About us:The College of Chemistry and Materials Engineering(CME)in Wenzhou University(Zhejiang Province,China)is looking for postdoctoral candidates(up to 25)specialized in Chemistry,Chemical Engineering and Materials...About us:The College of Chemistry and Materials Engineering(CME)in Wenzhou University(Zhejiang Province,China)is looking for postdoctoral candidates(up to 25)specialized in Chemistry,Chemical Engineering and Materials Science.The collegehas its Chemistry program ranking ESI Top 6‰ worldwide,and Materials Scienceprogram ranking 589th in the world since 2023.展开更多
About us:The College of Chemistry and Materials Engineering(CME)in Wenzhou University(Zhejiang Province,China)is looking for postdoctoral candidates(up to 25)specialized in Chemistry,Chemical Engineering and Materials...About us:The College of Chemistry and Materials Engineering(CME)in Wenzhou University(Zhejiang Province,China)is looking for postdoctoral candidates(up to 25)specialized in Chemistry,Chemical Engineering and Materials Science.The college has its Chemistry program ranking ESI Top 6%o worldwide,and Materials Science program ranking 589th in the world since2023.The college has led publications appearing in journals such as Nat.Catal.,Nat.Commun.,Sci.Adv.,J.Am.Chem.Soc.,Angew.Chem.展开更多
Correction to:Nano-Micro Letters(2025)17:135 https://doi.org/10.1007/s40820-024-01634-8 Following publication of the original article[1],the authors reported that the corresponding author would like to update the emai...Correction to:Nano-Micro Letters(2025)17:135 https://doi.org/10.1007/s40820-024-01634-8 Following publication of the original article[1],the authors reported that the corresponding author would like to update the email address from xingcai@stanford.edu to drtea1@wteao.com.Also,the corresponding author’s affiliation can be expanded.展开更多
From the club-like limbs of the mantis shrimp to the texture of a cicada's wing,recently reported studies of the structures found in living creatures are fostering innovations in synthetic materials.The work shows...From the club-like limbs of the mantis shrimp to the texture of a cicada's wing,recently reported studies of the structures found in living creatures are fostering innovations in synthetic materials.The work shows how biological research can underpin the development of novel materials that adopt key attributes or functions of natural substances with the goal of applying them in improved,often high-technology products[1].展开更多
Digital twins(DTs)are rapidly emerging as transformative tools in materials science and engineering,enabling real-time data integration,predictive modeling,and virtual testing.This study presents a systematic bibliome...Digital twins(DTs)are rapidly emerging as transformative tools in materials science and engineering,enabling real-time data integration,predictive modeling,and virtual testing.This study presents a systematic bibliometric review of 1106 peer-reviewed articles published in the last decade in Scopus and Web of Science.Using a five-stage methodology,the review examines publication trends,thematic areas,citation metrics,and keyword patterns.The results reveal exponential growth in scientific output,with Materials Theory,Computation,and Data Science as the most represented area.A thematic analysis of the most cited documents identifies four major research streams:foundational frameworks,DTs in additive manufacturing,sector-specific applications,and intelligent production systems.Keyword co-occurrence and strategic mapping show a strong foundation in modeling,simulation,and optimization,with growing links to machine learning and sustainability.The review highlights current challenges and proposes future research directions for advancing DTs in materials science.展开更多
The recent developments of electron tomography（ET） based on transmission electron microscopy（TEM） and scanning transmission electron microscopy（STEM） in the field of materials science were introduced. The variou...The recent developments of electron tomography（ET） based on transmission electron microscopy（TEM） and scanning transmission electron microscopy（STEM） in the field of materials science were introduced. The various types of ET based on TEM as well as STEM were described in detail, which included bright-field（BF）-TEM tomography, dark-field（DF）-TEM tomography, weak-beam dark-field（WBDF）-TEM tomography, annular dark-field（ADF）-TEM tomography, energy-filtered transmission electron microscopy（EFTEM） tomography, high-angle annular dark-field（HAADF）-STEM tomography, ADF-STEM tomography, incoherent bright field（IBF）-STEM tomography, electron energy loss spectroscopy（EELS）-STEM tomography and X-ray energy dispersive spectrometry（XEDS）-STEM tomography, and so on. The optimized tilt series such as dual-axis tilt tomography, on-axis tilt tomography, conical tilt tomography and equally-sloped tomography（EST） were reported. The advanced reconstruction algorithms, such as discrete algebraic reconstruction technique（DART）, compressed sensing（CS） algorithm and EST were overviewed. At last, the development tendency of ET in materials science was presented.展开更多
