Although electro-strain coupling is usually obtained by constructing multiphase boundaries via complex chemical compositions, defect engineering can also create opportunities for novel property and functionality advancements. In this work, a representative tetragonal niobate-based perovskite, i.e., KNbO3, is studied by using first-principles calculations. Two intrinsic types of Nb antisite defect complexes are selected to mimic alkali-deficiency induced excess Nb antisites in experiments. The formation energy, electronic profiles, polarization, and piezoelectric constants are systematically analyzed. It is shown that the structural distortion and chemical heterogeneity around the energetically favorable antisite pair defects, i.e., (NbK4·+KNb4), lower the crystal symmetry of KNbO3 from tetragonal to triclinic phase, and facilitate polarization emergence and reorientation to substantially enhance intrinsic ferro-piezoelectricity (i.e., spontaneous polarization Ps of 68.2 μC/cm2 and piezoelectric strain constant d33 of 228.3 pC/N) without complicated doping and alloying. Full article
(This article belongs to the Section Materials Simulation and Design)
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<p>Optimized supercell structures and the corresponding lengths of Nb–O bonds for (<b>a</b>,<b>d</b>) KNbO<sub>3</sub>, (<b>b</b>,<b>e</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>Nb</mi> </mrow> <mi mathvariant="normal">K</mi> <mrow> <mn>4</mn> <mo>·</mo> </mrow> </msubsup> </mrow> </semantics></math>-<math display="inline"><semantics> <mrow> <mn>4</mn> <msubsup> <mi mathvariant="normal">V</mi> <mi mathvariant="normal">K</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math>-KNbO<sub>3</sub>, and (<b>c</b>,<b>f</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>Nb</mi> </mrow> <mi mathvariant="normal">K</mi> <mrow> <mn>4</mn> <mo>·</mo> </mrow> </msubsup> </mrow> </semantics></math>-<math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="normal">K</mi> <mrow> <mi>Nb</mi> </mrow> <mrow> <mn>4</mn> <mo>′</mo> </mrow> </msubsup> </mrow> </semantics></math>-KNbO<sub>3</sub> models, respectively.</p>
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<p>Two-dimensional electron localization function (ELF) profiles in the (110) plane for (<b>a</b>) KNbO<sub>3</sub>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>Nb</mi> </mrow> <mi mathvariant="normal">K</mi> <mrow> <mn>4</mn> <mo>·</mo> </mrow> </msubsup> </mrow> </semantics></math>-<math display="inline"><semantics> <mrow> <mn>4</mn> <msubsup> <mi mathvariant="normal">V</mi> <mi mathvariant="normal">K</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math>-KNbO<sub>3</sub>, and (<b>c</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>Nb</mi> </mrow> <mi mathvariant="normal">K</mi> <mrow> <mn>4</mn> <mo>·</mo> </mrow> </msubsup> </mrow> </semantics></math>-<math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="normal">K</mi> <mrow> <mi>Nb</mi> </mrow> <mrow> <mn>4</mn> <mo>′</mo> </mrow> </msubsup> </mrow> </semantics></math>-KNbO<sub>3</sub>, and the ELF profiles in the (001) plane for (<b>d</b>) KNbO<sub>3</sub>, (<b>e</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>Nb</mi> </mrow> <mi mathvariant="normal">K</mi> <mrow> <mn>4</mn> <mo>·</mo> </mrow> </msubsup> </mrow> </semantics></math>-<math display="inline"><semantics> <mrow> <mn>4</mn> <msubsup> <mi mathvariant="normal">V</mi> <mi mathvariant="normal">K</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math>-KNbO<sub>3</sub>, and (<b>f</b>,<b>g</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>Nb</mi> </mrow> <mi mathvariant="normal">K</mi> <mrow> <mn>4</mn> <mo>·</mo> </mrow> </msubsup> </mrow> </semantics></math>-<math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="normal">K</mi> <mrow> <mi>Nb</mi> </mrow> <mrow> <mn>4</mn> <mo>′</mo> </mrow> </msubsup> </mrow> </semantics></math>-KNbO<sub>3</sub>. The marked Nb atoms refer to the ones in <a href="#materials-17-03442-f001" class="html-fig">Figure 1</a>b,c.</p>
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<p>Energy band structures and the total and partial density of states (DOS) for different elements and orbitals in (<b>a</b>,<b>d</b>) KNbO<sub>3</sub>, (<b>b</b>,<b>e</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>Nb</mi> </mrow> <mi mathvariant="normal">K</mi> <mrow> <mn>4</mn> <mo>·</mo> </mrow> </msubsup> </mrow> </semantics></math>-<math display="inline"><semantics> <mrow> <mn>4</mn> <msubsup> <mi mathvariant="normal">V</mi> <mi mathvariant="normal">K</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math>-KNbO<sub>3</sub>, and (<b>c</b>,<b>f</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>Nb</mi> </mrow> <mi mathvariant="normal">K</mi> <mrow> <mn>4</mn> <mo>·</mo> </mrow> </msubsup> </mrow> </semantics></math>-<math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="normal">K</mi> <mrow> <mi>Nb</mi> </mrow> <mrow> <mn>4</mn> <mo>′</mo> </mrow> </msubsup> </mrow> </semantics></math>-KNbO<sub>3</sub>. The black dashed lines are the Fermi level, set to zero energy. The valance band maximum and the conduction band minimum are depicted by the blue and red lines, respectively, in (<b>a</b>–<b>c</b>).</p>
