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Electronic structure and optical spectra of MSi2N4 (M = Mo, Ta, V) materials with single-atom decoration: a first-principles study

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Journal of materials Chemistry C 2023年第43期11卷 15097-15105页

作者： Wu, Tzu-Cheng Liao, Yin-Song Kao, Jui-Cheng Chou, Jyh-Pin Department of Physics National Tsing Hua University Hsinchu300044 Taiwan The Affiliated Senior High School of National Taiwan Normal University Taipei10658 Taiwan Tsing Hua Interdisciplinary Program National Tsing Hua University Hsinchu300044 Taiwan Department of Materials Science and Engineering National Tsing Hua University Hsinchu300044 Taiwan Department of Materials Science and Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Physics National Changhua University of Education Changhua50007 Taiwan

MoSi2N4 has outstanding potential for applications in electronics, spintronics, and semiconducting fabrication. This attractive scenario can be attributed to its remarkable electronic properties. In this study, we investigate the electronic structure and optical spectra of the MSi2N4 (M = Mo, Ta, and V) materials with single atom (H, N, and O) decoration using first-principles calculations. Pristine MoSi2N4 and VSi2N4 are semiconductors, and TaSi2N4 shows the half-metal properties. Decorating MoSi2N4 with single hydrogen and nitrogen atoms induces a magnetic moment. Single oxygen decorated TaSi2N4 does not change its intrinsic electronic properties, which is suitable for spintronic applications. On top of that, the semiconductor VSi2N4 can be turned into a half-metal via single hydrogen atom decoration. The absorption spectra demonstrate an enhancement in optical absorption for VSi2N4 after the decoration with a single nitrogen atom. Single atom decoration is one of the effective methods of tailoring the electronic properties of the two-dimensional MoSi2N4 family. Our findings can accelerate the development of the advanced electronic devices. © 2023 The Royal Society of Chemistry.

关键词： Silicon compounds

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Grating Coupled Attachment of Optical Fiber Arrays for in situ Photonics Experimentation

Grating Coupled Attachment of Optical Fiber Arrays for in si...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Arnold, Kellen P. Slaby, Joel B. Dattilo, Hannah M. Kaylor, C. Alex Schrimpf, Ronald D. Fleetwood, Daniel M. Ralph, Stephen E. Reed, Robert Weiss, Sharon M. Interdisciplinary Materials Science Program Vanderbilt University 2201 West End Ave NashvilleTN37235 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Electrical and Computer Engineering Vanderbilt University 2201 West End Ave NashvilleTN37235 United States

We report a simple, vacuum-compatible fiber attach process for in situ study of grating-coupled photonic devices. The robustness of this technique is demonstrated on grating-coupled waveguides exposed to multiple X-ra... 详细信息

ISBN: (纸本)9781957171395

关键词： X ray lasers

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Grating Coupled Attachment of Optical Fiber Arrays for in situ Photonics Experimentation

Grating Coupled Attachment of Optical Fiber Arrays for in si...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Photonic devices

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Development of exosome membrane materialsfused microbubbles for enhanced stability and efficient drug delivery of ultrasound contrast agent

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Acta Pharmaceutica Sinica B 2023年第12期13卷 4983-4998页

作者： Yongho Jang Jeehun Park Pilsu Kim Eun-Joo Park Hyungjin Sun Yujin Baek Jaehun Jung Tai-kyong Song Junsang Doh Hyuncheol Kim Department of Chemical and Biomolecular Engineering Sogang UniversitySeoul 04107Republic of Korea Research Institute of Advanced Materials(RIAM) Institute of Engineering ResearchSeoul National UniversitySeoul 08826Republic of Korea Department of Electronic Engineering Sogang UniversitySeoul 04107Republic of Korea Biomedical Research Institute&Department of Radiology Seoul National University HospitalSeoul 03080Republic of Korea Interdisciplinary Program in Bioengineering Seoul National UniversitySeoul 08826Republic of Korea Department of Materials Science and Engineering Seoul National UniversitySeoul 08826Republic of Korea Institute of Engineering Research Bio-MAX InstituteSeoul National UniversitySeoul 08826Republic of Korea Department of Biomedical Engineering Sogang UniversitySeoul 04107Republic of Korea

