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microstructure optimization and electrochemical behavior of in-situ growth Ramsdellite-MnO_(2)@NCA-LDH@CC for supercapacitors and oxygen evolution reaction catalysts

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Journal of Materiomics 2024年第3期10卷 552-565页

作者： Xinpeng Huang Yanli Li Xuehua Yan Feng Zhang Chu Chu Jili Wu Jianmei Pan Zohreh Shahnavaz Jamile Mohammadi Moradian School of Materials Science and Engineering Jiangsu UniversityZhenjiang212013JiangsuChina Institute for Advanced Materials Jiangsu UniversityZhenjiang212013JiangsuChina

Supercapacitors are electrochemical energy storage devices with great potential ***-while,the oxygen evolution reaction(OER)determines the efficiency of some electrochemical energy *** study aims at constructing,exploring,and optimizing Ramsdellite-MnO_(2)@NiCoAl-LDH@CC(R-MNCA@CC)*** effect of microstructure and Al role on the performance is investigated when R-MNCA@CC was used as supercapacitor electrode material and OER ***-like R-MNCA@CC in-situ growth composites were synthesized by a two-step hydrothermal method.R-MNCA@CC-2(molar ratio of Ni:Co:Al is 1:1:1)performs the best with the largest specific capacitance,1,742 F/g at 1 A/g,increased by 797%and 1,489%compared to that of NiCoAl-LDH and Ramsdellite-MnO_(2).The capacitance retention rate of the R-MNCA@CC-2//AC@CC supercapacitor is 80.1%after 5,000 cycles at 0.8 A/*** overpotential for driving an OER to reach 10 m/cm^(2)is only 276 mV,which is lower than that of commercial IrO_(2)(300 mV).Noteworthy,we propose a view that is“competing to trigger redox re-action”of electrochemical active sites in LDH during electrochemical processes derived from a discrepancy between theory and experimental results.

关键词： Supercapacitors Oxygen evolution reaction MnO_(2)@NiCoAl-LDH composites microstructure optimization Electrochemical mechanism

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Development and microstructure optimization of Mg-based metallic glass matrix composites with in situ B2-NiTi dispersoids

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MATERIALS & DESIGN 2015年 83卷 238-248页

作者： Guo, Wei Kato, Hidemi Tohoku Univ Grad Sch Engn Dept Mat Sci Sendai Miyagi 9808579 Japan Tohoku Univ Inst Mat Res Sendai Miyagi 9808577 Japan

In this paper, we designed a novel processing method and successfully developed in situ precipitated B2-NiTi shape-memory-alloy reinforced Mg-based bulk metallic glass matrix composites (BMGMCs). The composites exhibited improved fracture strength in both compressive and flexural modes and large plastic deformation in compressive mode compared with those of its monolithic glassy counterpart. The stress-induced martensitic transformation of B2-NiTi in this system caused the composite to exhibit work-hardening behavior, especially during compression. The microstructure of the composite was optimized by decreasing the size and increasing the volume fraction of the B2-NiTi phase, to obtain better mechanical properties. This novel in situ method is applicable to fabricating various BMGMCs, making it a breakthrough for designing ductile work-hardenable BMGMCs. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Bulk metallic glass Composites Plasticity Work hardening In situ shape-memory-alloy reinforcement microstructure optimization

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Influence of the graphene oxide-coated steel fiber on the microstructure optimization of UHPC

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CONSTRUCTION AND BUILDING MATERIALS 2024年 452卷

作者： Yu, Jiajian Cheng, Zhangjianing Jiang, Hui Xu, Xiaoli Liu, Yanming Gao, Yuan Nantong Univ Sch Transportat & Civil Engn Nantong 226019 Peoples R China Tongji Univ Coll Civil Engn Dept Struct Engn Shanghai 200092 Peoples R China Monash Univ Sch Publ Hlth & Prevent Med Melbourne Vic 3004 Australia

