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Song Z, Niu X, Chen H. Leveraging an all-fixed transfer framework to predict the interpretable formation energy of MXenes with hybrid terminals. Phys Chem Chem Phys 2024; 26:14847-14856. [PMID: 38727050 DOI: 10.1039/d4cp00386a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/23/2024]
Abstract
MXenes have attracted substantial attention for their various applications in energy storage, sensors, and catalysts. Experimental exploration of MXenes with hybrid terminal surfaces offers a unique means of property tailoring that is crucial for expanding the performance space of MXenes, wherein the formation energy of an MXene with mixed surface terminals plays a key role in determining its relative stability and practical applications. However, the challenge of identifying energetically stable MXenes with multifunctional surfaces persists, primarily due to the absence of precise surface modification during experiments and the vast structural space for DFT calculations. Herein, we use an all-fixed transfer (AFT) framework combined with first-principles calculations to predict the formation energies of MXenes terminated by binary elements from groups VIA and VIIA. The trained model exhibits a high average R2 of 0.99, maintaining transferability and accuracy in predicting larger supercells from smaller-sized MXenes and datasets despite the structural imbalance between the training and prediction sets. The underlying interpretation of the high accuracy is revealed through the capture of main attributes and comparison of node features. Additionally, it is important to mention that the factors influencing the average formation energy include the types of element pairs, the ratio of terminal groups, and the distribution of terminations on two surfaces, with the first one being dominant. Finally, we successfully streamline the diverse structural cardinality of a large hybrid terminated MXene space of over 700 million, thereby facilitating the rapid screening of the top 5 stable MXene classes with binary terminal elements (FO, FCl, FBr, FS, and FSe). Besides, in the scenarios of lithium storage, the TL-predicted MXene can enhance its relative stability by increasing the fluorine ratio where the capacity can be optimized by different surface group combinations. All results indicate that the AFT framework has the advantages of screening functional MXenes with a huge structure space from smaller and imbalanced data sets.
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Affiliation(s)
- Zihao Song
- School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Xiaobin Niu
- School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Haiyuan Chen
- School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 611731, China.
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2
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Wang G, Wang C, Zhang X, Li Z, Zhou J, Sun Z. Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations. iScience 2024; 27:109673. [PMID: 38646181 PMCID: PMC11033164 DOI: 10.1016/j.isci.2024.109673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024] Open
Abstract
Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design. In this review, the current state of the four essential stages of MLIP is discussed, including data generation methods, material structure descriptors, six unique machine learning algorithms, and available software. Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, material properties predicting, and the pre-trained universal models. Eventually, the future perspectives, consisting of standard datasets, transferability, generalization, and trade-off between accuracy and complexity in MLIPs, are reported.
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Affiliation(s)
- Guanjie Wang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
- School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, China
| | - Changrui Wang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Xuanguang Zhang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Zefeng Li
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Jian Zhou
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Zhimei Sun
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
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3
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Liu S, Chen Z, Liu Y, Wu L, Wang B, Wang Z, Wu B, Zhang X, Zhang J, Chen M, Huang H, Ye J, Chu PK, Yu XF, Polavarapu L, Hoye RLZ, Gao F, Zhao H. Data-Driven Controlled Synthesis of Oriented Quasi-Spherical CsPbBr 3 Perovskite Materials. Angew Chem Int Ed Engl 2024; 63:e202319480. [PMID: 38317379 DOI: 10.1002/anie.202319480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 02/02/2024] [Accepted: 02/05/2024] [Indexed: 02/07/2024]
Abstract
Controlled synthesis of lead-halide perovskite crystals is challenging yet attractive because of the pivotal role played by the crystal structure and growth conditions in regulating their properties. This study introduces data-driven strategies for the controlled synthesis of oriented quasi-spherical CsPbBr3, alongside an investigation into the synthesis mechanism. High-throughput rapid characterization of absorption spectra and color under ultraviolet illumination was conducted using 23 possible ligands for the synthesis of CsPbBr3 crystals. The links between the absorption spectra slope (difference in the absorbance at 400 nm and 450 nm divided by a wavelength interval of 50 nm) and crystal size were determined through statistical analysis of more than 100 related publications. Big data analysis and machine learning were employed to investigate a total of 688 absorption spectra and 652 color values, revealing correlations between synthesis parameters and properties. Ex situ characterization confirmed successful synthesis of oriented quasi-spherical CsPbBr3 perovskites using polyvinylpyrrolidone and Acacia. Density functional theory calculations highlighted strong adsorption of Acacia on the (110) facet of CsPbBr3. Optical properties of the oriented quasi-spherical perovskites prepared with these data-driven strategies were significantly improved. This study demonstrates that data-driven controlled synthesis facilitates morphology-controlled perovskites with excellent optical properties.
