Article
Nanoscience & Nanotechnology
Jina Bak, Seunggyu Kim, Kyumin Park, Jeechan Yoon, Mino Yang, Un Jeong Kim, Hideo Hosono, Jihyang Park, Bolim You, Ojun Kwon, Byungjin Cho, Sang-Won Park, Myung Gwan Hahm, Moonsang Lee
Summary: Physical doping in 2D transition-metal dichalcogenide nanomaterials can enhance the development of artificial synaptic devices by improving synaptic plasticity, reducing power consumption, increasing multilevel states, and enhancing symmetry and asymmetry ratios. This strategy is expected to be promising for the development of bioinspired artificial electronic devices.
ACS APPLIED MATERIALS & INTERFACES
(2023)
Article
Chemistry, Multidisciplinary
Zilong Dong, Qilin Hua, Jianguo Xi, Yuanhong Shi, Tianci Huang, Xinhuan Dai, Jianan Niu, Bingjun Wang, Zhong Lin Wang, Weiguo Hu
Summary: Memristors that mimic synaptic plasticity are crucial for energy-efficient neuromorphic computing architecture, and layered 2D Bi2O2Se is an important material in improving memristive device efficiency. High-quality Bi2O2Se nanosheets are grown on mica substrates, and bipolar Bi2O2Se memristors with outstanding performance are fabricated. These memristors exhibit ultrafast switching speed (<5 ns), low power consumption (<3.02 pJ), and demonstrate synaptic plasticity. Utilizing conductance modification in simulated artificial neural networks (ANN), MNIST recognition achieves high accuracy of 91%. The 2D Bi2O2Se enables the memristors to possess ultrafast and low-power attributes, showing great potential in neuromorphic computing applications.
Article
Engineering, Electrical & Electronic
Hyunseok Shim, Faheem Ershad, Shubham Patel, Yongcao Zhang, Binghao Wang, Zhihua Chen, Tobin J. Marks, Antonio Facchetti, Cunjiang Yu
Summary: The research team reported an elastic and reconfigurable synaptic transistor that exhibits inhibitory and excitatory characteristics even under mechanical strain. The device uses a stretchable bilayer semiconductor and an encapsulating elastomer, showing memory characteristics and low energy consumption. When applied to an artificial neural network, it achieves recognition accuracy of over 90% even when stretched by 50%.
NATURE ELECTRONICS
(2022)
Article
Chemistry, Multidisciplinary
Molla Manjurul Islam, Adithi Krishnaprasad, Durjoy Dev, Ricardo Martinez-Martinez, Victor Okonkwo, Benjamin Wu, Sang Sub Han, Tae-Sung Bae, Hee-Suk Chung, Jimmy Touma, Yeonwoong Jung, Tania Roy
Summary: This study presents an optoelectronic synapse device that can integrate various functions for real-time object identification. The device is capable of sensing, storing, and processing optical data for different wavelengths of light. By extracting weight update parameters, an artificial neural network can be trained to identify patterns of different wavelengths.
Article
Chemistry, Physical
Jia-Lin Meng, Tian-Yu Wang, Lin Chen, Qing-Qing Sun, Hao Zhu, Li Ji, Shi-Jin Ding, Wen-Zhong Bao, Peng Zhou, David Wei Zhang
Summary: A low-dimensional flexible hybrid photoelectric-modulated artificial heterosynapse was constructed, demonstrating extremely low energy consumption and ultrafast response while successfully emulating neuromorphic functions. The device can effectively modulate the short-term potentiation correlations and multiple memory states of the heterosynapse.
Review
Energy & Fuels
Syed Shujaat Karim, Abubakar Sudais, Muhammad Salman Shah, Sarah Farrukh, Subhan Ali, Mubashir Ahmed, Zarrar Salahuddin, Xianfeng Fan
Summary: The rapid development of science and technology has led to challenges, which has prompted researchers to focus on the development of two-dimensional nanomaterials. These materials offer exceptional features that can be used to resolve industrial problems such as energy storage and conversion, electronic and optoelectronic device improvement, and pollution monitoring. This review discusses the synthesis methods, applications, and characteristics of MoS2 nanosheets, as well as the current challenges and future research directions.
