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Cao Y, Xu B, Li B, Fu H. Advanced Design of Soft Robots with Artificial Intelligence. NANO-MICRO LETTERS 2024; 16:214. [PMID: 38869734 DOI: 10.1007/s40820-024-01423-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 04/22/2024] [Indexed: 06/14/2024]
Affiliation(s)
- Ying Cao
- Nanotechnology Center, School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong, 999077, People's Republic of China
| | - Bingang Xu
- Nanotechnology Center, School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong, 999077, People's Republic of China.
| | - Bin Li
- Bioinspired Engineering and Biomechanics Center, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
| | - Hong Fu
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong, 999077, People's Republic of China.
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2
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Zhou W, Zeng J, Dong Z, Xiao C, Gong L, Fan B, Li Y, Chen Y, Zhao J, Zhang C. A Degradable Tribotronic Transistor for Self-Destructing Intelligent Package e-Labels. ACS APPLIED MATERIALS & INTERFACES 2024; 16:30255-30263. [PMID: 38813772 DOI: 10.1021/acsami.4c04322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
Recently, discarded electronic products have caused serious environmental pollution and information security issues, which have attracted widespread attention. Here, a degradable tribotronic transistor (DTT) for self-destructing intelligent package e-labels has been developed, integrated by a triboelectric nanogenerator and a protonic field-effect transistor with sodium alginate as a dielectric layer. The triboelectric potential generated by external contact electrification is used as the gate voltage of the organic field-effect transistor, which regulates carrier transport through proton migration/accumulation. The DTT has successfully demonstrated its output characteristics with a high sensitivity of 0.336 mm-1 and a resolution of over 100 μm. Moreover, the DTT can be dissolved in water within 3 min and completely degraded in soil within 12 days, demonstrating its excellent degradation characteristics, which may contribute to environmental protection. Finally, an intelligent package e-label based on the modulation of the DTT is demonstrated, which can display information about the package by a human touch. The e-label will automatically fail due to the degradation of the DTT over time, achieving the purpose of information confidentiality. This work has not only presented a degradable tribotronic transistor for package e-labels but also exhibited bright prospects in military security, information hiding, logistics privacy, and personal affairs.
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Affiliation(s)
- Weilin Zhou
- School of Mechanical Engineering, Guangxi University, Nanning 530004, China
- Beijing Key Laboratory of Micro-Nano Energy and Sensor, Center for High-Entropy Energy and Systems, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
| | - Jianhua Zeng
- Beijing Key Laboratory of Micro-Nano Energy and Sensor, Center for High-Entropy Energy and Systems, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- Center on Nanoenergy Research, Institute of Science and Technology for Carbon Peak & Neutrality, Key Laboratory of Blue Energy and Systems Integration (Guangxi University), Education Department of Guangxi Zhuang Autonomous Region, School of Physical Science & Technology, Guangxi University, Nanning 530004, China
| | - Zefang Dong
- Beijing Key Laboratory of Micro-Nano Energy and Sensor, Center for High-Entropy Energy and Systems, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chongyong Xiao
- School of Mechanical Engineering, Guangxi University, Nanning 530004, China
| | - Likun Gong
- Beijing Key Laboratory of Micro-Nano Energy and Sensor, Center for High-Entropy Energy and Systems, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Beibei Fan
- Beijing Key Laboratory of Micro-Nano Energy and Sensor, Center for High-Entropy Energy and Systems, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- Center on Nanoenergy Research, Institute of Science and Technology for Carbon Peak & Neutrality, Key Laboratory of Blue Energy and Systems Integration (Guangxi University), Education Department of Guangxi Zhuang Autonomous Region, School of Physical Science & Technology, Guangxi University, Nanning 530004, China
| | - Yongbo Li
- Beijing Key Laboratory of Micro-Nano Energy and Sensor, Center for High-Entropy Energy and Systems, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuanfen Chen