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Affiliation(s)
- Shaohui Liu
- Center for Intelligent and Biomimetic Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, PR China
- Nano Science and Technology Institute, University of Science and Technology of China, Suzhou, 215000, PR China
- Wenzhou Institute of Technology, Digital Intelligent Manufacturing Research Center, Wenzhou, 325000, PR China
| | - Zijian Chen
- Center for Intelligent and Biomimetic Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, PR China
- Wenzhou Institute of Technology, Digital Intelligent Manufacturing Research Center, Wenzhou, 325000, PR China
- Department of Chemical and Environmental Engineering, the University of Nottingham Ningbo China, Ningbo, 315100, PR China
| | - Yingming Liu
- Centre for Photonics Information and Energy Materials, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, PR China
| | - Lingjun Wu
- Center for Intelligent and Biomimetic Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, PR China
| | - Boyuan Wang
- Wenzhou Institute of Technology, Digital Intelligent Manufacturing Research Center, Wenzhou, 325000, PR China
| | - Zixuan Wang
- Center for Intelligent and Biomimetic Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, PR China
- Wenzhou Institute of Technology, Digital Intelligent Manufacturing Research Center, Wenzhou, 325000, PR China
| | - Bobin Wu
- Center for Intelligent and Biomimetic Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, PR China
- Nano Science and Technology Institute, University of Science and Technology of China, Suzhou, 215000, PR China
- Wenzhou Institute of Technology, Digital Intelligent Manufacturing Research Center, Wenzhou, 325000, PR China
| | - Xinyu Zhang
- Center for Intelligent and Biomimetic Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, PR China
- Wenzhou Institute of Technology, Digital Intelligent Manufacturing Research Center, Wenzhou, 325000, PR China
| | - Jie Zhang
- Center for Intelligent and Biomimetic Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, PR China
- Nano Science and Technology Institute, University of Science and Technology of China, Suzhou, 215000, PR China
| | - Mengyun Chen
- Department of Physics, Chemistry and Biology (IFM), Linköping University, Linköping, SE-58183, Sweden
| | - Hao Huang
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, PR China
| | - Junzhi Ye
- Inorganic Chemistry Laboratory, University of Oxford, South Parks Road, Oxford, OX1 3QR, United Kingdom
| | - Paul K Chu
- Department of Physics, Department of Materials Science and Engineering, and Department of Biomedical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
| | - Xue-Feng Yu
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, PR China
| | - Lakshminarayana Polavarapu
- CINBIO, Materials Chemistry and Physics Group, University of Vigo, Campus Universitario Marcosende, Vigo, 36310, Spain
| | - Robert L Z Hoye
- Inorganic Chemistry Laboratory, University of Oxford, South Parks Road, Oxford, OX1 3QR, United Kingdom
| | - Feng Gao
- Department of Physics, Chemistry and Biology (IFM), Linköping University, Linköping, SE-58183, Sweden
| | - Haitao Zhao
- Center for Intelligent and Biomimetic Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, PR China
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4
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Mao Y, Yao X, Yu Z, An Z, Ma H. Ground-State Orbital Descriptors for Accelerated Development of Organic Room-Temperature Phosphorescent Materials. Angew Chem Int Ed Engl 2024; 63:e202318836. [PMID: 38141053 DOI: 10.1002/anie.202318836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 12/24/2023]
Abstract
Organic materials with room-temperature phosphorescence (RTP) are in high demand for optoelectronics and bioelectronics. Developing RTP materials highly relies on expert experience and costly excited-state calculations. It is a challenge to find a tool for effectively screening RTP materials. Herein we first establish ground-state orbital descriptors (πFMOs ) derived from the π-electron component of the frontier molecular orbitals to characterize the RTP lifetime (τp ), achieving a balance in screening efficiency and accuracy. Using the πFMOs , a data-driven machine learning model gains a high accuracy in classifying long τp , filtering out 836 candidates with long-lived RTP from a virtual library of 19,295 molecules. With the aid of the excited-state calculations, 287 compounds are predicted with high RTP efficiency. Impressively, experiments further confirm the reliability of this workflow, opening a novel avenue for designing high-performance RTP materials for potential applications.
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Affiliation(s)
- Yufeng Mao
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
- The Institute of Flexible Electronics (IFE, Future Technologies), Xiamen University, Xiamen 361005 Fujian, China
| | - Xiaokang Yao
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
| | - Ze Yu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
| | - Zhongfu An
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
- The Institute of Flexible Electronics (IFE, Future Technologies), Xiamen University, Xiamen 361005 Fujian, China
| | - Huili Ma
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
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5
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Zhuang J, Midgley AC, Wei Y, Liu Q, Kong D, Huang X. Machine-Learning-Assisted Nanozyme Design: Lessons from Materials and Engineered Enzymes. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2210848. [PMID: 36701424 DOI: 10.1002/adma.202210848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/03/2023] [Indexed: 05/11/2023]
Abstract
Nanozymes are nanomaterials that exhibit enzyme-like biomimicry. In combination with intrinsic characteristics of nanomaterials, nanozymes have broad applicability in materials science, chemical engineering, bioengineering, biochemistry, and disease theranostics. Recently, the heterogeneity of published results has highlighted the complexity and diversity of nanozymes in terms of consistency of catalytic capacity. Machine learning (ML) shows promising potential for discovering new materials, yet it remains challenging for the design of new nanozymes based on ML approaches. Alternatively, ML is employed to promote optimization of intelligent design and application of catalytic materials and engineered enzymes. Incorporation of the successful ML algorithms used in the intelligent design of catalytic materials and engineered enzymes can concomitantly facilitate the guided development of next-generation nanozymes with desirable properties. Here, recent progress in ML, its utilization in the design of catalytic materials and enzymes, and how emergent ML applications serve as promising strategies to circumvent challenges associated with time-expensive and laborious testing in nanozyme research and development are summarized. The potential applications of successful examples of ML-aided catalytic materials and engineered enzymes in nanozyme design are also highlighted, with special focus on the unified aims in enhancing design and recapitulation of substrate selectivity and catalytic activity.
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Affiliation(s)
- Jie Zhuang
- School of Medicine, and State, Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, 300071, China
| | - Adam C Midgley
- Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontiers, Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
| | - Yonghua Wei
- Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontiers, Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
| | - Qiqi Liu
- Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontiers, Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
| | - Deling Kong
- Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontiers, Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
| | - Xinglu Huang
- Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontiers, Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
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6
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Wang Z, Chen A, Tao K, Han Y, Li J. MatGPT: A Vane of Materials Informatics from Past, Present, to Future. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2306733. [PMID: 37813548 DOI: 10.1002/adma.202306733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/05/2023] [Indexed: 10/17/2023]
Abstract
Combining materials science, artificial intelligence (AI), physical chemistry, and other disciplines, materials informatics is continuously accelerating the vigorous development of new materials. The emergence of "GPT (Generative Pre-trained Transformer) AI" shows that the scientific research field has entered the era of intelligent civilization with "data" as the basic factor and "algorithm + computing power" as the core productivity. The continuous innovation of AI will impact the cognitive laws and scientific methods, and reconstruct the knowledge and wisdom system. This leads to think more about materials informatics. Here, a comprehensive discussion of AI models and materials infrastructures is provided, and the advances in the discovery and design of new materials are reviewed. With the rise of new research paradigms triggered by "AI for Science", the vane of materials informatics: "MatGPT", is proposed and the technical path planning from the aspects of data, descriptors, generative models, pretraining models, directed design models, collaborative training, experimental robots, as well as the efforts and preparations needed to develop a new generation of materials informatics, is carried out. Finally, the challenges and constraints faced by materials informatics are discussed, in order to achieve a more digital, intelligent, and automated construction of materials informatics with the joint efforts of more interdisciplinary scientists.