Benefiting from its superior mechanical properties, large specific surface area and abundant oxygen-containing functional groups, graphene oxide (GO) can generate nucleation and pore-infilling effects to optimize the microstructure of the cementitious composites, thus reinforcing the mechanical performance and durability of cement-based materials. However, due to the dispersion issues and relatively high cost, the widespread use of GO in the cement industry has not yet been realized. In the present study, we applied three different methods to coat industrial GO nanosheets on the steel fiber's surface and reinforce the UHPC microstructure. The proposed coating method effectively disperses GO within the ultra-high-performance concrete (UHPC) matrix and targets its enhancement effects on the interfacial transition zone (ITZ), which has the weakest pore structure in UHPC. The result presents that the three-step dip method of GO coating on the steel fiber demonstrates higher effectiveness in reinforcing the microstructure of UHPC, the porosity of the matrix is 1.88 %, with a relative decrease of 37.1 similar to 60.3 %. In addition, the average distance of ITZ after GO enhanced UHPC is about 3.7 mu m, about 37.3 %-68.1 % shorter, and the ITZ porosity is 3.8 %, which is 15.6 %-65.5 % lower. With the assistance of the metal intrusion technique, backscattered electron characterization and deep learning-based analysis, the pore structure features of UHPC were extracted with a classification recognition accuracy of 88.5 %, and the pore intrusion volume inside the characteristic region of the UHPC matrix increased by 1.89-2.14 times. The extraction characteristics illustrated that the GO strengthening on steel fibers was more effective in ITZ, with an optimization efficiency of about 8.1 % higher than in other regions. We expect that the findings of this study could provide deep insights into GO modification for cementitious composite reinforcement applications and open new pat

关键词： Graphene oxide Ultra-high performance concrete Coating Steel fiber microstructure optimization

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A particle packing electrode model for microstructure optimization of lithium-ion batteries

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CHEMICAL ENGINEERING SCIENCE 2024年 295卷

作者： Lin, Yiting Cai, Yunqi Lian, Cheng Liu, Honglai East China Univ Sci & Technol Sch Chem Engn Shanghai 200237 Peoples R China East China Univ Sci & Technol Sch Chem & Mol Engn Shanghai 200237 Peoples R China East China Univ Sci & Technol State Key Lab Chem Engn Shanghai 200237 Peoples R China

The microstructure of porous electrodes has a significant impact on the performance of lithium-ion batteries (LIBs). The critical challenge in theoretical research of porous electrodes is to construct an electrode model that properly describes its microstructure while ensuring solving efficiency. In this work, a particle packing electrode (PPE) model is used to analyze the charging dynamics of LIBs, which is constructed based on the randomness distribution of the active materials. After accurately predicting the experimental data, the PPE model illustrates two key factors governing the C-rate performance: The C-rate performance depends on the Li+ transfer in active material at low C-rate and depends on Li+ transfer in the electrolyte at high C-rate. By optimizing the uniformity of the particle size and the orderliness of the pore structure, LIBs achieve improved performance. This model offers novel possibilities for designing and optimizing electrode microstructures of LIBs.

关键词： Performance modeling Porous electrodes Particle packing electrode model microstructure optimization Lithium-ion batteries

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microstructure optimization in the Shear-Exfoliated Bi6Cu2Se4O6 through Introducing Reduced Graphene Oxide Leads to Wide-Ranged Thermoelectric Performance

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ADVANCED FUNCTIONAL MATERIALS 2024年第33期34卷 2401735-2401735页

作者： Zheng, Junqing Wen, Yi Wang, Sining Li, Yichen Wang, Siqi Zhao, Zhe Liu, Shan Liu, Shibo Gao, Xiang Zhao, Li-Dong Beihang Univ Sch Mat Sci & Engn Beijing 100191 Peoples R China Ctr High Pressure Sci & Technol Adv Res HPSTAR Beijing 100094 Peoples R China Tianmushan Lab Hangzhou 311115 Peoples R China