Lipid-coated microbubbles are widely used as an ultrasound contrast agent,as well as drug delivery ***,the two main limitations in ultrasound diagnosis and drug delivery using microbubbles are the short half-life in the blood system,and the difficulty of surface modification of microbubbles for active *** exosome,a type of extracellular vesicle,has a preferentially targeting ability for its original *** this study,exosome-fused microbubbles(Exo-MBs)were developed by embedding the exosome membrane proteins into *** a result,the stability of Exo-MBs is improved over the conventional *** the same principle that under the exposure of ultrasound,microbubbles are cavitated and self-assembled into nano-sized particles,and Exo-MBs are self-assembled into exosome membrane proteins-embedded nanoparticles(Exo-NPs).The Exo-NPs showed favorable targeting properties to their original cells.A photosensitizer,chlorin e6,was loaded into Exo-MBs to evaluate therapeutic efficacy as a drug *** higher therapeutic efficacy of photodynamic therapy was confirmed,followed by cancer immunotherapy from immunogenic cell *** have therefore developed a novel ultrasound image-guided drug delivery platform that overcomes the shortcomings of the conventional ultrasound contrast agent and is capable of simultaneous photodynamic therapy and cancer immunotherapy.

关键词： Exosomes Microbubbles Photodynamic therapy Ultrasound Sonoporation Cancer immunotherapy Drug delivery

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Advancing electrochemical impedance analysis through innovations in the distribution of relaxation times method

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Joule 2024年第7期8卷 1958-1981页

作者： Maradesa, Adeleke Py, Baptiste Huang, Jake Lu, Yang Iurilli, Pietro Mrozinski, Aleksander Law, Ho Mei Wang, Yuhao Wang, Zilong Li, Jingwei Xu, Shengjun Meyer, Quentin Liu, Jiapeng Brivio, Claudio Gavrilyuk, Alexander Kobayashi, Kiyoshi Bertei, Antonio Williams, Nicholas J. Zhao, Chuan Danzer, Michael Zic, Mark Wu, Phillip Yrjänä, Ville Pereverzyev, Sergei Chen, Yuhui Weber, André Kalinin, Sergei V. Schmidt, Jan Philipp Tsur, Yoed Boukamp, Bernard A. Zhang, Qiang Gaberšček, Miran O'Hayre, Ryan Ciucci, Francesco Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong Metallurgical and Materials Engineering Colorado School of Mines Golden80401 United States Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology Department of Chemical Engineering Tsinghua University Beijing China Green Energy Storage Trento38123 Italy Department of Manufacturing and Production Engineering Faculty of Mechanical Engineering and Ship Technology Institute of Machine and Materials Technology Gdańsk University of Technology Gdańsk Poland Electrode Design for Electrochemical Energy Systems University of Bayreuth Bayreuth Germany School of Chemistry University of New South Wales Sydney Australia School of Advanced Energy Sun Yat-Sen University Shenzhen China Sustainable Energy Center CSEM Neuchâtel2002 Switzerland Interdisciplinary Faculty of Science and Engineering Shimane University Matsue Japan Centre for Electronic and Optical Materials National Institute for Materials Science Tsukuba Japan Department of Civil and Industrial Engineering University of Pisa Pisa Italy Department of Materials Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Department of Chemical Engineering Massachusetts Institute of Technology Cambridge02139 United States Electrical Energy Systems University of Bayreuth Bayreuth Germany Ruder Boskovic Institute Zagreb Croatia Department of Materials and Minerals Resources Engineering National Taipei University Taipei Taiwan Finland Johann Radon Institute for Computational and Applied Mathematics Linz Austria School of Science and Engineering Nanjing Tech. University Nanjing China Karlsruhe Germany Department of Materials Science and Engineering University of Tennessee KnoxvilleTN37996 United States Physical Science Division Pacific Northwest National Laboratory RichlandWA99354 United States Systems Engineering for Electrical Energy Storage University of B