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Affiliation(s)
- Zhilong Wang
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - An Chen
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Kehao Tao
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yanqiang Han
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jinjin Li
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
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7
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Jia X, Yu Z, Liu F, Liu H, Zhang D, Campos dos Santos E, Zheng H, Hashimoto Y, Chen Y, Wei L, Li H. Identifying Stable Electrocatalysts Initialized by Data Mining: Sb 2 WO 6 for Oxygen Reduction. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305630. [PMID: 38059832 PMCID: PMC10837344 DOI: 10.1002/advs.202305630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 11/09/2023] [Indexed: 12/08/2023]
Abstract
Data mining from computational materials database has become a popular strategy to identify unexplored catalysts. Herein, the opportunities and challenges of this strategy are analyzed by investigating a discrepancy between data mining and experiments in identifying low-cost metal oxide (MO) electrocatalysts. Based on a search engine capable of identifying stable MOs at the pH and potentials of interest, a series of MO electrocatalysts is identified as potential candidates for various reactions. Sb2 WO6 attracted the attention among the identified stable MOs in acid. Based on the aqueous stability diagram, Sb2 WO6 is stable under oxygen reduction reaction (ORR) in acidic media but rather unstable under high-pH ORR conditions. However, this contradicts to the subsequent experimental observation in alkaline ORR conditions. Based on the post-catalysis characterizations, surface state analysis, and an advanced pH-field coupled microkinetic modeling, it is found that the Sb2 WO6 surface will undergo electrochemical passivation under ORR potentials and form a stable and 4e-ORR active surface. The results presented here suggest that though data mining is promising for exploring electrocatalysts, a refined strategy needs to be further developed by considering the electrochemistry-induced surface stability and activity.
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Affiliation(s)
- Xue Jia
- Advanced Institute for Materials Research (WPI‐AIMR)Tohoku UniversitySendai980‐8577Japan
| | - Zixun Yu
- Advanced Institute for Materials Research (WPI‐AIMR)Tohoku UniversitySendai980‐8577Japan
- School of Chemical and Biomolecule EngineeringThe University of SydneyDarlingtonNSW2006Australia
| | - Fangzhou Liu
- School of Chemical and Biomolecule EngineeringThe University of SydneyDarlingtonNSW2006Australia
| | - Heng Liu
- Advanced Institute for Materials Research (WPI‐AIMR)Tohoku UniversitySendai980‐8577Japan
| | - Di Zhang
- Advanced Institute for Materials Research (WPI‐AIMR)Tohoku UniversitySendai980‐8577Japan
- State Key Laboratory of Mechanical System and VibrationShanghai Jiao Tong UniversityShanghai200240P. R. China
| | - Egon Campos dos Santos
- Advanced Institute for Materials Research (WPI‐AIMR)Tohoku UniversitySendai980‐8577Japan
| | - Hao Zheng
- Advanced Institute for Materials Research (WPI‐AIMR)Tohoku UniversitySendai980‐8577Japan
| | | | - Yuan Chen
- School of Chemical and Biomolecule EngineeringThe University of SydneyDarlingtonNSW2006Australia
| | - Li Wei
- School of Chemical and Biomolecule EngineeringThe University of SydneyDarlingtonNSW2006Australia
| | - Hao Li
- Advanced Institute for Materials Research (WPI‐AIMR)Tohoku UniversitySendai980‐8577Japan
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8
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Yamakoshi K, Ohno Y, Kutsukake K, Kojima T, Yokoi T, Yoshida H, Tanaka H, Liu X, Kudo H, Usami N. Multicrystalline Informatics Applied to Multicrystalline Silicon for Unraveling the Microscopic Root Cause of Dislocation Generation. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2308599. [PMID: 38041569 DOI: 10.1002/adma.202308599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/19/2023] [Indexed: 12/03/2023]
Abstract
A comprehensive analysis of optical and photoluminescence images obtained from practical multicrystalline silicon wafers is conducted, utilizing various machine learning models for dislocation cluster region extraction, grain segmentation, and crystal orientation prediction. As a result, a realistic 3D model that includes the generation point of dislocation clusters is built. Finite element stress analysis on the 3D model coupled with crystal growth simulation reveals inhomogeneous and complex stress distribution and that dislocation clusters are frequently formed along the slip plane with the highest shear stress among twelve equivalents, concentrated along bending grain boundaries (GBs). Multiscale analysis of the extracted GBs near the generation point of dislocation clusters combined with ab initio calculations has shown that the dislocation generation due to the concentration of shear stress is caused by the nanofacet formation associated with GB bending. This mechanism cannot be captured by the Haasen-Alexander-Sumino model. Thus, this research method reveals the existence of a dislocation generation mechanism unique to the multicrystalline structure. Multicrystalline informatics linking experimental, theoretical, computational, and data science on multicrystalline materials at multiple scales is expected to contribute to the advancement of materials science by unraveling complex phenomena in various multicrystalline materials.
