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Journal of Materials Science & Technology 2023年第20期151卷 245-257页

作者： Shiyu Huang Heng Liu yanjing su Lijie Qiao Yu Yan Beijing Advanced Innovation Center for Materials Genome Engineering Corrosion and Protection CenterInstitute for Advanced Materials and TechnologyUniversity of Science and TechnologyBeijing 100083China

In this work,trace Li was introduced to strengthen Zn–4Cu *** results indicated that trace amounts of Li contributed to a significant increase in strength,resulting in an acceptable loss of elongation at ***,Li in the form of LiZn_(4) led to more intensive galvanic corrosion,which accelerated the early corrosion *** release of a large amount of Zn^(2+),caused by the addition of Li,affected the phase composition of the main Zn-containing corrosion ***,the inhibition effect of the alloy on Staphylococcus aureus(***)was enhanced by the addition of 0.02 wt.%Li.

关键词： Microalloying Zn-Cu alloy LiZn_(4)/Zn pairs Biodegradable mechanism SKPFM

Correlation between multi-factor phase diagrams and complex electrocaloric behaviors in PNZST antiferroelectric ceramic system

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Journal of Advanced Ceramics 2023年第3期12卷 463-473页

作者： Junjie Li Ruowei Yin Jianting Li Xiaopo su yanjing su Lijie Qiao Yang Baia Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China Sichuan Province Key Laboratory of Information Materials and Devices Application College of Optoelectronic EngineeringChengdu University of Information TechnologyChengdu 610225China Faculty of Mechanical Engineering Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Ferroelectric(FE)phase transition with a large polarization change benefits to generate large electrocaloric(EC)effect for solid-sate and zero-carbon cooling ***,most EC studies only focus on the single-physical factor associated phase ***,we initiated a comprehensive discussion on phase transition in Pb_(0.99)Nb_(0.02)[(Zr_(0.6)Sn_(0.4))1−xTix]_(0.98)O_(3)(PNZST100x)antiferroelectric(AFE)ceramic system under the joint action of multi-physical factors,including composition,temperature,and electric *** to low energy barrier and enhanced zero-field entropy,the multi-phase coexistence point(x=0.12)in the composition–temperature phase diagram yields a large positive EC peak of maximum temperature change(ΔT_(max))=2.44 K(at 40 kV/cm).Moreover,the electric field–temperature phase diagrams for four representative ceramics provide a more explicit guidance for EC evolution *** the positive EC peaks near various phase transition temperatures,giant positive EC effects are also brought out by the electric field-induced phase transition from tetragonal AFE(AFET)to low-temperature rhombohedral FE(FER),which is reflected by a positive-slope boundary in the electric field–temperature phase diagram,while significant negative EC responses are generated by the phase transition from AFET to high-temperature multi-cell cubic paraelectric(PEMCC)with a negative-slope phase *** work emphasizes the importance of phase diagram covering multi-physical factors for high-performance EC material design.

关键词： antiferroelectric(AFE) electrocaloric(EC)effect phase diagram phase transition

A Vision of Materials Genome Engineering in China

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Engineering 2022年第3期8卷 10-12页

作者： Jianxin Xie yanjing su Dawei Zhang Qiang Feng Beijing Advanced Innovation Center for Materials Genome Engineering&Institute for Advanced Materials and Technology&State Key Laboratory for Advanced Metals and Materials University of Science and Technology BeijingBeijing 100083China

In the new round of global scientific and technological revolution and industrial transformation,one of the biggest bottlenecks we are facing is the lack of adequate materials *** is urgently hoped that there can be a major revolution in the materials research and development(R&D)field,rather than gradual and slow development[1].Under this background,materials genome engineering(MGE)has been developing rapidly,and its key technologies and engineering applications have become the frontiers of materials research[2].

