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Dynamic response of B4C, TiB2 and TiB2-B4C under shock wave compression

Ceramics International 2025年

作者： Shu, Zaiqin Jing, Qiang Wan, Mingming Zhang, Jinyong School of Physics and Mechanics Wuhan University of Technology Wuhan430070 China School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China State Key laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan430070 China Inner Mongolian Metal Materials Research Institute Yantai264003 China

TiB2-B4C (TBC) has been widely utilized in lightweight armor due to its exceptional mechanical properties. The Hugoniot elastic limit (HEL) has been considered as an important dynamic property of ceramics. This paper presents the shock responses of pure B4C, pure TiB2, and TiB2-B4C composite. By microstructure analysis, the amorphization in B4C and dislocations in TiB2 at shock pressures around 37 GPa in the recycled debris were observed. The dislocations in TiB2 are believed to be the reason for ‘two HELs’ in TiB2. The addition of TiB2 has little effect on the shock properties of B4C, while the shock pressure exceeds HEL. Additionally, a failure wave was detected in the ceramics at shock pressures lower than HEL. A damage parameter was defined to quantify the extent of damage in the failure zone. The incorporation of TiB2 into B4C can significantly slow down failure propagation in TBC at pressure lower than HEL. Furthermore, the analysis of the damage parameter suggests that TBC exhibits superior impact resistance compared to pure B4C and TiB2. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Amorphization

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Ag-Induced Synergistic Enhancement of mechanical and shape memory characteristics in Cu-Al-Ni alloys

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materials Letters 2025年 395卷

作者： Zhang, Jinjin Zhang, Chi Jia, Mingyong Yin, Guanchao Chen, Fei Hubei Longzhong Laboratory Xiangyang441106 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan430070 China Hubei Technology Innovation Center for Advanced Composites Wuhan University of Technology Wuhan430070 China International School of Materials Science and Engineering Wuhan University of Technology Wuhan430070 China

This study investigates the microstructure, mechanical properties, and shape memory behavior of a series of (84-x) Cu-12Al-4Ni-xAg (x = 0, 4, 8, 12 wt%) alloys with varying Ag contents. The alloys predominantly exhibited martensitic phases: 18R and 2H, with the 18R phase becoming more prevalent as Ag was introduced. The formation of the Ag2Al phase and microstructural refinement contributed to significant improvements in mechanical properties. Notably, the alloy with 8 wt% Ag (x = 8) exhibited peak compressive strength of 947.1 MPa, the compressive strain of 14.1 %. Additionally, the inclusion of Ag enhanced the shape memory effect of the alloys, with the x = 8 alloy demonstrating a reversible strain of 4.7 % upon heating after an 8 % pre-strain. © 2025 Elsevier B.V.

关键词： Microhardness

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Solid-state stepwise temperature-programmable synthesis of bioinspired Fe-N-C oxygen reduction electrocatalyst featuring Fe-N_(5)configuration

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Nano Research 2025年第3期18卷 654-665页

作者： Wei Sang Somboon Chaemchuen Longyang Zhang Zechen Wang Xingchuan Li Cocoro Andrew Nagasaka Mo Xiong Naoki Ogiwara Cheng Chen Zhao Wang Jian Zhang Francis Verpoort Shichun Mu Zongkui Kou Tingting Wang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China Department of Basic Science School of Arts and SciencesThe University of TokyoKomabaMeguro-kuTokyo 153-8902Japan MOE Key Laboratory for Non-Equilibrium Synthesis and Modulation of Condensed Matter School of PhysicsXi’an Jiao tong UniversityXi’an 710049China Sanya Science and Education Innovation Park of Wuhan University of Technology Sanya 572000China Department of Chemical Engineering Faculty of EngineeringMahidol UniversityNakhon Pathom 73170Thailand School of Mathematical&Physical Sciences Wuhan Textile UniversityWuhan 430200China Joint Institute of Chemical Research(FFMiEN) Peoples Friendship University of Russia(RUDN University)6 Miklukho-Maklaya Str.117198 MoscowRussia National Research Tomsk Polytechnic University Lenin Avenue 30634050 TomskRussia Hubei Key Laboratory of Ferro &Piezo-electric Materials and DevicesFaculty of Physics and Electronic SciencesHubei UniversityWuhan 430062China School of Chemistry and Chemical Engineering Hainan UniversityHaikou 57022China

The bioinspired Fe-N-C features an asymmetric Fe-N_(5)configuration to produce active metal-oxygen intermediates by introducing axial N ligand into a symmetric Fe-N_(4) structure,enabling highly active oxygen reduction reaction(ORR).However,the artificial creation of active Fe-N_(5)configuration with a direct,facile and green method has been rarely developed yet,as current techniques involve complex processes and costly ***,we advance a novel solid-state stepwise temperature-programmable(SST)route to directly produce bioinspired Fe-N_(5)-*** then demonstrate that such a Fe-N_(5)-C exhibits a quite higher half-wave potential(0.92 V)with 22-fold faster ORR kinetics(15.6 mA·cm^(−2)@0.85 V)over that of the commercial Pt/C ***,we perform density functional theory(DFT)to find that the Fe is discharged with an extra 0.1 e^(−)through the axially coordinate N ligand,which significantly enhances the ability to activate O_(2) and enables an easier desorption of the crucial intermediate ^(*)OH on the Fe-N_(5)configuration over the conventional Fe-N_(4) structure.