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Affiliation(s)
- Kenta Yamakoshi
- Graduate School of Engineering, Nagoya University, Nagoya, 464-8603, Japan
| | - Yutaka Ohno
- Institute for Materials Research, Tohoku University, Sendai, 980-8577, Japan
| | - Kentaro Kutsukake
- Center for Advanced Intelligence Project, RIKEN, Tokyo, 103-0027, Japan
| | - Takuto Kojima
- Graduate School of Informatics, Nagoya University, Nagoya, 464-8601, Japan
| | - Tatsuya Yokoi
- Graduate School of Engineering, Nagoya University, Nagoya, 464-8603, Japan
| | | | - Hiroyuki Tanaka
- Graduate School of Engineering, Nagoya University, Nagoya, 464-8603, Japan
| | - Xin Liu
- Graduate School of Engineering, Nagoya University, Nagoya, 464-8603, Japan
| | - Hiroaki Kudo
- Graduate School of Informatics, Nagoya University, Nagoya, 464-8601, Japan
| | - Noritaka Usami
- Graduate School of Engineering, Nagoya University, Nagoya, 464-8603, Japan
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9
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Wang S, Wan Y, Song N, Liu Y, Xie T, Hoex B. Automatically Generated Datasets: Present and Potential Self-Cleaning Coating Materials. Sci Data 2024; 11:146. [PMID: 38296978 PMCID: PMC10831094 DOI: 10.1038/s41597-024-02983-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 01/17/2024] [Indexed: 02/02/2024] Open
Abstract
The rise of urbanization coupled with pollution has highlighted the importance of outdoor self-cleaning coatings. These revolutionary coatings contribute to the longevity of various surfaces and reduce maintenance costs for a wide range of applications. Despite ongoing research to develop efficient and durable self-cleaning coatings, adopting systematic research methodologies could accelerate these advancements. In this work, we use Natural Language Processing (NLP) strategies to generate open- and traceable-sourced datasets about self-cleaning coating materials from 39,011 multi-disciplinary papers. The data are from function-based and property-based corpora for self-cleaning purposes. These datasets are presented in four different formats for diverse uses or combined uses: material frequency statistics, material dictionary, measurement value datasets for self-cleaning-related properties and optical properties, and sentiment statistics of material stability and durability. This provides a literature-based data resource for the development of self-cleaning coatings and also offers potential pathways for material discovery and prediction by machine learning.
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Affiliation(s)
- Shaozhou Wang
- School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW, Australia
- GreenDynamics Pty. Ltd, Kensington, NSW, Australia
| | - Yuwei Wan
- GreenDynamics Pty. Ltd, Kensington, NSW, Australia
- Department of Linguistics and Translation, City University of Hong Kong, Kowloon Tong, Hong Kong, China
| | - Ning Song
- School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW, Australia.
| | - Yixuan Liu
- GreenDynamics Pty. Ltd, Kensington, NSW, Australia
| | - Tong Xie
- School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW, Australia.
- GreenDynamics Pty. Ltd, Kensington, NSW, Australia.
| | - Bram Hoex
- School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW, Australia.
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10
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Hammad R, Mondal S. Predicting Poisson's Ratio: A Study of Semisupervised Anomaly Detection and Supervised Approaches. ACS OMEGA 2024; 9:1956-1961. [PMID: 38222642 PMCID: PMC10785625 DOI: 10.1021/acsomega.3c08861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 11/22/2023] [Accepted: 12/13/2023] [Indexed: 01/16/2024]
Abstract
Auxetics are a rare class of materials that exhibit a negative Poisson's ratio. The existence of these auxetic materials is rare but has a large number of applications in the design of exotic materials. We build a complete machine learning framework to detect Auxetic materials as well as Poisson's ratio of non-auxetic materials. A semisupervised anomaly detection model is presented, which is capable of separating out the auxetics materials (treated as an anomaly) from an unknown database with an average precision of 0.64. Another regression model (supervised) is also created to predict the Poisson's ratio of non-auxetic materials with an R2 of 0.82. Additionally, this regression model helps us to find the optimal features for the anomaly detection model. This methodology can be generalized and used to discover materials with rare physical properties.
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Affiliation(s)
- Raheel Hammad
- Tata Institute of Fundamental
Research Hyderabad, Hyderabad 500046, Telangana, India
| | - Sownyak Mondal
- Tata Institute of Fundamental
Research Hyderabad, Hyderabad 500046, Telangana, India
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11
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Miao R, Bissoli M, Basagni A, Marotta E, Corni S, Amendola V. Data-Driven Predetermination of Cu Oxidation State in Copper Nanoparticles: Application to the Synthesis by Laser Ablation in Liquid. J Am Chem Soc 2023; 145:25737-25752. [PMID: 37907392 PMCID: PMC10690790 DOI: 10.1021/jacs.3c09158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/12/2023] [Accepted: 10/18/2023] [Indexed: 11/02/2023]
Abstract
Copper-based nanocrystals are reference nanomaterials for integration into emerging green technologies, with laser ablation in liquid (LAL) being a remarkable technique for their synthesis. However, the achievement of a specific type of nanocrystal, among the whole library of nanomaterials available using LAL, has been until now an empirical endeavor based on changing synthesis parameters and characterizing the products. Here, we started from the bibliographic analysis of LAL synthesis of Cu-based nanocrystals to identify the relevant physical and chemical features for the predetermination of copper oxidation state. First, single features and their combinations were screened by linear regression analysis, also using a genetic algorithm, to find the best correlation with experimental output and identify the equation giving the best prediction of the LAL results. Then, machine learning (ML) models were exploited to unravel cross-correlations between features that are hidden in the linear regression analysis. Although the LAL-generated Cu nanocrystals may be present in a range of oxidation states, from metallic copper to cuprous oxide (Cu2O) and cupric oxide (CuO), in addition to the formation of other materials such as Cu2S and CuCN, ML was able to guide the experiments toward the maximization of the compounds in the greatest demand for integration in sustainable processes. This approach is of general applicability to other nanomaterials and can help understand the origin of the chemical pathways of nanocrystals generated by LAL, providing a rational guideline for the conscious predetermination of laser-synthesis parameters toward the desired compounds.
