Magnetic Field Enhancing the Electrocatalytic Oxygen Evolution Reaction of FeMn-Based Spinel Oxides

For the widespread application of electrolytic water hydrogen production technology, it is crucial to prepare electrolytic water catalysts via inexpensive and abundant transition metals. Commercially, Mn3O4 can be utilized extensively since it is cheap, abundant, and stable, but its electrocatalytic performance still needs to be enhanced. In this paper, we adopted the conventional hydrothermal method to introduce Fe atoms into Mn3O4 to form spinel-structured FeMn oxides on Ni foam (marked as FexMn3–xO4); then, an external magnetic field was applied to further improve its oxygen evolution reaction (OER) performance. The overpotential of FeMn2O4 is 258 mV (current density at 20 mA·cm–2), and the Tafel slope is 28.7 mV·dec–1 when the magnetic field strength is 105 mT and the angle between the electric field and the magnetic field is 45°. The introduction of Fe and the synergistic effect of the mixed Mn and Fe promote the reaction kinetics and thus improve the OER performance of Mn3O4. The enhanced performance of FexMn3–xO4 under the magnetic field may mainly originate from the magnetohydrodynamic (MHD) effect, charge transfer effect, and energy effect

Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization

High-entropy alloys (HEAs) with high hardness and high ductility can be considered as candidates for wear-resistant applications. However, designing novel HEAs with multiple desired properties using traditional alloy design methods remains challenging due to the enormous composition space. In this work, we proposed a machine-learning-based framework to design HEAs with high Vickers hardness (H) and high compressive fracture strain (D). Initially, we constructed data sets containing 172,467 data with 161 features for D and H, respectively. Four-step feature selection was performed, with the selection of 12 and 8 features for the D and H prediction models based on the optimal algorithms of the support vector machine (SVR) and light gradient boosting machine (LightGBM), respectively. The R2 of the well-trained models reached 0.76 and 0.90 for the 10-fold cross validation. Nondominated sorting genetic algorithm version II (NSGA-II) and virtual screening were employed to search for the optimal alloying compositions, and four recommended candidates were synthesized to validate our methods. Notably, the D of three candidates have shown significant improvements compared to the samples with similar H in the original data sets, with increases of 135.8, 282.4, and 194.1% respectively. Analyzing the candidates, we have recommended suitable atomic percentage ranges for elements such as Al (2–14.8 at %), Nb (4–25 at %), and Mo (3–9.9 at %) in order to design HEAs with high hardness and ductility

Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization

High-entropy alloys (HEAs) with high hardness and high ductility can be considered as candidates for wear-resistant applications. However, designing novel HEAs with multiple desired properties using traditional alloy design methods remains challenging due to the enormous composition space. In this work, we proposed a machine-learning-based framework to design HEAs with high Vickers hardness (H) and high compressive fracture strain (D). Initially, we constructed data sets containing 172,467 data with 161 features for D and H, respectively. Four-step feature selection was performed, with the selection of 12 and 8 features for the D and H prediction models based on the optimal algorithms of the support vector machine (SVR) and light gradient boosting machine (LightGBM), respectively. The R2 of the well-trained models reached 0.76 and 0.90 for the 10-fold cross validation. Nondominated sorting genetic algorithm version II (NSGA-II) and virtual screening were employed to search for the optimal alloying compositions, and four recommended candidates were synthesized to validate our methods. Notably, the D of three candidates have shown significant improvements compared to the samples with similar H in the original data sets, with increases of 135.8, 282.4, and 194.1% respectively. Analyzing the candidates, we have recommended suitable atomic percentage ranges for elements such as Al (2–14.8 at %), Nb (4–25 at %), and Mo (3–9.9 at %) in order to design HEAs with high hardness and ductility

Machine Learning Combined with Weighted Voting Regression and Proactive Searching Progress to Discover ABO_3‑δ Perovskites with High Oxide Ionic Conductivity

ABO3‑δ-type perovskites are one of the important oxygen ion conductors because of the enhanced properties through adjustments to the composition via elemental doping. In this work, machine learning combined with weighted voting regression (WVR) and proactive searching progress (PSP) was used to develop a model with high accuracy for the prediction of the oxide ionic conductivity of doped ABO3‑δ perovskites. After feature selection, algorithm selection, and parameter optimization, Gradient Boosting regression (GBR), random forest regression (RFR), and extra trees regression (ETR) were determined to be the optimal methods for WVR in constructing the integrated model. The R values of leave-one-out cross-validation (LOOCV) and the test set for the integrated model MWVR could reach 0.812 and 0.920, respectively. After the PSP was conducted, a total of 179 perovskites with high oxide ionic conductivity were discovered. PSP searching identified 8 types of perovskites with high oxide ionic conductivity. Pattern recognition was employed to identify the optimization area that exhibited a high oxide ionic conductivity. Visualization of factor effects was used to visualize the effect of the doping element type and ratio on the oxide ionic conductivity. The Shapley Additive exPlanations (SHAP) analysis of the significant features revealed that Ra/Rb had the highest influence on the oxide ionic conductivity with a negative impact. The developed integrated model, explored patterns, and optimization areas in this work can serve as a valuable guide for the discovery and design of perovskites with high oxide ionic conductivity

Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization

High-entropy alloys (HEAs) with high hardness and high ductility can be considered as candidates for wear-resistant applications. However, designing novel HEAs with multiple desired properties using traditional alloy design methods remains challenging due to the enormous composition space. In this work, we proposed a machine-learning-based framework to design HEAs with high Vickers hardness (H) and high compressive fracture strain (D). Initially, we constructed data sets containing 172,467 data with 161 features for D and H, respectively. Four-step feature selection was performed, with the selection of 12 and 8 features for the D and H prediction models based on the optimal algorithms of the support vector machine (SVR) and light gradient boosting machine (LightGBM), respectively. The R2 of the well-trained models reached 0.76 and 0.90 for the 10-fold cross validation. Nondominated sorting genetic algorithm version II (NSGA-II) and virtual screening were employed to search for the optimal alloying compositions, and four recommended candidates were synthesized to validate our methods. Notably, the D of three candidates have shown significant improvements compared to the samples with similar H in the original data sets, with increases of 135.8, 282.4, and 194.1% respectively. Analyzing the candidates, we have recommended suitable atomic percentage ranges for elements such as Al (2–14.8 at %), Nb (4–25 at %), and Mo (3–9.9 at %) in order to design HEAs with high hardness and ductility

Kinetic Study on the Self-Assembly of Au(I)–Thiolate Lamellar Sheets: Preassembled Precursor vs Molecular Precursor

Molecular self-assembly has played an important role in nanofabrication. Due to the weak driving forces of noncovalent bonds, developing molecular nanoassemblies that have both robust preparation conditions and stable structure is a challenge. In our previous work, we have developed a reversible self-assembly system of Au­(I)–thiolate coordination polymer (ATCP) to form colloidal lamellar sheets and demonstrated the high tailorability and stability of their structures, as well as their promising applications in gold nanocluster/nanoparticle fabrication and UV light shielding. Here, we first reported our progress in exploring a robust and green assembly protocol toward ATCP colloidal lamellar sheets in water by allowing the molecular precursors of HAuCl₄ and the thiol ligand to form ATCP preassembled intermediates. In this way, colloidal ATCP lamellar sheets can be prepared in a wide range of synthetic concentrations ([Au]₀ ≥ 2 × 10^–4 M) and at broad assembly temperatures (80–100 °C) with similar high yields (>80%). The assembly kinetics at different conditions are also studied in detail to help understand the robust assembly process. The robust and green synthetic protocols will pave a way for their real applications

Le Peuple : organe quotidien du syndicalisme

25 juin 19381938/06/25 (A20,N6363)-1938/06/25

Marie-Claire / dir. Jean Prouvost

29 juillet 19381938/07/29 (A0,N74)-1938/07/29.Appartient à l’ensemble documentaire : UnivJeun

Additional file 8: of Development of the body image self-rating questionnaire for breast cancer (BISQ-BC) for Chinese mainland patients

Body Image Self-rating Questionnaire for Breast Cancer (BISQ-BC). (DOC 79 kb

Table_1_High-throughput analysis of polyethoxylated tallow amine homologs in citrus using a modified QuEChERS-HILIC-MS method.DOCX

A new method is described based on ultrahigh-performance liquid chromatography-mass spectrometry (UHPLC) with electrospray mass spectrometry detection for comprehensive quantitative analysis of 66 polyethoxylated tallow amine (POE-tallowamine) homologs in citrus. Efficient separation, reduced band broadening, and high sensitivity were achieved by employing an acetonitrile-aqueous solution containing a 10 mM ammonium formate gradient on a hydrophilic interaction chromatography (HILIC) column with a modified QuEChERS (quick, easy, cheap, effective, rugged, and safe) method. The quantitative accuracy and precision of the method were improved by the use of matrix-matched calibration standards. At spiked levels of (50 + 250) μg/kg, (200 + 1000) μg/kg, and (500 + 2500) μg/kg POE-5 and POE-15 (1:5), the average recoveries of the POE-tallowamine homologs ranged from 71.9 to 112%, with RSDs < 16.6%. The limits of detection (LODs) and limits of quantification (LOQs) for POE-tallowamine homologs were 0.01–2.57 and 0.03–8.58 μg/kg, respectively. The method was successfully applied to determine POE-tallowamine in citrus samples from typical Chinese regions in 2021. POE-tallowamine was detected in all 54 samples, and the highest concentration (143 μg/kg) of POE-tallowamine was found in Jelly orange from Zhejiang Province, which might indicate a higher usage and demand of glyphosate herbicides in Zhejiang.</p