关键词： technology frontier revolution

Alloy synthesis and processing by semi-supervised text mining

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npj Computational Materials 2023年第1期9卷 459-470页

作者： Weiren Wang Xue Jiang Shaohan Tian Pei Liu Turab Lookman yanjing su Jianxin Xie Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology Beijing100083 BeijingChina Collaborative Innovation Center of Steel Technology University of Science and Technology Beijing100083 BeijingChina Liaoning Academy of Materials Shenyang 110000 LiaoningChina Beijing Key Laboratory of Advanced High Temperature Materials Central Iron&Steel Research Institute100081 BeijingChina Beijing GAONA Materials&Technology Co. LTD100081 BeijingChina AiMaterials Research LLC Santa FeNM 87501USA

Alloy synthesis and processing determine the design of alloys with desired microstructure and ***,using data science to identify optimal synthesis-design routes from a specified set of starting materials has been limited by large-scale data *** mining has made it possible to convert scientific text into structured data ***,the complexity,diversity,and flexibility of synthesis and processing expressions,and the lack of annotated corpora with a gold standard severely hinder accurate and efficient *** we introduce a semi-supervised text mining method to extract the parameters corresponding to the sequence of actions of synthesis and *** automatically extract a total of 9853 superalloy synthesis and processing actions with chemical compositions from a corpus of 16,604 superalloy articles published up to *** have then been used to capture an explicitly expressed synthesis factor for predictingγ′phase *** synthesis factor derived from text mining significantly improves the performance of the data-drivenγ′size prediction *** method thus complements the use of data-driven approaches in the search for relationships between synthesis and structures.

关键词： superalloy microstructure synthesis

Symbolic regression in materials science via dimension-synchronous-computation

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Journal of Materials Science & Technology 2022年第27期122卷 77-83页

作者： Changxin Wang Yan Zhang Cheng Wen Mingli Yang Turab Lookman yanjing su Tong-Yi Zhang Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China Corrosion and Protection Center University of Science and Technology BeijingBeijing 100083China Research Center for Materials Genome Engineering Sichuan UniversityChengdu 610065China Ai Materials Research LLC Santa FeNM 87501United States of America School of Materials Science and Engineering Harbin Institute of TechnologyShenzhenChina Materials Genome Institute Shanghai University333 Nanchen RoadShanghai 200444China

There is growing interest in applying machine learning techniques in the field of materials ***,the interpretation and knowledge extracted from machine learning models is a major concern,particularly as formulating an explicit model that provides insight into physics is the goal of *** the present study,we propose a framework that utilizes the filtering ability of feature engineering,in conjunction with symbolic regression to extract explicit,quantitative expressions for the band gap energy from materials *** propose enhancements to genetic programming with dimensional consistency and artificial constraints to improve the search efficiency of symbolic *** show how two descriptors attributed to volumetric and electronic factors,from 32 possible candidates,explicitly express the band gap energy of Na Cl-type *** approach provides a basis to capture underlying physical relationships between materials descriptors and target properties.

关键词： Symbolic regression Band gap Dimensional calculation

Automated pipeline for superalloy data by text mining

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npj Computational Materials 2022年第1期8卷 58-69页

作者： Weiren Wang Xue Jiang Shaohan Tian Pei Liu Depeng Dang yanjing su Turab Lookman Jianxin Xie Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing100083China Collaborative Innovation Center of Steel Technology University of Science and Technology BeijingBeijing100083China Beijing Key Laboratory of Materials Genome Engineering University of Science and Technology BeijingBeijing100083China School of Artificial Intelligence Beijing Normal UniversityBeijing100875China AiMaterials Research LLC Santa FeNM87501USA

Data provides a foundation for machine learning,which has accelerated data-driven materials *** scientific literature contains a large amount of high-quality,reliable data,and automatically extracting data from the literature continues to be a *** propose a natural language processing pipeline to capture both chemical composition and property data that allows analysis and prediction of *** 3 h,2531 records with both composition and property are extracted from 14,425 articles,coveringγ′solvus temperature,density,solidus,and liquidus temperatures.A data-driven model forγ′solvus temperature is built to predict unexplored Co-based superalloys with highγ′solvus temperatures within a relative error of 0.81%.We test the predictions via synthesis and characterization of three alloys.A web-based toolkit as an online open-source platform is provided and expected to serve as the basis for a general method to search for targeted materials using data extracted from the literature.

关键词： superalloy property pipeline

Remarkably enhanced piezo-photocatalytic performance in BaTiO_(3)/CuO heterostructures for organic pollutant degradation

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Journal of Advanced Ceramics 2022年第3期11卷 414-426页

作者： Chenye YU Mengxi TAN Chengdong TAO Yuxuan HU Chuanbao LIU Huimin MENG yanjing su Lijie QIAO Yang BAI Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China Institute for Advanced Material and Technology University of Science and Technology BeijingBeijing 100083China School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083China