- School of Mechanical Engineering, Guangxi University, Nanning 530004, China
- Center on Nanoenergy Research, Institute of Science and Technology for Carbon Peak & Neutrality, Key Laboratory of Blue Energy and Systems Integration (Guangxi University), Education Department of Guangxi Zhuang Autonomous Region, School of Physical Science & Technology, Guangxi University, Nanning 530004, China
| | - Junqing Zhao
- Beijing Key Laboratory of Micro-Nano Energy and Sensor, Center for High-Entropy Energy and Systems, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chi Zhang
- School of Mechanical Engineering, Guangxi University, Nanning 530004, China
- Beijing Key Laboratory of Micro-Nano Energy and Sensor, Center for High-Entropy Energy and Systems, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
- Center on Nanoenergy Research, Institute of Science and Technology for Carbon Peak & Neutrality, Key Laboratory of Blue Energy and Systems Integration (Guangxi University), Education Department of Guangxi Zhuang Autonomous Region, School of Physical Science & Technology, Guangxi University, Nanning 530004, China
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Wang W, Wang Y, Yin F, Niu H, Shin YK, Li Y, Kim ES, Kim NY. Tailoring Classical Conditioning Behavior in TiO 2 Nanowires: ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware. NANO-MICRO LETTERS 2024; 16:133. [PMID: 38411720 PMCID: PMC10899558 DOI: 10.1007/s40820-024-01338-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/28/2023] [Indexed: 02/28/2024]
Abstract
Neuromorphic hardware equipped with associative learning capabilities presents fascinating applications in the next generation of artificial intelligence. However, research into synaptic devices exhibiting complex associative learning behaviors is still nascent. Here, an optoelectronic memristor based on Ag/TiO2 Nanowires: ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors. Effective implementation of synaptic behaviors, including long and short-term plasticity, and learning-forgetting-relearning behaviors, were achieved in the device through the application of light and electrical stimuli. Leveraging the optoelectronic co-modulated characteristics, a simulation of neuromorphic computing was conducted, resulting in a handwriting digit recognition accuracy of 88.9%. Furthermore, a 3 × 7 memristor array was constructed, confirming its application in artificial visual memory. Most importantly, complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli, respectively. After training through associative pairs, reflexes could be triggered solely using light stimuli. Comprehensively, under specific optoelectronic signal applications, the four features of classical conditioning, namely acquisition, extinction, recovery, and generalization, were elegantly emulated. This work provides an optoelectronic memristor with associative behavior capabilities, offering a pathway for advancing brain-machine interfaces, autonomous robots, and machine self-learning in the future.
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Affiliation(s)
- Wenxiao Wang
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, People's Republic of China
- RFIC Centre, NDAC Centre, Kwangwoon University, Nowon-gu, Seoul, 139-701, South Korea
- Department of Electronics Engineering, Kwangwoon University, Nowon-Gu, Seoul, 139-701, South Korea
| | - Yaqi Wang
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, People's Republic of China
| | - Feifei Yin
- RFIC Centre, NDAC Centre, Kwangwoon University, Nowon-gu, Seoul, 139-701, South Korea
- Department of Electronics Engineering, Kwangwoon University, Nowon-Gu, Seoul, 139-701, South Korea
| | - Hongsen Niu
- RFIC Centre, NDAC Centre, Kwangwoon University, Nowon-gu, Seoul, 139-701, South Korea
- Department of Electronics Engineering, Kwangwoon University, Nowon-Gu, Seoul, 139-701, South Korea
| | - Young-Kee Shin
- Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul, 08826, South Korea
| | - Yang Li
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, People's Republic of China.
- School of Microelectronics, Shandong University, Jinan, 250101, People's Republic of China.
| | - Eun-Seong Kim
- RFIC Centre, NDAC Centre, Kwangwoon University, Nowon-gu, Seoul, 139-701, South Korea.
- Department of Electronics Engineering, Kwangwoon University, Nowon-Gu, Seoul, 139-701, South Korea.
| | - Nam-Young Kim
- RFIC Centre, NDAC Centre, Kwangwoon University, Nowon-gu, Seoul, 139-701, South Korea.