关键词： single atom catalysts Fe-N_(5)coordination solvent-free synthesis oxygen reduction reaction(ORR) metal-air battery

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Controllable preparation of Eu3+-doped octacalcium phosphate and fluorescence study of its hydrolysis into hydroxyapatite

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CrystEngComm 2025年

作者： Zhou, Heng Chen, Jia Tan, Xinyu Wei, Xuanhe Han, Yingchao State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Biomedical Materials and Engineering Research Center of Hubei Province Wuhan University of Technology Wuhan430070 China

Octacalcium phosphate (OCP) is generally considered the intermediate of hydroxyapatite (HAP) during bone mineralization. Crystalline phase transformation of OCP to HAP is an important topic in the study of biomineralization processes. In this study, Eu3+-doped OCP (Eu-OCP) was synthesized controllably by an improved co-precipitation method. The results showed that Eu-OCP had different fluorescence properties (i.e. emission intensity and I614/I591 ratio) due to the varied crystal field environment compared with Eu3+-doped HAP. Furthermore, the relationship between the I614/I591 ratio and the mass ratio of HAP to OCP in a HAP/OCP mixture was revealed based on a Eu3+ fluorescent probe, which was verified by X-ray diffraction (XRD) analysis. The I614/I591 ratio was related to the content (x) of HAP in the HAP/OCP mixture according to I614/I591 = 2.0971 − 0.70246/(1 + exp(x − 0.68956)/0.11274) (R2 = 0.970). By this fluorescent probe method, the crystal phase transition from OCP to HAP was quantitatively analyzed during the hydrolysis of OCP, which was in accordance with the XRD analysis. Therefore, this fluorescent probe method has potential to monitor crystal phase transition in biomineralization in situ. © 2025 The Royal Society of Chemistry.

关键词： Hydrolysis

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A machine learning approach to predict coefficient of friction of laser cladding high-entropy alloy coatings

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Journal of materials Research and technology 2025年 36卷 8240-8253页

作者： Yawen Pan Bingquan Xu Junjun Wang Jian Peng Zhigang Xu Qiang Shen Chuanbin Wang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology 122 Luoshi Road Wuhan 430070 PR China Hubei Key Laboratory of Plasma Chemistry and Advanced Materials Engineering Research Center of Environmental Materials and Membrane Technology of Hubei Province Wuhan Institute of Technology Wuhan 430074 PR China Chaozhou Branch of Chemistry and Chemical Engineering Guangdong Laboratory Chaozhou Guangdong 521000 PR China

Traditional method for evaluating the coefficient of friction (COF) of laser cladding high-entropy alloy (HEA) coatings, relying on extensive experiments, is costly and inefficient. To address these limitations, this study proposed an efficient machine learning (ML) approach. Random Forest, Gradient Boosting Decision Tree, and XGBoost models were developed as base models to predict the COF of HEA coatings. Feature engineering and a voting strategy were employed to enhance model accuracy and generalization ability, yielding substantial improvements in performance. The optimal model achieved a testing R 2 of 0.97. A satisfactory agreement was achieved between the predicted COFs and the experimental measurements as well as their trend. Additionally, the impact of each feature on the COF was clarified using SHAP (Shapley Additive Explanations). The results indicate that this ML approach is a promising tool for predicting the COF of HEA coatings and can serve as a valuable aid in the design and optimization of coatings.

关键词： High-entropy alloy coating Laser cladding Machine learning Tribology Wear Coefficient of friction

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Machine learning-accelerated discovery of polyimide derivatives for high-temperature electrostatic energy storage

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Energy Storage materials 2025年 78卷

作者： Wan, Chao-Fan Shen, Zhong-Hui Jiang, Jian-Yong Shen, Jie Shen, Yang Nan, Ce-Wen State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center of Smart Materials and Devices Wuhan University of Technology Wuhan430070 China School of Materials and Microelectronics Wuhan University of Technology Wuhan430070 China Wuzhen Laboratory Tongxiang314500 China Longzhong Laboratory Xiangyang441022 China School of Materials Science and Engineering State Key Lab of New Ceramics and Fine Processing Tsinghua University Beijing 100084 China