Electrochemical impedance spectroscopy (EIS) is widely used in electrochemistry, energy sciences, biology, and beyond. Analyzing EIS data is crucial, but it often poses challenges because of the numerous possible equivalent circuit models, the need for accurate analytical models, the difficulties of nonlinear regression, and the necessity of managing large datasets within a unified framework. To overcome these challenges, non-parametric models, such as the distribution of relaxation times (DRT, also known as the distribution function of relaxation times, DFRT), have emerged as promising tools for EIS analysis. For example, the DRT can be used to generate equivalent circuit models, initialize regression parameters, provide a time-domain representation of EIS spectra, and identify electrochemical processes. However, mastering the DRT method poses challenges as it requires mathematical and programming proficiency, which may extend beyond experimentalists’ usual expertise. Post-inversion analysis of DRT data can be difficult, especially in accurately identifying electrochemical processes, leading to results that may not always meet expectations. This article examines non-parametric EIS analysis methods, outlining their strengths and limitations from theoretical, computational, and end-user perspectives, and provides guidelines for their future development. Moreover, insights from survey data emphasize the need to develop a large impedance database, akin to an impedance genome. In turn, software development should target one-click, fully automated DRT analysis for multidimensional EIS spectra interpretation, software validation, and reliability. Particularly, creating a collaborative ecosystem hinged on free software could promote innovation and catalyze the adoption of the DRT method throughout all fields that use impedance data. © 2024 Elsevier Inc.

关键词： Electrochemical impedance spectroscopy

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Machine learning of microstructure–property relationships in materials with robust features from foundational vision transformers

arXiv

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arXiv 2025年

作者： Whitman, Sheila E. Latypov, Marat I. Graduate Interdisciplinary Program in Applied Mathematics University of Arizona TucsonAZ85721 United States Department of Materials Science and Engineering University of Arizona TucsonAZ85721 United States

Machine learning of microstructure–property relationships from data is an emerging approach in computational materials science. Most existing machine learning efforts focus on the development of task-specific models for each microstructure–property relationship. We propose utilizing pre-trained foundational vision transformers for the extraction of task-agnostic microstructure features and subsequent light-weight machine learning of a microstructure-dependent property. We demonstrate our approach with pre-trained state-of-the-art vision transformers (CLIP, DINOV2, SAM) in two case studies on machine-learning: (i) elastic modulus of two-phase microstructures based on simulations data;and (ii) Vicker’s hardness of Ni-base and Co-base superalloys based on experimental data published in literature. Our results show the potential of foundational vision transformers for robust microstructure representation and efficient machine learning of microstructure–property relationships without the need for expensive task-specific training or fine-tuning of bespoke deep learning models. © 2025, CC BY.

关键词： Elastic moduli

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Stretchable and self-healable spoof plasmonic meta-waveguide for wearable wireless communication system

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Light(science & Applications) 2022年第11期11卷 2725-2737页

作者： Bu-Yun Yu De-Wei Yue Ke-Xin Hou Lu JU Hao Chen Cong Ding Zhen-Guo Liu Yun-Qian Dai Hari Krishna Bisoyi Ying-Shi Guan Wei-Bing Lu Cheng-Hui Li Quan Li State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China Center for Flexible RF Technology Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 21111China State Key Laboratory of Coordination Chemistry School of Chemistry and Chemical EngineeringNanjing UniversityNanjing 210023China Institute of Advanced Materials and School of Chemistry and Chemical Engineering Southeast UniversityNanjing 211189China Advanced Materials and Liquid Crystal Institute and Chemical Physics Interdisciplinary Program Kent State UniversityKentOH 44242USA

Microwave transmission lines in wearable systems are easily damaged after frequent mechanical deformation,posing a severe threat to wireless ***,we report a new strategy to achieve stretchable microwave transmission lines with superior reliability and durability by integrating a self-healable elastomer with serpentine-geometry plasmonic meta-waveguide to support the spoof surface plasmon polariton(SSPP).After mechanical damage,the self-healable elastomer can autonomously repair itself to maintain the electromagnetic performance and mechanical ***,the specially designed SSPP structure exhibits excellent stability and damage *** if the self-healing process has not been completed or the eventual repair effect is not ideal,the spoof plasmonic meta-waveguide Can still maintain reliable ***-healing material enhances strength and durability,while the SSPP improves stability and gives more tolerance to the self-healing *** design coordinates the structural design with material synthesis to maximize the advantages of the SSPP and self-healing material,signifcantly improving the relability and durability of stretchable microwave transmission *** also perform communication quality experiments to demonstrate the potential of the proposed meta-waveguide as interconnects in future body area network systems.