Introducing polarization field of piezoelectric materials is an effective strategy to improve photocatalytic *** this study,a new type of BaTiO_(3)/CuO heterostructure catalyst was designed and synthesized to achieve high piezo-photocatalytic activity through the synergy of heterojunction and piezoelectric *** BaTiO_(3)/CuO heterostructure shows a significantly enhanced piezo-photocatalytic degradation efficiency of organic pollutants compared with the individual BaTiO_(3) nanowires(NWs)and CuO nanoparticles(NPs).Under the co-excitation of ultrasonic vibration and ultraviolet radiation,the optimal degradation reaction rate constant k of polarized BaTiO_(3)/CuO heterostructure on methyl orange(MO)dye can reach 0.05 min^(−1),which is 6.1 times of photocatalytic rate and 7 times of piezocatalytic *** BaTiO_(3)/CuO heterostructure with remarkable piezo-photocatalytic behavior provides a promising strategy for the development of high-efficiency catalysts for wastewater purification,and it also helps understand the coupling mechanism between piezoelectric effect and photocatalysis.

关键词： piezotronics photocatalysis polarization heterostructure

Large electrocaloric effect over a wide temperature span in lead-free bismuth sodium titanate-based relaxor ferroelectrics

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Journal of Materiomics 2023年第2期9卷 289-298页

作者： Xiaopo su Junjie Li Yuxuan Hou Ruowei Yin Jianting Li Shiqiang Qin yanjing su Lijie Qiao Chuanbao Liu Yang Bai Beijing Advanced Innovation Center for Materials Genome Engineering and Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing100083China School of Materials Science and Engineering University of Science and Technology BeijingBeijing100083China College of Optoelectronic Engineering Chengdu University of Information TechnologyChengdu610225China School of Mechanical and Automotive Engineering Qilu University of Technology(Shandong Academy of Sciences)Jinan250353China

For efficient solid-state refrigeration technologies based on electrocaloric effect(ECE),it is a great challenge of simultaneously obtaining a large adiabatic temperature change(DT)within a wide temperature span(Tspan)in lead-free ferroelectric ***,we studied the electrocaloric effect(ECE)in(1-x)(Na_(0.5)Bi_(0.5))TiO_(3)-xCaTiO_(3)((1-x)NBT-xCT)and explored the combining effect of morphotropic phase boundary(MPB)and relaxor *** addition of CT not only constructs a MPB region with the coexistence of rhombohedral and orthorhombic phases,but also enhances the relaxor *** ECE peak appears around the freezing temperature(Tf),and shifts toward to lower temperature with the increasing CT *** directly measured ECE result shows that the ceramic of x=0.10,which is in the MPB region,has an optimal ECE property of DTmax=1.28 K@60℃under 60 kV/cm with a wide Tspan of 65*** enhanced ECE originates from the electric-field-induced transition between more types of polar nanoregions and long-range ferroelectric *** the composition with more relaxor feature in the MPB region,such as x?0.12,the ECE is relatively weak under low electric fields but it exhibits a sharp increment under a sufficiently high electric *** work provides a guideline to develop the solidestate cooling devices for electronic components.

关键词： Electrocaloric effect Relaxor ferroelectric Morphotropic phase boundary (Na_(0.5)Bi_(0.5))TiO_(3)

脱脂辣木籽提取物成分分析及其对体外瘤胃发酵和细菌群落结构的影响

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动物营养学报 2024年第8期36卷 5152-5169页

作者：宗玉洁程志强孙善涛武中秀周晓洁刘丽吉慧敏苏衍菁林淼扬州大学动物科学与技术学院扬州225009 光明牧业有限公司农业农村部南方奶牛遗传改良重点实验室上海200436

本试验旨在研究脱脂辣木籽提取物成分组成及其对体外瘤胃发酵和细菌群落结构的影响。试验采用脱脂辣木籽粉为试验材料,通过液相色谱-质谱(LC-MS)法测定乙醇相和水相2种提取物的成分差异,并采用体外模拟瘤胃发酵技术,分别设置乙醇和水提... 详细信息