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Affiliation(s)
- Runpeng Miao
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
| | - Michael Bissoli
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
| | - Andrea Basagni
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
| | - Ester Marotta
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
| | - Stefano Corni
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
| | - Vincenzo Amendola
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
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12
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Nishikawa C, Nishikubo R, Ishiwari F, Saeki A. Exploration of Solution-Processed Bi/Sb Solar Cells by Automated Robotic Experiments Equipped with Microwave Conductivity. JACS AU 2023; 3:3194-3203. [PMID: 38034953 PMCID: PMC10685419 DOI: 10.1021/jacsau.3c00519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 12/02/2023]
Abstract
Solution-processed inorganic solar cells with less toxic and earth-abundant elements are emerging as viable alternatives to high-performance lead-halide perovskite solar cells. However, the wide range of elements and process parameters impede the rapid exploration of vast chemical spaces. Here, we developed an automated robot-embedded measurement system that performs photoabsorption spectroscopy, optical microscopy, and white-light flash time-resolved microwave conductivity (TRMC). We tested 576 films of quaternary element-blended wide-bandgap Cs-Bi-Sb-I semiconductors with various compositions, organic salt additives (MACl, FACl, MAI, and FAI, where MA and FA represent methylammonium and formamidinium, respectively), and thermal annealing temperatures. Among them, we found that the maximum power conversion efficiency (PCE) was 2.36%, which is significantly higher than the PCE of 0.68% for a reference film without an additive. Machine learning (ML) and statistical analyses revealed significant features and their relationships with TRMC transients, thereby demonstrating the advantages of combining ML and automated experiments for the high-throughput exploration of photovoltaic materials.
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Affiliation(s)
- Chisato Nishikawa
- Department
of Applied Chemistry, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Ryosuke Nishikubo
- Department
of Applied Chemistry, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
- Innovative
Catalysis Science Division, Institute for Open and Transdisciplinary
Research Initiatives (ICS-OTRI), Osaka University, 1-1 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Fumitaka Ishiwari
- Department
of Applied Chemistry, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
- Innovative
Catalysis Science Division, Institute for Open and Transdisciplinary
Research Initiatives (ICS-OTRI), Osaka University, 1-1 Yamadaoka, Suita, Osaka 565-0871, Japan
- PRESTO,
Japan Science and Technology Agency (JST), Kawaguchi, Saitama 332-0012, Japan
| | - Akinori Saeki
- Department
of Applied Chemistry, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
- Innovative
Catalysis Science Division, Institute for Open and Transdisciplinary
Research Initiatives (ICS-OTRI), Osaka University, 1-1 Yamadaoka, Suita, Osaka 565-0871, Japan
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13
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Borislavov L, Nedyalkova M, Tadjer A, Aydemir O, Romanova J. Machine Learning-Based Screening for Potential Singlet Fission Chromophores: The Challenge of Imbalanced Data Sets. J Phys Chem Lett 2023; 14:10103-10112. [PMID: 37921710 PMCID: PMC10659028 DOI: 10.1021/acs.jpclett.3c02365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/13/2023] [Accepted: 10/18/2023] [Indexed: 11/04/2023]
Abstract
Excitation with one photon of a singlet fission (SF) material generates two triplet excitons, thus doubling the solar cell efficiency. Therefore, the SF molecules are regarded as new generation organic photovoltaics, but it is hard to identify them. Recently, it was demonstrated that molecules of low-to-intermediate diradical character (DRC) are potential SF chromophores. This prompts a low-cost strategy for finding new SF candidates by computational high-throughput workflows. We propose a machine learning aided screening for SF entrants based on their DRC. Our data set comprises 469 784 compounds extracted from the PubChem database, structurally rich but inherently imbalanced regarding DRC values. We developed well performing classification models that can retrieve potential SF chromophores. The latter (∼4%) were analyzed by K-means clustering to reveal qualitative structure-property relationships and to extract strategies for molecular design. The developed screening procedure and data set can be easily adapted for applications of diradicaloids in photonics and spintronics.
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Affiliation(s)
- Lyuben Borislavov
- Institute
of General and Inorganic Chemistry, Bulgarian
Academy of Sciences, 11 Akad. Georgi Bonchev str., 1113 Sofia, Bulgaria
| | - Miroslava Nedyalkova
- Chemistry
Department, University of Fribourg, Chemin du Musée 9, 1700 Fribourg, Switzerland
- Faculty
of Chemistry and Pharmacy, Sofia University, 1 James Bourchier Blvd., 1164 Sofia, Bulgaria
| | - Alia Tadjer
- Faculty
of Chemistry and Pharmacy, Sofia University, 1 James Bourchier Blvd., 1164 Sofia, Bulgaria
| | - Onder Aydemir
- Faculty
of Engineering, Department of Electrical & Electronics Engineering, Karadeniz Technical University, 61080 Trabzon, Turkey
| | - Julia Romanova
- Faculty
of Chemistry and Pharmacy, Sofia University, 1 James Bourchier Blvd., 1164 Sofia, Bulgaria
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14
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Lu Y, Kong D, Yang G, Wang R, Pang G, Luo H, Yang H, Xu K. Machine Learning-Enabled Tactile Sensor Design for Dynamic Touch Decoding. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2303949. [PMID: 37740421 PMCID: PMC10646241 DOI: 10.1002/advs.202303949] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/15/2023] [Indexed: 09/24/2023]
Abstract
Skin-like flexible sensors play vital roles in healthcare and human-machine interactions. However, general goals focus on pursuing intrinsic static and dynamic performance of skin-like sensors themselves accompanied with diverse trial-and-error attempts. Such a forward strategy almost isolates the design of sensors from resulting applications. Here, a machine learning (ML)-guided design of flexible tactile sensor system is reported, enabling a high classification accuracy (≈99.58%) of tactile perception in six dynamic touch modalities. Different from the intuition-driven sensor design, such ML-guided performance optimization is realized by introducing a support vector machine-based ML algorithm along with specific statistical criteria for fabrication parameters selection to excavate features deeply concealed in raw sensing data. This inverse design merges the statistical learning criteria into the design phase of sensing hardware, bridging the gap between the device structures and algorithms. Using the optimized tactile sensor, the high-quality recognizable signals in handwriting applications are obtained. Besides, with the additional data processing, a robot hand assembled with the sensor is able to complete real-time touch-decoding of an 11-digit braille phone number with high accuracy.