关键词： process durability maintain

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Meta-optic accelerators for object classifier

Meta-optic accelerators for object classifier

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Zheng, Hanyu Liu, Quan Zhou, You Kravchenko, Ivan I. Huo, Yuankai Valentine, Jason Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37212 United States Department of Computer Science Vanderbilt University NashvilleTN37212 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37212 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in a high-speed and robust classifier with 93.1% accurac...

ISBN: (纸本)9781957171258

关键词：

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Meta-optic accelerators for object classification

Meta-optic accelerators for object classification

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2023 Flat Optics: Components to Systems, FLatOptics 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in high-speed classifiers that are demonstrated for hand... 详细信息

ISBN: (纸本)9781957171289

关键词： Imaging systems Machine vision Neural networks Optical components Optical neural systems Optical systems

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AI for organic and polymer synthesis

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science China Chemistry 2024年第8期67卷 2461-2496页

作者： Xin Hong Qi Yang Kuangbiao Liao Jianfeng Pei Mao Chen Fanyang Mo Hua Lu Wen-Bin Zhang Haisen Zhou Jiaxiao Chen Lebin Su Shuo-Qing Zhang Siyuan Liu Xu Huang Yi-Zhou Sun Yuxiang Wang Zexi Zhang Zhunzhun Yu Sanzhong Luo Xue-Feng Fu Shu-Li You Center of Chemistry for Frontier Technologies Department of ChemistryZhejiang UniversityHangzhou 310027China Center of Basic Molecular Science Department of ChemistryTsinghua UniversityBeijing 100084China Guangzhou National Laboratory Guangzhou 510005China Center for Quantitative Biology Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing 100871China State Key Laboratory of Molecular Engineering of Polymers Department of Macromolecular ScienceFudan UniversityShanghai 200433China School of Materials Science and Engineering Peking UniversityBeijing 100871China Beijing National Laboratory for Molecular Sciences Center for Soft Matter Science and EngineeringKey Laboratory of Polymer Chemistry and Physics of Ministry of EducationCollege of Chemistry and Molecular EngineeringPeking UniversityBeijing 100871China AI for Science(AI4S)-Preferred Program Shenzhen Graduate SchoolPeking UniversityShenzhen 518055China State Key Laboratory of Chemical Biology Shanghai Institute of Organic ChemistryUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200032China Department of Chemical Sciences National Natural Science Foundation of ChinaBeijing 100085China State Key Laboratory of Organometallic Chemistry Shanghai Institute of Organic ChemistryChinese Academy of SciencesShanghai 200032China

Recent years have witnessed the transformative impact from the integration of artificial intelligence with organic and polymer synthesis. This synergy offers innovative and intelligent solutions to a range of classic problems in synthetic chemistry. These exciting advancements include the prediction of molecular property, multi-step retrosynthetic pathway planning, elucidation of the structure-performance relationship of single-step transformation, establishment of the quantitative linkage between polymer structures and their functions, design and optimization of polymerization process, prediction of the structure and sequence of biological macromolecules, as well as automated and intelligent synthesis platforms. Chemists can now explore synthetic chemistry with unprecedented precision and efficiency, creating novel reactions, catalysts, and polymer materials under the datadriven paradigm. Despite these thrilling developments, the field of artificial intelligence(AI) synthetic chemistry is still in its infancy, facing challenges and limitations in terms of data openness, model interpretability, as well as software and hardware support. This review aims to provide an overview of the current progress, key challenges, and future development suggestions in the interdisciplinary field between AI and synthetic chemistry. It is hoped that this overview will offer readers a comprehensive understanding of this emerging field, inspiring and promoting further scientific research and development.

关键词： organic synthesis polymer synthesis machine learning prediction chemical database automated synthesis

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