本试验旨在研究脱脂辣木籽提取物成分组成及其对体外瘤胃发酵和细菌群落结构的影响。试验采用脱脂辣木籽粉为试验材料,通过液相色谱-质谱(LC-MS)法测定乙醇相和水相2种提取物的成分差异,并采用体外模拟瘤胃发酵技术,分别设置乙醇和水提取物的添加剂量为0(对照)、1.875、3.750和7.500 mg/kg(n=5),厌氧发酵72 h,测定其对体外瘤胃发酵发酵参数和菌群结构的影响。结果表明:1)脱脂辣木籽乙醇和水提取物的产率分别为12.66%和38.90%;与水提取物相比,乙醇提取物含有较高的类黄酮和异黄酮类化合物,但含有较低的苯类和酚类物质。2)与对照组相比,乙醇提取物添加组干物质降解率显著降低(P<0.05)。3)添加1.875和3.750 mg/kg乙醇提取物培养6 h后,各时间点产气量均显著低于对照组(P<0.05);水提取物添加组各时间点产气量均显著高于对照组(P<0.05)。4)与对照组相比,添加脱脂辣木籽乙醇提取物显著提高了发酵液乳酸含量(P<0.05);添加1.875和3.750 mg/kg乙醇提取物显著提高了发酵液丙酸比例(P<0.05),显著降低了发酵液异丁酸、丁酸、异戊酸、戊酸和己酸比例(P<0.05)。与对照组相比,添加脱脂辣木籽水提取物显著降低了发酵液pH和乙酸比例(P<0.05),显著提高了发酵液乳酸和总短链脂肪酸含量以及丙酸比例(P<0.05);添加1.875和3.750 mg/kg水提取物显著降低了发酵液氨态氮含量和戊酸比例(P<0.05),显著提高了发酵液异戊酸比例(P<0.05)。5)与对照组相比,添加1.875 mg/kg乙醇提取物显著降低了理研菌科RC9肠道群(Rikenellaceae_RC9_gut_group)、克里斯滕森菌科R-7群(Christensenellaceae_R-7_group)和毛螺菌科NK3A20群(Lachnospiraceae_NK3A20_group)相对丰度(P<0.05),显著提高了拟杆菌属(Bacteroides)、埃希氏-志贺氏菌属(Escherichia-Shigella)和不动杆菌属(Acinetobacter)相对丰度(P<0.05);而添加3.750 mg/kg水提取物添加显著提高了克里斯滕森菌科R-7群和毛螺菌科NK3A20群相对丰度(P<0.05)。综上所述,脱脂辣木籽乙醇和水提取物营养成分和活性物质含量存在差异,其中,乙醇提取物中的黄酮类物质可能会抑制瘤胃发酵、减少有益菌数量,而水提取物可以促进瘤胃发酵。

关键词：辣木籽提取物瘤胃发酵细菌群落体外

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Machine learning assisted prediction of dielectric temperature spectrum of ferroelectrics

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Journal of Advanced Ceramics 2023年第9期12卷 1793-1804页

作者： Jingjin He Changxin Wang Junjie Li Chuanbao Liu Dezhen Xue Jiangli Cao yanjing su Lijie Qiao Turab Lookman Yang Bai Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China Institute for Advanced Material and Technology University of Science and Technology BeijingBeijing 100083China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming 650093China Sichuan Province Key Laboratory of Information Materials and Devices Application College of Optoelectronic EngineeringChengdu University of Information TechnologyChengdu 610225China School of Materials Science and Technology University of Science and Technology BeijingBeijing 100083China State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an 710049China AiMaterials Research LLC Santa FeNM 87501USA

In material science and engineering,obtaining a spectrum from a measurement is often time-consuming and its accurate prediction using data mining can also be *** this work,we propose a machine learning strategy based on a deep neural network model to accurately predict the dielectric temperature spectrum for a typical multi-component ferroelectric system,i.e.,(Ba_(1−x−y)Ca_(x)Sr_(y))(Ti_(1−u−v−w)Zr_(u)Sn_(v)Hf_(w))O_(3).The deep neural network model uses physical features as inputs and directly outputs the full spectrum,in addition to yielding the octahedral factor,Matyonov–Batsanov electronegativity,ratio of valence electron to nuclear charge,and core electron distance(Schubert)as four key *** to the physically meaningful features,our model exhibits better performance and generalization ability in the broader composition space of BaTiO3-based solid *** the prediction accuracy is superior to traditional machine learning models that predict dielectric permittivity values at each ***,the transition temperature and the degree of dispersion of the ferroelectric phase transition are easily extracted from the predicted spectra to provide richer physical *** prediction is also experimentally validated by typical samples of(Ba_(0.85)Ca_(0.15))(Ti_(0.98–x)Zr_(x)Hf_(0.02))O_(3).This work provides insights for accelerating spectra predictions and extracting ferroelectric phase transition information.

关键词： machine learning(ML) dielectric temperature spectrum ferroelectrics phase transition information