In this paper,the main research work and related reports of materials science research in China’s space technology field during 2020-2022 are summarized.This paper covers Materials Sciences in Space Environment,Mater...In this paper,the main research work and related reports of materials science research in China’s space technology field during 2020-2022 are summarized.This paper covers Materials Sciences in Space Environment,Materials Sciences for Space Environment,Materials Behavior in Space Environment and Space experimental hardware for material investigation.With the rapid development of China’s space industry,more scientists will be involved in materials science,space technology and earth science researches.In the future,a series of disciplines such as space science,machinery,artificial intelligence,digital twin and big data will be further integrated with materials science,and space materials will also usher in new development opportunities.展开更多
The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our a...The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence(GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge data.This specialized AI model, named Mat Chat, focuses on predicting inorganic material synthesis pathways. Mat Chat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although Mat Chat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. Mat Chat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science.展开更多
Activities of space materials science research in China have been continuously supported by two main national programs.One is the China Space Station(CSS)program since 1992,and the other is the Strategic Priority Prog...Activities of space materials science research in China have been continuously supported by two main national programs.One is the China Space Station(CSS)program since 1992,and the other is the Strategic Priority Program(SPP)on Space Science since 2011.In CSS plan in 2019,eleven space materials science experimental projects were officially approved for execution during the construction of the space station.In the SPP Phase Ⅱ launched in 2018,seven pre-research projects are deployed as the first batch in 2018,and one concept study project in 2019.These pre-research projects will be cultivated as candidates for future selection as space experiment projects on the recovery of scientific experimental satellites in the future.A new apparatus of electrostatic levitation system for ground-based research of space materials science and rapid solidification research has been developed under the support of the National Natural Science Foundation of China.In order to promote domestic academic activities and to enhance the advancement of space materials science in China,the Space Materials Science and Technology Division belong to the Chinese Materials Research Society was established in 2019.We also organized scientists to write five review papers on space materials science as a special topic published in the journal Scientia Sinica to provide valuable scientific and technical references for Chinese researchers.展开更多
The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classificatio...The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.展开更多
文摘About us:The College of Chemistry and Materials Engineering(CME)in Wenzhou University(Zhejiang Province,China)is looking for postdoctoral candidates(up to 25)specialized in Chemistry,Chemical Engineering and Materials Science.The collegehas its Chemistry program ranking ESI Top 6‰ worldwide,and Materials Scienceprogram ranking 589th in the world since 2023.
文摘About us:The College of Chemistry and Materials Engineering(CME)in Wenzhou University(Zhejiang Province,China)is looking for postdoctoral candidates(up to 25)specialized in Chemistry,Chemical Engineering and Materials Science.The college has its Chemistry program ranking ESI Top 6%o worldwide,and Materials Science program ranking 589th in the world since2023.The college has led publications appearing in journals such as Nat.Catal.,Nat.Commun.,Sci.Adv.,J.Am.Chem.Soc.,Angew.Chem.
文摘Correction to:Nano-Micro Letters(2025)17:135 https://doi.org/10.1007/s40820-024-01634-8 Following publication of the original article[1],the authors reported that the corresponding author would like to update the email address from xingcai@stanford.edu to drtea1@wteao.com.Also,the corresponding author’s affiliation can be expanded.
文摘From the club-like limbs of the mantis shrimp to the texture of a cicada's wing,recently reported studies of the structures found in living creatures are fostering innovations in synthetic materials.The work shows how biological research can underpin the development of novel materials that adopt key attributes or functions of natural substances with the goal of applying them in improved,often high-technology products[1].
文摘Digital twins(DTs)are rapidly emerging as transformative tools in materials science and engineering,enabling real-time data integration,predictive modeling,and virtual testing.This study presents a systematic bibliometric review of 1106 peer-reviewed articles published in the last decade in Scopus and Web of Science.Using a five-stage methodology,the review examines publication trends,thematic areas,citation metrics,and keyword patterns.The results reveal exponential growth in scientific output,with Materials Theory,Computation,and Data Science as the most represented area.A thematic analysis of the most cited documents identifies four major research streams:foundational frameworks,DTs in additive manufacturing,sector-specific applications,and intelligent production systems.Keyword co-occurrence and strategic mapping show a strong foundation in modeling,simulation,and optimization,with growing links to machine learning and sustainability.The review highlights current challenges and proposes future research directions for advancing DTs in materials science.