- Department of Electronics Engineering, Kwangwoon University, Nowon-Gu, Seoul, 139-701, South Korea.
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Zhou H, Li S, Ang KW, Zhang YW. Recent Advances in In-Memory Computing: Exploring Memristor and Memtransistor Arrays with 2D Materials. NANO-MICRO LETTERS 2024; 16:121. [PMID: 38372805 PMCID: PMC10876512 DOI: 10.1007/s40820-024-01335-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/25/2023] [Indexed: 02/20/2024]
Abstract
The conventional computing architecture faces substantial challenges, including high latency and energy consumption between memory and processing units. In response, in-memory computing has emerged as a promising alternative architecture, enabling computing operations within memory arrays to overcome these limitations. Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays, rapid response times, and ability to emulate biological synapses. Among these devices, two-dimensional (2D) material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing, thanks to their exceptional performance driven by the unique properties of 2D materials, such as layered structures, mechanical flexibility, and the capability to form heterojunctions. This review delves into the state-of-the-art research on 2D material-based memristive arrays, encompassing critical aspects such as material selection, device performance metrics, array structures, and potential applications. Furthermore, it provides a comprehensive overview of the current challenges and limitations associated with these arrays, along with potential solutions. The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing, leveraging the potential of 2D material-based memristive devices.
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Affiliation(s)
- Hangbo Zhou
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Republic of Singapore
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Republic of Singapore.
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore, 138634, Republic of Singapore.
| | - Yong-Wei Zhang
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore.
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Ma H, Fang H, Xie X, Liu Y, Tian H, Chai Y. Optoelectronic Synapses Based on MXene/Violet Phosphorus van der Waals Heterojunctions for Visual-Olfactory Crossmodal Perception. NANO-MICRO LETTERS 2024; 16:104. [PMID: 38300424 PMCID: PMC10834395 DOI: 10.1007/s40820-024-01330-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/11/2023] [Indexed: 02/02/2024]
Abstract
The crossmodal interaction of different senses, which is an important basis for learning and memory in the human brain, is highly desired to be mimicked at the device level for developing neuromorphic crossmodal perception, but related researches are scarce. Here, we demonstrate an optoelectronic synapse for vision-olfactory crossmodal perception based on MXene/violet phosphorus (VP) van der Waals heterojunctions. Benefiting from the efficient separation and transport of photogenerated carriers facilitated by conductive MXene, the photoelectric responsivity of VP is dramatically enhanced by 7 orders of magnitude, reaching up to 7.7 A W-1. Excited by ultraviolet light, multiple synaptic functions, including excitatory postsynaptic currents, paired-pulse facilitation, short/long-term plasticity and "learning-experience" behavior, were demonstrated with a low power consumption. Furthermore, the proposed optoelectronic synapse exhibits distinct synaptic behaviors in different gas environments, enabling it to simulate the interaction of visual and olfactory information for crossmodal perception. This work demonstrates the great potential of VP in optoelectronics and provides a promising platform for applications such as virtual reality and neurorobotics.
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Affiliation(s)
- Hailong Ma
- Center for Advancing Materials Performance From the Nanoscale (CAMP-Nano), State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
| | - Huajing Fang
- Center for Advancing Materials Performance From the Nanoscale (CAMP-Nano), State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China.
| | - Xinxing Xie
- Center for Advancing Materials Performance From the Nanoscale (CAMP-Nano), State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
| | - Yanming Liu
- Institute of Microelectronics and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, People's Republic of China
| | - He Tian
- Institute of Microelectronics and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, People's Republic of China.
| | - Yang Chai
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, People's Republic of China.