Molecular engineering of polyimide (PI) has been an effective method for achieving high-performance polymer dielectrics with both good energy storage capability and enhanced thermal stability. However, the rational design of PI derivatives on demand remains a great challenge due to the complex and nonlinear structure-property relationships. To address this challenge, we developed an integrated framework that combines theoretical calculations, advanced molecular descriptors, and machine learning models to study the effect of molecular structures on five key properties of energy gap (Eg), lowest unoccupied molecular orbital (LUMO), dielectric constant (Dk), fractional free volume (FFV) and glass transition temperature (Tg). By employing Artificial Neural Network (ANN), the framework captured nonlinear dependencies between molecular structures and five properties, achieving the prediction accuracy of R2 > 0.90, far surpassing traditional linear models. Using a multi-objective optimization strategy to screen over 200,000 polyimide derivatives, eight optimal molecules with superior properties (e.g., Eg > 4.0 eV, Tg > 300 °C, and Dk > 3.3) were discovered with great potential for applications in high-temperature electrostatic energy storage. This study provides a robust, data-driven approach for multi-property optimization, bridging theoretical insights with machine learning to accelerate the design of advanced polymer dielectrics. © 2025

关键词： Molecular orbitals

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High-entropy alloy nanocrystals boosting photocatalytic hydrogen evolution coupled with selective oxidation of cinnamyl alcohol

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Chinese Journal of Catalysis 2025年第1期68卷 326-335页

作者： Xianglin Xiang Bei Cheng Bicheng Zhu Chuanjia Jiang Guijie Liang Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming 650093YunnanChina State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070HubeiChina Laboratory of Solar Fuel Faculty of Materials Science and ChemistryChina University of GeosciencesWuhan 430078HubeiChina College of Environmental Science and Engineering Nankai UniversityTianjin 300350China Hubei Key Laboratory of Low Dimensional Arts and Science Hubei University of Arts and ScienceXiangyang 441053HubeiChina

Photocatalysis provides a promising solution to the worldwide shortages of energy and industrially important raw materials by utilizing sunlight for coupled hydrogen(H_(2))production with controllable organic ***,we demonstrate that PtFeNiCoCu high-entropy alloy(HEA)nanocrystals can act as efficient cocatalysts for H_(2)evolution coupled with selective oxidation of cinnamyl alcohol to cinnamaldehyde by cubic cadmium sulfide(CdS)quantum dots(QDs)with uniform sizes of 4.0±0.5 *** nanocrystals were prepared via a simple solvothermal approach,and were successfully integrated with CdS QDs by an electrostatic self-assembly method to construct HEA/CdS *** optimized HEA/CdS sample presented an enhanced photocatalytic H_(2)production rate of 7.15 mmol g^(-1)h^(-1),which was 13 times that of pure CdS ***,a cinnamyl alcohol conversion of 96.2%with cinnamaldehyde selectivity of 99.5%was achieved after photoreaction for 3 *** integration of HEA with CdS QDs extended the optical absorption edge from 475 to 484 *** d-band center analysis,Pt atoms in the HEA are the active sites for H_(2)evolution,exhibiting higher catalytic activity than pure ***,the band structure of the CdS QDs enables the oxidative transformation of cinnamyl alcohol to cinnamaldehyde with high ***,femtosecond transient absorption spectroscopy shows that HEA can significantly promote the separation of photogenerated carriers in CdS,which is vital for achieving enhanced photocatalytic *** work inspires atomic-level design of photocatalytic materials for coordinated production of green energy carriers and value-added products.

关键词： Artificial photosynthesis d-Band center Photocatalytic hydrogen evolution Quantum dots Value-added organic synthesis

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Platinum clusters stabilized by zincosilicate zeolite for efficient propane dehydrogenation

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Chinese Journal of Catalysis 2025年第4期71卷 208-219页

作者： Hao Liu Ying Zhang Liyang Liu Tianxiang Chen Xingcong Zhang Peng Hu Chao Xiong Jie Zhou Hao Zhang Lihui Dong Tsz Woon Benedict Lo Bing Nan Xiaohui He Hongbing Ji Key Laboratory of Bioinorganicand Synthetic Chemistry of Ministry of Education Fine Chemical Industry Research InstituteSchool of ChemistryIGCMESun Yat-sen UniversityGuangzhou 510275GuangdongChina Guangxi Key Laboratory of Petrochemical Resource Processing and Process Intensification Technology School of Chemistry and Chemical EngineeringGuangxi UniversityNanning 530004GuangxiChina Shanghai Synchrotron Radiation Facility Shanghai Advanced Research InstituteShanghai 200120China State Key Laboratory of Chemical Biology and Drug Discovery Department of Applied Biology and Chemical TechnologyThe Hong Kong Polytechnic UniversityHunghomHong KongChina State Key Laboratory Breeding Base of Green-Chemical Synthesis Technology Institute of Green Petroleum Processing and Light Hydrocarbon ConversionCollege of Chemical EngineeringZhejiang University of TechnologyHangzhou 310014ZhejiangChina Guangdong Technology Research Center for Synthesis and Separation of Thermosensitive Chemicals Guangzhou 510006GuangdongChina