Bi6Cu2Se4O6 is considered as an ideal n-type thermoelectric material to pair with p-type BiCuSeO for preparing oxyselenide-based thermoelectric devices, but its thermoelectric performance is limited by poor electrical conductivity. In this research, the reduced graphene oxide (rGO) nanosheets are introduced into Bi6Cu2Se3.6Cl0.4O6 matrix through liquid-phase shear exfoliation to modify the microstructure. rGO can insert into matrix grains as intercalations, or embed into grain boundaries as wetting phase, and prompt grain alignment, which contributes to the significantly enhanced carrier mobility, thus leading to an improvement in electrical conductivity from approximate to 15 S cm(-1) to approximate to 230 S cm(-1) at 303 K. Whereafter, the effective donor dopant Nb is chosen to substitute Bi. The carrier concentration is increased without damaging the carrier mobility, resulting in a further improved electrical conductivity of approximate to 840 S cm(-1) at 303 K. Lattice thermal conductivity is also suppressed owing to the intensive phonon scattering by point defects and grain boundaries. Ultimately, a record-breaking peak ZT approximate to 0.5 (873 K) and average ZT approximate to 0.3 (303-873 K) can be achieved in Bi5.91Nb0.09Cu2Se3.6Cl0.4O6 + 0.5% rGO. The microstructure optimization method in this research effectively improves thermoelectric performance, and is anticipated to be applied in other thermoelectrics.

关键词： Bi6Cu2Se4O6 carrier mobility microstructure optimization shear exfoliation thermoelectrics

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Hydrogen-induced microstructure optimization and mechanical properties enhancement of forged (TiB + TiC)/Ti6Al4V

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MATERIALS CHARACTERIZATION 2023年第1期202卷

作者： Jiang, Botao Wang, Liang Yan, Hui Wang, Binbin Luo, Liangshun Chen, Ruirun Su, Yanqing Guo, Jingjie Fu, Hengzhi Harbin Inst Technol Sch Mat Sci & Engn Natl Key Lab Precis Hot Proc Met Harbin 150001 Peoples R China

This study investigates the impact of hydrogen on the mechanical properties and microstructure of forged titanium matrix composites (TMCs) reinforced with 2.5 vol% TiB and 2.5 vol% TiC. Melt hydrogenation technology was used to fabricate the TMCs with different hydrogen contents, which were then forged at 880 degrees C. Microstructural analysis revealed that hydrogen accelerated atomic diffusion, resulting in an approximate network microstructure. Hydrogen also promoted dynamic recrystallization during forging, leading to grain refinement and reduced dislocation density. Room temperature tensile tests showed that hydrogen significantly improved the strength and ductility of the forged TMCs within a certain range of hydrogen content. The greatest improvement was observed in the forged TMCs with a hydrogen content of 7.87 x 10-2 wt%, which exhibited a 10.48% increase in yield strength and a 31.36% increase in ductility relative to those without hydrogen. The effects of hydrogenation on crack propagation mechanisms are also discussed in detail. Overall, these findings provide valuable insights into the strengthening and toughening of forged titanium matrix composites through hydrogenation.

关键词： Melt hydrogenation Titanium matrix composites microstructure optimization Mechanical properties enhancement Forged

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Modeling of solid oxide fuel cell (SOFC) electrodes from fabrication to operation: microstructure optimization via artificial neural networks and multi-objective genetic algorithms

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ENERGY CONVERSION AND MANAGEMENT 2019年 198卷 111916-000页

作者： Yan, Z. He, A. Hara, S. Shikazono, N. Univ Tokyo Inst Ind Sci Meguro Ku Komaba 4-6-1 Tokyo 1538505 Japan Chiba Inst Technol Dept Mech Engn Fac Engn Tsudanuma 2-17-1 Narashino Chiba 2750016 Japan

In this study, a modeling framework is proposed for the optimization of the solid oxide fuel cell (SOFC) electrode microstructures. This involves sequential simulations of the SOFCs from initial powder to final electrochemical performance with artificial intelligence-assisted multi-objective optimization. The effects of starting powder parameters such as particle size, particle size distribution (PSD) and pore former content on cathodic overpotential and degradation rate of SOFCs are studied. It is shown that fine particle size and/or low pore former content lead to low cathodic overpotential but high degradation rate in the investigated range of the parameters. Predictive models for the cathode overpotential and degradation rate are established by an artificial neural network using the simulation data. The Sobol global sensitivity study suggests that particle size and pore former content play important roles in determination of the cathode overpotential and degradation rate while the PSD effect is insignificant. A multi-objective genetic algorithm (MOGA) is used to minimize both the overpotential and degradation rate of the cathode. The Pareto front is obtained for the optimal design of cathode microstructures. Compared to the grid search method, the MOGA proves to be more robust and efficient for SOFC electrode microstructure optimization.