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<p>Atomic structures of pathways corresponding to the polarization switching of (<b>a</b>) KNbO<sub>3</sub>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>Nb</mi> </mrow> <mi mathvariant="normal">K</mi> <mrow> <mn>4</mn> <mo>·</mo> </mrow> </msubsup> </mrow> </semantics></math>-<math display="inline"><semantics> <mrow> <mn>4</mn> <msubsup> <mi mathvariant="normal">V</mi> <mi mathvariant="normal">K</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math>-KNbO<sub>3</sub>, and (<b>c</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>Nb</mi> </mrow> <mi mathvariant="normal">K</mi> <mrow> <mn>4</mn> <mo>·</mo> </mrow> </msubsup> </mrow> </semantics></math>-<math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="normal">K</mi> <mrow> <mi>Nb</mi> </mrow> <mrow> <mn>4</mn> <mo>′</mo> </mrow> </msubsup> </mrow> </semantics></math>-KNbO<sub>3</sub>, and their corresponding energy (<b>d</b>–<b>f</b>) and polarization (<b>g</b>–<b>i</b>) profiles as functions of percentage distortion from the high symmetry (0% distortion) structure to the ferroelectric phase (<math display="inline"><semantics> <mo>±</mo> </semantics></math>100% distortion) structure using the linear interpolation method. In (<b>g</b>–<b>i</b>), <span class="html-italic">P</span><sub>s</sub> is the spontaneous polarization along the −<span class="html-italic">c</span> axis and <span class="html-italic">P</span><sub>q</sub> is the polarization quantum. The open and solid dots are calculated points and the solid and dashed lines illustrate the evolution along the energy gradient and branches of the polarization lattice, respectively. Notice that the different colored lines represent the various branches of the lattice.</p>
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15 pages, 3571 KiB  
Article
Structural, Morphological and Ferroelectric Properties of Sr-Cd Co-Doped Nickel Ferrite for Energy Storage Devices
by Huda A. Alburaih, Muhammad Ahsan ul Haq, Abdul Jabbar, Atiq ur Rehman, Amel Laref, Mohamed Musa Saad Hasb Elkhalig and Naveed Ahmad Noor
Magnetochemistry 2024, 10(7), 48; https://doi.org/10.3390/magnetochemistry10070048 - 2 Jul 2024
Viewed by 1337
Abstract
Ferroelectric materials, renowned for their capacity to demonstrate spontaneous electric polarization reversible through an external electric field, are essential in numerous technological applications owing to their distinctive characteristics. For this, a series of spinel Sr-Cd co-doped nickel ferrite nanomaterials Cd0.5−xSrx [...] Read more.
Ferroelectric materials, renowned for their capacity to demonstrate spontaneous electric polarization reversible through an external electric field, are essential in numerous technological applications owing to their distinctive characteristics. For this, a series of spinel Sr-Cd co-doped nickel ferrite nanomaterials Cd0.5−xSrxNi0.5Fe2O4 (x = 0.0, 0.1, 0.2 and 0.3) were prepared through the standard sol-gel auto combustion method The XRD patterns showed that the prepared samples have a cubic spinel structure. The crystallite sizes of the samples vary from 29 to 40 nm. The morphology of prepared samples showed uniformly distributed spheres. Magnetic properties showed the soft magnetic nature of the prepared ferrites. The ferroelectric study revealed that Sr-Cd substituted ferrites exhibited the elliptical nature of ferroelectric loops at normal room temperature. The maximum polarization has been achieved at x = 0.3. The understanding of current and voltage (I–V) showed a slowly decreasing tendency of leakage current on both sides symmetrically against the increasing Sr content. The conductivity of the prepared spinel increases as a function of higher Sr doping. The real part of dielectric constant increases with increasing frequency. The materials show large elliptical loops indicating high asymmetric ferroelectric energy storage capability. Full article
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<p>(<b>a</b>) XRD pattern of Cd<sub>0.5−x</sub>Sr<sub>x</sub>Ni<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub> (where x = 0.0, 0.1, 0.2 and 0.3) (<b>b</b>) enlarged view of (220) peak.</p>
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<p>SEM images of Cd<sub>0.5−x</sub>Sr<sub>x</sub>Ni<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub> where (<b>a</b>) x = 0.0 (<b>b</b>) x = 0.1 (<b>c</b>) x = 0.2 and (<b>d</b>) x = 0.3.</p>
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<p>Hysteresis loop for Cd<sub>0.5−x</sub>Sr<sub>x</sub>Ni<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub> where x = 0.0, 0.1, 0.2 and 0.3.</p>
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<p>Ferroelectric behavior of Cd<sub>0.5−x</sub>Sr<sub>x</sub>Ni<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub> (where x = 0.0, 0.1, 0.2 and 0.3) respectively.</p>
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<p>Maximum Polarization (P<sub>m</sub>), and Remnance Polarization (P<sub>r</sub>) of Cd<sub>0.5−x</sub>Sr<sub>x</sub>Ni<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub> (where x = 0.0, 0.1, 0.2 and 0.3) respectively.</p>
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<p>% Efficiency of Cd<sub>0.5−x</sub>Sr<sub>x</sub>Ni<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub> (where x = 0.0, 0.1, 0.2 and 0.3) respectively.</p>
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<p>I–V characteristics of Cd<sub>0.5−x</sub>Sr<sub>x</sub>Ni<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub> (where x = 0.0, 0.1, 0.2 and 0.3) respectively.</p>
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<p>Real (ε′) and imaginary (ε″) part of dielectric constant for undoped and doped (x = 0.3) samples.</p>
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