基金Projects(51071125,51201135)supported by the National Natural Science Foundation of ChinaProject(B08040)supported by the Program of Introducing Talents of Discipline to Universities,China
文摘The recent developments of electron tomography（ET） based on transmission electron microscopy（TEM） and scanning transmission electron microscopy（STEM） in the field of materials science were introduced. The various types of ET based on TEM as well as STEM were described in detail, which included bright-field（BF）-TEM tomography, dark-field（DF）-TEM tomography, weak-beam dark-field（WBDF）-TEM tomography, annular dark-field（ADF）-TEM tomography, energy-filtered transmission electron microscopy（EFTEM） tomography, high-angle annular dark-field（HAADF）-STEM tomography, ADF-STEM tomography, incoherent bright field（IBF）-STEM tomography, electron energy loss spectroscopy（EELS）-STEM tomography and X-ray energy dispersive spectrometry（XEDS）-STEM tomography, and so on. The optimized tilt series such as dual-axis tilt tomography, on-axis tilt tomography, conical tilt tomography and equally-sloped tomography（EST） were reported. The advanced reconstruction algorithms, such as discrete algebraic reconstruction technique（DART）, compressed sensing（CS） algorithm and EST were overviewed. At last, the development tendency of ET in materials science was presented.
基金Supported by the National Natural Science Fundation of China(51873146)。
文摘In this paper,the main research work and related reports of materials science research in China’s space technology field during 2020-2022 are summarized.This paper covers Materials Sciences in Space Environment,Materials Sciences for Space Environment,Materials Behavior in Space Environment and Space experimental hardware for material investigation.With the rapid development of China’s space industry,more scientists will be involved in materials science,space technology and earth science researches.In the future,a series of disciplines such as space science,machinery,artificial intelligence,digital twin and big data will be further integrated with materials science,and space materials will also usher in new development opportunities.
基金supported by the Informatization Plan of the Chinese Academy of Sciences (Grant No. CASWX2023SF-0101)the Key Research Program of Frontier Sciences, CAS (Grant No. ZDBS-LY-7025)+1 种基金the Youth Innovation Promotion Association CAS (Grant No. 2021167)the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB33020000)。
文摘The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence(GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge data.This specialized AI model, named Mat Chat, focuses on predicting inorganic material synthesis pathways. Mat Chat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although Mat Chat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. Mat Chat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science.
基金Supports by the Strategic Priority Research Program on Space Science,the Chinese Academy of Sciences(XDA15013200,XDA15013700,XDA15013800,XDA15051200)the China’s Manned Space Station Project(TGJZ800-2-RW024)and the National Natural Science Foundation of China(51327901)。
文摘Activities of space materials science research in China have been continuously supported by two main national programs.One is the China Space Station(CSS)program since 1992,and the other is the Strategic Priority Program(SPP)on Space Science since 2011.In CSS plan in 2019,eleven space materials science experimental projects were officially approved for execution during the construction of the space station.In the SPP Phase Ⅱ launched in 2018,seven pre-research projects are deployed as the first batch in 2018,and one concept study project in 2019.These pre-research projects will be cultivated as candidates for future selection as space experiment projects on the recovery of scientific experimental satellites in the future.A new apparatus of electrostatic levitation system for ground-based research of space materials science and rapid solidification research has been developed under the support of the National Natural Science Foundation of China.In order to promote domestic academic activities and to enhance the advancement of space materials science in China,the Space Materials Science and Technology Division belong to the Chinese Materials Research Society was established in 2019.We also organized scientists to write five review papers on space materials science as a special topic published in the journal Scientia Sinica to provide valuable scientific and technical references for Chinese researchers.
基金funded by the Informatization Plan of Chinese Academy of Sciences(Grant No.CASWX2021SF-0102)the National Key R&D Program of China(Grant Nos.2022YFA1603903,2022YFA1403800,and 2021YFA0718700)+1 种基金the National Natural Science Foundation of China(Grant Nos.11925408,11921004,and 12188101)the Chinese Academy of Sciences(Grant No.XDB33000000)。
文摘The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.