关键词： Solid oxide fuel cells microstructure optimization Artificial neural network Parameter sensitivity Multi-objective genetic algorithm

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Integrated computational framework for the optimization of the microstructure in additive manufacturing of metals

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COMPUTATIONAL MATERIALS SCIENCE 2025年 256卷

作者： Fachinotti, Victor D. Gouttebrozze, Sylvain Dumoulin, Stephane Ren, Xiaobo SINTEF Ind Dept Met Prod & Proc Forskningsveien 1 N-0373 Oslo Norway SINTEF Ind Dept Mat & Nanotechnol Richard Birkelands vei 2B N-7034 Trondheim Norway

We present an integral multiscale computational framework for the optimization of the microstructure in metal additive manufacturing. It consists of four modules: (i) the optimization solver that systematically generates feasible designs, (ii) the macroscale module for determining temperature evolution along the deposition process and further cooling, (iii) the microscale module that computes the evolution of microstructure in the deposited part for the temperature histories computed in the previous step, and (iv) the assessment module that quantifies how good the microstructure of the as-deposited part is for each design. The macroscale module uses ABAQUS for the finite element analysis of nonlinear transient heat transfer. The microscale module is a fast metamodel of the multiphase-field model for multicomponent alloys from the microstructure simulation software MICRESS. This metamodel is based on the Johnson-Mehl-Avrami-Kolmogorov law for isothermal transformations, calibrated using MICRESS' results, and extended to non-isothermal transformations by approximating them as a series of isothermal steps. The whole workflow is implemented into ISIGHT, a user-friendly software that provides a suite of visual tools to create simulation process flows. Finally, the laser directed energy deposition of duplex stainless steels, whose mechanical properties are highly dependent on the ferrite-austenite ratio, is taken as case study. Results show that the microstructure of the as-deposited part can be significantly improved.

关键词： microstructure optimization Integrated computational framework Additive manufacturing Duplex stainless steel ICME

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Product design: An optimization-based approach for targeting of particulate composite microstructure

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COMPUTERS & CHEMICAL ENGINEERING 2020年 140卷 106975-106975页

作者： Mushtaq, Faheem Zhang, Xiang Fung, Ka Yip Ng, Ka Ming Hong Kong Univ Sci & Technol Dept Chem & Biol Engn Clear Water Bay Hong Kong Peoples R China iCreate Ltd 18-F1 Lockhart Rd Hong Kong Peoples R China

The microstructure of a particulate composite with given ingredients has enormous influence on its physicochemical properties. An optimization-based approach is presented for determining the target of two-component particulate composite microstructure. The dominant physicochemical phenomena are captured by the relevant gradient-based transport equations. Particles of different size, shape, and orientation are placed in the composite to maximize a desired physicochemical property. The formulated problem is readily solved via mixed-integer non-linear programming. A case study is performed on the design of a die attach adhesive the objective of which is to obtain the maximum effective thermal conductivity at different loadings of the nano filler particles. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Structure-property relationship microstructure optimization Particulate composite Die attach adhesive

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Neural network-driven framework for efficient microstructural modeling of particle-enriched composites

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MATERIALS TODAY COMMUNICATIONS 2025年 42卷

作者： Barai, Shishir Liu, Feihong Kumar, Manik Peco, Christian Penn State Univ Dept Engn Sci & Mech Univ Pk State Coll PA 16802 USA Penn State Univ Inst Computat & Data Sci Univ Pk State Coll PA 16802 USA

Simulating materials with complex, variable microstructures like nanoparticle-enriched matrices presents significant challenges for FEM, particularly in defining constitutive equations across diverse loading conditions and internal structures. These complexities often make purely physics-based FEM simulations impractical for larger macroscopic domains. We present a novel approach that significantly reduces the dimensionality of the FEM data, while preserving the ability to interpolate and extrapolate across various nanoparticle configurations. Our method combines raw data projection, neural network surrogates, and convolutional autoencoders to geometrically normalize the data in a reduced-dimensional space. This reduction enables efficient training and prediction, while retaining flexibility for different microstructural variations. Compared to direct training on unprocessed data, our approach reduces dimensionality and computational costs without sacrificing accuracy, allowing robust virtual testing of particle-enriched composites. This framework can be applied to other multiscale composite systems for optimized material design.

关键词： Autoencoders Dimensionality reduction Finite element analysis High performance computing microstructure optimization

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