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Affiliation(s)
- Yuyao Lu
- State Key Laboratory of Fluid Power and Mechatronic SystemsSchool of Mechanical EngineeringZhejiang UniversityHangzhou310027China
| | - Depeng Kong
- State Key Laboratory of Fluid Power and Mechatronic SystemsSchool of Mechanical EngineeringZhejiang UniversityHangzhou310027China
| | - Geng Yang
- State Key Laboratory of Fluid Power and Mechatronic SystemsSchool of Mechanical EngineeringZhejiang UniversityHangzhou310027China
- Zhejiang Key Laboratory of Intelligent Operation and Maintenance RobotHangzhou310000China
| | - Ruohan Wang
- State Key Laboratory of Fluid Power and Mechatronic SystemsSchool of Mechanical EngineeringZhejiang UniversityHangzhou310027China
| | - Gaoyang Pang
- School of Electrical and Information EngineeringThe University of SydneySydneyNSW2006Australia
| | - Huayu Luo
- State Key Laboratory of Fluid Power and Mechatronic SystemsSchool of Mechanical EngineeringZhejiang UniversityHangzhou310027China
| | - Huayong Yang
- State Key Laboratory of Fluid Power and Mechatronic SystemsSchool of Mechanical EngineeringZhejiang UniversityHangzhou310027China
| | - Kaichen Xu
- State Key Laboratory of Fluid Power and Mechatronic SystemsSchool of Mechanical EngineeringZhejiang UniversityHangzhou310027China
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15
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Murai T, Matsuda S. Integrated Multimodal Omics and Dietary Approaches for the Management of Neurodegeneration. EPIGENOMES 2023; 7:20. [PMID: 37754272 PMCID: PMC10529483 DOI: 10.3390/epigenomes7030020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/26/2023] [Accepted: 08/31/2023] [Indexed: 09/28/2023] Open
Abstract
Neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease, are caused by a combination of multiple events that damage neuronal function. A well-characterized biomarker of neurodegeneration is the accumulation of proteinaceous aggregates in the brain. However, the gradually worsening symptoms of neurodegenerative diseases are unlikely to be solely due to the result of a mutation in a single gene, but rather a multi-step process involving epigenetic changes. Recently, it has been suggested that a fraction of epigenetic alternations may be correlated to neurodegeneration in the brain. Unlike DNA mutations, epigenetic alterations are reversible, and therefore raise the possibilities for therapeutic intervention, including dietary modifications. Additionally, reactive oxygen species may contribute to the pathogenesis of Alzheimer's disease and Parkinson's disease through epigenetic alternation. Given that the antioxidant properties of plant-derived phytochemicals are likely to exhibit pleiotropic effects against ROS-mediated epigenetic alternation, dietary intervention may be promising for the management of neurodegeneration in these diseases. In this review, the state-of-the-art applications using single-cell multimodal omics approaches, including epigenetics, and dietary approaches for the identification of novel biomarkers and therapeutic approaches for the treatment of neurodegenerative diseases are discussed.
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Affiliation(s)
- Toshiyuki Murai
- Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita 565-0871, Japan;
| | - Satoru Matsuda
- Department of Food Science and Nutrition, Nara Women’s University, Kita-Uoya Nishimachi, Nara 630-8506, Japan
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16
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Tian H, Wang J, Lai G, Dou Y, Gao J, Duan Z, Feng X, Wu Q, He X, Yao L, Zeng L, Liu Y, Yang X, Zhao J, Zhuang S, Shi J, Qu G, Yu XF, Chu PK, Jiang G. Renaissance of elemental phosphorus materials: properties, synthesis, and applications in sustainable energy and environment. Chem Soc Rev 2023; 52:5388-5484. [PMID: 37455613 DOI: 10.1039/d2cs01018f] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
The polymorphism of phosphorus-based materials has garnered much research interest, and the variable chemical bonding structures give rise to a variety of micro and nanostructures. Among the different types of materials containing phosphorus, elemental phosphorus materials (EPMs) constitute the foundation for the synthesis of related compounds. EPMs are experiencing a renaissance in the post-graphene era, thanks to recent advancements in the scaling-down of black phosphorus, amorphous red phosphorus, violet phosphorus, and fibrous phosphorus and consequently, diverse classes of low-dimensional sheets, ribbons, and dots of EPMs with intriguing properties have been produced. The nanostructured EPMs featuring tunable bandgaps, moderate carrier mobility, and excellent optical absorption have shown great potential in energy conversion, energy storage, and environmental remediation. It is thus important to have a good understanding of the differences and interrelationships among diverse EPMs, their intrinsic physical and chemical properties, the synthesis of specific structures, and the selection of suitable nanostructures of EPMs for particular applications. In this comprehensive review, we aim to provide an in-depth analysis and discussion of the fundamental physicochemical properties, synthesis, and applications of EPMs in the areas of energy conversion, energy storage, and environmental remediation. Our evaluations are based on recent literature on well-established phosphorus allotropes and theoretical predictions of new EPMs. The objective of this review is to enhance our comprehension of the characteristics of EPMs, keep abreast of recent advances, and provide guidance for future research of EPMs in the fields of chemistry and materials science.
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Affiliation(s)
- Haijiang Tian
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China.
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, P. R. China
| | - Jiahong Wang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, P. R. China.
- Hubei Three Gorges Laboratory, Yichang, Hubei 443007, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Gengchang Lai
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, P. R. China.
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Yanpeng Dou
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, P. R. China.
- Hubei Three Gorges Laboratory, Yichang, Hubei 443007, P. R. China
| | - Jie Gao
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China.
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, P. R. China
| | - Zunbin Duan
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, P. R. China.
- Hubei Three Gorges Laboratory, Yichang, Hubei 443007, P. R. China
| | - Xiaoxiao Feng
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, P. R. China.
| | - Qi Wu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China.
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, P. R. China
| | - Xingchen He
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, P. R. China.
- Hubei Three Gorges Laboratory, Yichang, Hubei 443007, P. R. China
| | - Linlin Yao
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China.
| | - Li Zeng
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China.
| | - Yanna Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China.
| | - Xiaoxi Yang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China.
| | - Jing Zhao
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China.
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, P. R. China
| | - Shulin Zhuang
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, P. R. China
| | - Jianbo Shi
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China.