Dispersing metals from nanoparticles to clusters is often achieved using ligand protection methods,which exhibit unique properties such as suppressing structure-sensitive side ***,this method is limited by the use of different metal precursor salts corresponding to different *** alternative approach,the ion exchange(IE)method,can overcome this limitation to some ***,there is still an urgent need to address the stabilization of metals(especially precious metals)by using IE ***,we reported a Pt cluster catalyst prepared mainly by anchoring Pt atoms via O located near the framework Zn in zincosilicate zeolites and riveted by zeolite surface rings after reduction(reduced Pt/Zn-3-IE).The catalyst can achieve an initial propane conversion of 26%in a pure propane atmosphere at 550℃and shows little deactivation even after 7.5 d of ***,the alteration of catalyst by the introduction of framework Zn was also highlighted and interpreted.

关键词： Zincosilicate zeolite Acid-site identification Rivet effect Pt clusters Propane dehydrogenation

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An interpretable uncertainty quantification framework for proton exchange membrane fuel cell degradation prediction

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Renewable Energy 2025年 249卷

作者： Guo, Bingxin Zhu, Wenchao Yang, Wenlong Yang, Yang Wei, Peng You, Li Xiong, Liangli Xie, Changjun School of Automation Wuhan University of Technology Wuhan430070 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan430070 China Hubei Key Laboratory of Advanced Technology for Automotive Components Wuhan University of Technology Wuhan430070 China State Grid Hubei Electric Power Research Institute Wuhan430077 China National Graduate College for Engineers Wuhan University of Technology Wuhan430119 China

Accurate prediction of fuel cell degradation characteristics is crucial for its control and health diagnosis. However, existing mainstream data-driven methods often fail to adequately account for uncertainty factors during model training and the transient changes in fuel cell characteristics, resulting in predictions that typically provide only point estimates, lacking both credibility and interpretability. To address this, this paper proposes a framework that integrates multi-head attention mechanisms and Bayesian 1D convolution (Bayes-1DCNN-MSH), aimed at quantifying uncertainty in the predictions of various deep learning models while offering interpretable prediction information. This method uses multi-head attention to capture critical multi-dimensional features impacting fuel cell output voltage and applies Bayesian 1D convolution to quantify uncertainty, offering predictions with probability density distributions and confidence intervals for degradation forecasting. The Bayes-1DCNN-MSH structure, validated across six deep learning models, shows a 100 % improvement rate under diverse conditions, significantly enhancing noise resistance and reliability. Experimental results confirm that the framework effectively captures transient feature changes and explains prediction uncertainty through feature probability distributions. This approach improves the reliability and interpretability of fuel cell prediction models, supporting safer integration into sectors like transportation. © 2025 Elsevier Ltd

关键词： Prediction models

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Multi-functionalized sites in metalated COFs film for chemiresistive gas sensing at room temperature

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Chemical Engineering Journal 2025年 514卷

作者： Mei, Aohan Zhou, Min Zhang, Wenyuan Bao, Yunan Liu, Yueli Chen, Wen State Key Laboratory of Silicate Materials for Architectures School of Materials Science and Engineering Wuhan University of Technology Wuhan430070 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing School of Materials Science and Engineering Wuhan University of Technology Wuhan430070 China Wuhan Brilliant Tech Co. Ltd. Wuhan430205 China

Post-synthetic modification of covalent organic frameworks (COFs) has attracted much attention due to its simple introduction of metals and functional groups as active sites for diverse applications. However, the charge transport improvement and the functions of active sites in metalated COFs are so far ignored in gas sensors. Herein, Zn2+ is loaded on two kinds of sites on TpBpy COFs film by a simple photochemical synthesis. Zn2+ coordination improves the NH3 sensitivity by increasing the charge density and adsorption of ammonia molecules. DFT simulation and UPS result prove that the suitable adsorption configuration and work function of Zn@TpBpy COFs film play the key role for the gas sensing behavior. The as-fabricated Zn@TpBpy COFs gas sensors exhibit high sensitivity with linear response behavior and a low detection limit of 500 ppb. By comparing Zn@TpBpy with Cu@TpBpy COFs, the electron donating coordination structure from the fully occupied 3d orbital in Zn2+ boosts the response value and sensing speed, providing a novel insight into manipulating sensing behavior of metalated COFs films. © 2025 Elsevier B.V.

关键词： Chemical sensors

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