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Sun T, Feng B, Huo J, Xiao Y, Wang W, Peng J, Li Z, Du C, Wang W, Zou G, Liu L. Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses. NANO-MICRO LETTERS 2023; 16:14. [PMID: 37955844 PMCID: PMC10643743 DOI: 10.1007/s40820-023-01235-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/24/2023] [Indexed: 11/14/2023]
Abstract
The recent wave of the artificial intelligence (AI) revolution has aroused unprecedented interest in the intelligentialize of human society. As an essential component that bridges the physical world and digital signals, flexible sensors are evolving from a single sensing element to a smarter system, which is capable of highly efficient acquisition, analysis, and even perception of vast, multifaceted data. While challenging from a manual perspective, the development of intelligent flexible sensing has been remarkably facilitated owing to the rapid advances of brain-inspired AI innovations from both the algorithm (machine learning) and the framework (artificial synapses) level. This review presents the recent progress of the emerging AI-driven, intelligent flexible sensing systems. The basic concept of machine learning and artificial synapses are introduced. The new enabling features induced by the fusion of AI and flexible sensing are comprehensively reviewed, which significantly advances the applications such as flexible sensory systems, soft/humanoid robotics, and human activity monitoring. As two of the most profound innovations in the twenty-first century, the deep incorporation of flexible sensing and AI technology holds tremendous potential for creating a smarter world for human beings.
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Affiliation(s)
- Tianming Sun
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China
- College of Materials Science and Engineering, Shanxi Province, Taiyuan University of Technology, Taiyuan, 030024, People's Republic of China
| | - Bin Feng
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Jinpeng Huo
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Yu Xiao
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Wengan Wang
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Jin Peng
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Zehua Li
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Chengjie Du
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Wenxian Wang
- College of Materials Science and Engineering, Shanxi Province, Taiyuan University of Technology, Taiyuan, 030024, People's Republic of China.
| | - Guisheng Zou
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China.
| | - Lei Liu
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China.
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Wang L, Zhang T, Shen J, Huang J, Li W, Shi W, Huang W, Yi M. Flexibly Photo-Regulated Brain-Inspired Functions in Flexible Neuromorphic Transistors. ACS APPLIED MATERIALS & INTERFACES 2023; 15:13380-13392. [PMID: 36853974 DOI: 10.1021/acsami.2c22754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
As an attractive prototype for neuromorphic computing, the difficultly attained three-terminal platforms have specific advantages in implementing the brain-inspired functions. Also, in these devices, the most utilized mechanisms are confined to the electrical gate-controlled ionic migrations, which are sensitive to the device defects and stoichiometric ratio. The resultant memristive responses have fluctuant characteristics, which have adverse influences on the neural emulations. Herein, we designed a specific transistor platform with light-regulated ambipolar memory characteristics. Also, based on its gentle processes of charge trapping, we obtain the impressive memristive performances featured by smooth responses and long-term endurable characteristics. The optoelectronic samples were also fabricated on flexible substrates successfully. Interestingly, based on the optoelectronic signals of the flexible devices, we endow the desirable optical processes with the brain-inspired emulations. We can flexibly emulate the light-inspired learning-memory functions in a synapse and further devise the advanced synapse array. More importantly, through this versatile platform, we investigate the mutual regulation of excitation and inhibition and implement their sensitive-mode transformations and the homeostasis property, which is conducive to ensuring the stability of overall neural activity. Furthermore, our flexible optoelectronic platform achieves high classification accuracy when implemented in artificial neural network simulations. This work demonstrates the advantages of the optoelectronic platform in implementing the significant brain-inspired functions and provides an insight into the future integration of visible sensing in flexible optoelectronic transistor platforms.
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Affiliation(s)
- Laiyuan Wang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
- Shaanxi Institute of Flexible Electronics (SIFE), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an 710072, China
| | - Tao Zhang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Junhao Shen
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Jin Huang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Wen Li
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Wei Shi
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), 30 South Puzhu Road, Nanjing 211816, People's Republic of China
| | - Wei Huang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
- Shaanxi Institute of Flexible Electronics (SIFE), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an 710072, China
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), 30 South Puzhu Road, Nanjing 211816, People's Republic of China
| | - Mingdong Yi
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
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