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Guangbo Qu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China.
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Xue-Feng Yu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, P. R. China.
- Hubei Three Gorges Laboratory, Yichang, Hubei 443007, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Paul K Chu
- Department of Physics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
- Department of Materials Science and Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
- Department of Biomedical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China.
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, P. R. China
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
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17
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Ali MK, Javaid S, Afzal H, Zafar I, Fayyaz K, Ain Q, Rather MA, Hossain MJ, Rashid S, Khan KA, Sharma R. Exploring the multifunctional roles of quantum dots for unlocking the future of biology and medicine. ENVIRONMENTAL RESEARCH 2023; 232:116290. [PMID: 37295589 DOI: 10.1016/j.envres.2023.116290] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/28/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023]
Abstract
With recent advancements in nanomedicines and their associated research with biological fields, their translation into clinically-applicable products is still below promises. Quantum dots (QDs) have received immense research attention and investment in the four decades since their discovery. We explored the extensive biomedical applications of QDs, viz. Bio-imaging, drug research, drug delivery, immune assays, biosensors, gene therapy, diagnostics, their toxic effects, and bio-compatibility. We unravelled the possibility of using emerging data-driven methodologies (bigdata, artificial intelligence, machine learning, high-throughput experimentation, computational automation) as excellent sources for time, space, and complexity optimization. We also discussed ongoing clinical trials, related challenges, and the technical aspects that should be considered to improve the clinical fate of QDs and promising future research directions.
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Affiliation(s)
- Muhammad Kashif Ali
- Deparment of Physiology, Rashid Latif Medical College, Lahore, Punjab, 54700, Pakistan.
| | - Saher Javaid
- KAM School of Life Sciences, Forman Christian College (a Chartered University) Lahore, Punjab, Pakistan.
| | - Haseeb Afzal
- Department of ENT, Ameer Ud Din Medical College, Lahore, Punjab, 54700, Pakistan.
| | - Imran Zafar
- Department of Bioinformatics and Computational Biology, Virtual University, Punjab, 54700, Pakistan.
| | - Kompal Fayyaz
- Department of National Centre for Bioinformatics, Quaid-I-Azam University, Islamabad, 45320, Pakistan.
| | - Quratul Ain
- Department of Chemistry, Government College Women University Faisalabad (GCWUF), Punjab, 54700, Pakistan.
| | - Mohd Ashraf Rather
- Division of Fish Genetics and Biotechnology, Faculty of Fisheries, Rangil- Gandarbal (SKAUST-K), India.
| | - Md Jamal Hossain
- Department of Pharmacy, State University of Bangladesh, 77 Satmasjid Road, Dhanmondi, Dhaka, 1205, Bangladesh.
| | - Summya Rashid
- Department of Pharmacology & Toxicology, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj, 11942, Saudi Arabia.
| | - Khalid Ali Khan
- Unit of Bee Research and Honey Production, Research Center for Advanced Materials Science (RCAMS), King Khalid University, P.O. Box 9004, Abha, 61413, Saudi Arabia; Applied College, King Khalid University, P. O. Box 9004, Abha, 61413, Saudi Arabia.
| | - Rohit Sharma
- Department of Rasa Shastra and Bhaishajya Kalpana, Faculty of Ayurveda, Institute of Medical Sciences, Banaras Hindu University, Varanasi, 221005, Uttar Pradesh, India.
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18
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Kim JM, Lee KH, Lee JY. Extracting Polaron Recombination from Electroluminescence in Organic Light-Emitting Diodes by Artificial Intelligence. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2209953. [PMID: 36788120 DOI: 10.1002/adma.202209953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/03/2023] [Indexed: 06/18/2023]
Abstract
Direct exploring the electroluminescence (EL) of organic light-emitting diodes (OLEDs) is a challenge due to the complicated processes of polarons, excitons, and their interactions. This study demonstrated the extraction of the polaron dynamics from transient EL by predicting the recombination coefficient via artificial intelligence, overcoming multivariable kinetics problems. The performance of a machine learning (ML) model trained by various EL decay curves is significantly improved using a novel featurization method and input node optimization, achieving an R2 value of 0.947. The optimized ML model successfully predicts the recombination coefficients of actual OLEDs based on an exciplex-forming cohost, enabling the quantitative understanding of the overall polaron behavior under various electrical excitation conditions.
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Affiliation(s)
- Jae-Min Kim
- School of Chemical Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
| | - Kyung Hyung Lee
- School of Chemical Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
| | - Jun Yeob Lee
- School of Chemical Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
- SKKU Advanced Institute of Nano Technology, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of korea
- SKKU Institute of Energy Science and Technology, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of korea
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19
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Chen A, Ye S, Wang Z, Han Y, Cai J, Li J. Machine-learning-assisted rational design of 2D doped tellurene for fin field-effect transistor devices. PATTERNS 2023; 4:100722. [PMID: 37123447 PMCID: PMC10140614 DOI: 10.1016/j.patter.2023.100722] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/18/2022] [Accepted: 03/08/2023] [Indexed: 04/09/2023]
Abstract
Fin field-effect transistors (FinFETs) have been widely used in electronic devices on account of their excellent performance, but this new type of device is facing many challenges because of size constraints. Two-dimensional (2D) materials with a layer structure can meet the required thickness of FinFETs and provide ideal carrier transport performance. In this work, we used 2D tellurene as the parent material and modified it with doping techniques to improve electronic device performance. High-performance FinFET devices were prepared with 23 systems screened from 385 doping systems by a combination of first-principle calculations and a machine-learning (ML) model. Moreover, theoretical calculations demonstrated that 1S1@Te and 2S2@Te have high carrier mobility and stability with an electron mobility and a hole mobility of 6.211 × 104 cm2 V-1 S-1 and 1.349 × 104 cm2 V-1 S-1, respectively. This work can provide a reference for subsequent experiments and advance the development of functional materials by using an ML-assisted design paradigm.
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Affiliation(s)
- An Chen
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 200240, China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Simin Ye
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 200240, China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zhilong Wang
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 200240, China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yanqiang Han
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Junfei Cai
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 200240, China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jinjin Li
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 200240, China
- Corresponding author
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20
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Wang Y, Adam ML, Zhao Y, Zheng W, Gao L, Yin Z, Zhao H. Machine Learning-Enhanced Flexible Mechanical Sensing. NANO-MICRO LETTERS 2023; 15:55. [PMID: 36800133 PMCID: PMC9936950 DOI: 10.1007/s40820-023-01013-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/08/2023] [Indexed: 05/31/2023]
Abstract
To realize a hyperconnected smart society with high productivity, advances in flexible sensing technology are highly needed. Nowadays, flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device's software. Significant research efforts have been devoted to improving materials, sensing mechanism, and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology. Meanwhile, advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors. Machine learning (ML) as an important branch of artificial intelligence can efficiently handle such complex data, which can be multi-dimensional and multi-faceted, thus providing a powerful tool for easy interpretation of sensing data. In this review, the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented. Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated, which includes health monitoring, human-machine interfaces, object/surface recognition, pressure prediction, and human posture/motion identification. Finally, the advantages, challenges, and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed. These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.
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Affiliation(s)
- Yuejiao Wang
- Applied Mechanics Laboratory, Department of Engineering Mechanics, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Mukhtar Lawan Adam
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China
| | - Yunlong Zhao
- Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen, 361102, People's Republic of China
| | - Weihao Zheng
- School of Mechano-Electronic Engineering, Xidian University, Xi'an , 710071, People's Republic of China
| | - Libo Gao
- Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen, 361102, People's Republic of China.
| | - Zongyou Yin
- Research School of Chemistry, Australian National University, Canberra, ACT, 2601, Australia.
| | - Haitao Zhao
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China.
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21
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Zhang L, Zhuang Z, Fang Q, Wang X. Study on the Automatic Identification of ABX3 Perovskite Crystal Structure Based on the Bond-Valence Vector Sum. MATERIALS (BASEL, SWITZERLAND) 2022; 16:ma16010334. [PMID: 36614673 PMCID: PMC9821887 DOI: 10.3390/ma16010334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 12/26/2022] [Accepted: 12/28/2022] [Indexed: 05/14/2023]
Abstract
Perovskite materials have a variety of crystal structures, and the properties of crystalline materials are greatly influenced by geometric information such as the space group, crystal system, and lattice constant. It used to be mostly obtained using calculations based on density functional theory (DFT) and experimental data from X-ray diffraction (XRD) curve fitting. These two techniques cannot be utilized to identify materials on a wide scale in businesses since they require expensive equipment and take a lot of time. Machine learning (ML), which is based on big data statistics and nonlinear modeling, has advanced significantly in recent years and is now capable of swiftly and reliably predicting the structures of materials with known chemical ratios based on a few key material-specific factors. A dataset encompassing 1647 perovskite compounds in seven crystal systems was obtained from the Materials Project database for this study, which used the ABX3 perovskite system as its research object. A descriptor called the bond-valence vector sum (BVVS) is presented to describe the intricate geometry of perovskites in addition to information on the usual chemical composition of the elements. Additionally, a model for the automatic identification of perovskite structures was built through a comparison of various ML techniques. It is possible to identify the space group and crystal system using just a small dataset of 10 feature descriptors. The highest accuracy is 0.955 and 0.974, and the highest correlation coefficient (R2) value of the lattice constant can reach 0.887, making this a quick and efficient method for determining the crystal structure.
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Affiliation(s)
- Laisheng Zhang
- Institute of Material Science and Information Technology, Anhui University, Hefei 230601, China
- Institute of Solid State Physics, Hefei Institute of Materials Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Zhong Zhuang
- Institute of Solid State Physics, Hefei Institute of Materials Science, Chinese Academy of Sciences, Hefei 230031, China
- Correspondence: ; Tel.: +86-138-5513-7903
| | - Qianfeng Fang
- Institute of Solid State Physics, Hefei Institute of Materials Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Xianping Wang
- Institute of Solid State Physics, Hefei Institute of Materials Science, Chinese Academy of Sciences, Hefei 230031, China
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Wang L, Saji SE, Wu L, Wang Z, Chen Z, Du Y, Yu XF, Zhao H, Yin Z. Emerging Synthesis Strategies of 2D MOFs for Electrical Devices and Integrated Circuits. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2201642. [PMID: 35843870 DOI: 10.1002/smll.202201642] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Indexed: 06/15/2023]
Abstract
The development of advanced electronic devices is boosting many aspects of modern technology and industry. The ever-increasing demand for advanced electrical devices and integrated circuits calls for the design of novel materials, with superior properties for the improvement of working performance. In this review, a detailed overview of the synthesis strategies of 2D metal organic frameworks (MOFs) acquiring growing attention is presented, as a basis for expansion of novel key materials in electrical devices and integrated circuits. A framework of controllable synthesis routes to be implanted in the synthesis strategies of 2D materials and MOFs is described. In short, the synthesis methods of 2D MOFs are summarized and discussed in depth followed by the illustrations of promising applications relating to various electrical devices and integrated circuits. It is concluded by outlining how 2D MOFs can be synthesized in a simpler, highly efficient, low-cost, and more environmentally friendly way which can open up their applicable opportunities as key materials in advanced electrical devices and integrated circuits, enabling their use in broad aspects of the society.
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Affiliation(s)
- Linjuan Wang
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
| | - Sandra Elizabeth Saji
- Research School of Chemistry, Australian National University, Acton, ACT, 2601, Australia
| | - Lingjun Wu
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
| | - Zixuan Wang
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
| | - Zijian Chen
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
| | - Yaping Du
- Tianjin Key Lab for Rare Earth Materials and Applications, Center for Rare Earth and Inorganic Functional Materials, School of Materials Science and Engineering & National Institute for Advanced Materials, Nankai University, Tianjin, 300350, P. R. China
| | - Xue-Feng Yu
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
| | - Haitao Zhao
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
| | - Zongyou Yin
- Research School of Chemistry, Australian National University, Acton, ACT, 2601, Australia
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