Review
Chemistry, Multidisciplinary
Geonyeop Lee, Ji-Hwan Baek, Fan Ren, Stephen J. Pearton, Gwan-Hyoung Lee, Jihyun Kim
Summary: Neuromorphic systems, which mimic neural functionalities of a human brain using artificial synapses and neurons, have advantages of high energy efficiency and fast computing speed. 2D materials, with unique surface properties and excellent crystallinity, have emerged as promising candidates for neuromorphic computing hardware due to uncontrollable defects in bulk material-based devices.
Article
Chemistry, Multidisciplinary
Revannath Dnyandeo Nikam, Jongwon Lee, Wooseok Choi, Dongmin Kim, Hyunsang Hwang
Summary: This study proposes the use of monolayer graphene as a low-power heating source in O-ECRAM to enhance oxygen-ion transport for improved learning accuracy and conductance tuning, achieving better performance in artificial synapses.
Article
Chemistry, Multidisciplinary
Revannath Dnyandeo Nikam, Jongwon Lee, Wooseok Choi, Dongmin Kim, Hyunsang Hwang
Summary: By using a monolayer graphene as a heating source, the migration of ions in O-ECRAM can be increased, enabling learning and storage in neural networks. This method features long-term retention, high stability, and the ability to store analog states. These findings demonstrate the application of 2D materials as heating elements in artificial synapse chips to accelerate neuromorphic computation.
Article
Nanoscience & Nanotechnology
Xuelian Zhang, Haohan Chen, Siqi Cheng, Feng Guo, Wenjing Jie, Jianhua Hao
Summary: This study investigates the resistive switching characteristics, synaptic functions, and neuromorphic computing of memristors based on two-dimensional MXene Ti3C2 nanosheets. The results show that both digital and analog resistive switching behaviors can coexist in these memristors depending on the magnitude of operation voltage. Additionally, the artificial synapses based on these memristors exhibit basic synaptic functions and successfully emulate the learning-forgetting experience. Moreover, the artificial synapses can be used to construct an artificial neural network for image recognition.
ACS APPLIED MATERIALS & INTERFACES
(2022)
Article
Chemistry, Multidisciplinary
Rongjie Zhang, Yongjue Lai, Wenjun Chen, Changjiu Teng, Yujie Sun, Liusi Yang, Jingyun Wang, Bilu Liu, Hui-Ming Cheng
Summary: The use of wrinkles in monolayer 2D semiconductors as controllable carrier trapping centers enables multilevel storage capability, high on/off ratios, and long retention times in memory devices. The study also reveals a wrinkle-based carrier trapping mechanism, offering a new approach for controlling carriers in ultra-thin memory devices and for in-memory calculations.
Article
Nanoscience & Nanotechnology
Song Hao, Xinglong Ji, Faqiang Liu, Shuai Zhong, Khin Yin Pang, Kian Guan Lim, Tow Chong Chong, Rong Zhao
Summary: The study introduces a vertical heterostructure composed of MoS2 and WO3 films, with WO3 acting as an anion reservoir to address the nonlinearity and limited conductance states in 2D materials-based synaptic devices. Experimental results show nearly linear conductance change and up to 130 weight states, with simulations demonstrating significantly improved learning accuracy of 93.2% in artificial neural networks.
ACS APPLIED NANO MATERIALS
(2021)
Article
Multidisciplinary Sciences
Xiong Xiong, Xin Wang, Qianlan Hu, Xuefei Li, Yanqing Wu
Summary: The study demonstrates a flexible van der Waals synaptic device based on black phosphorus with two working modes, showing nonvolatile and quasi-nonvolatile memory effects, excellent performance in energy-efficient computation, and good endurance under bending cycles.
Article
Engineering, Environmental
Jeyavelan Muthu, Farheen Khurshid, Hao-Ting Chin, Yu-Chi Yao, Ya-Ping Hsieh, Mario Hofmann
Summary: 2D transition metal dichalcogenides (2D-TMDs) show significant variations in electrochemical performance due to differences in morphology. In this study, the limitations in homogeneous charge transfer of 2D-TMDs were identified as the main cause for variations in the hydrogen evolution reactions (HER). Statistical electrochemical characterization was conducted on MoS2 and WS2 devices with compositional uniformity and controllable morphology. The results highlight the impact of morphology-dependent carrier transport on the electrochemical properties of 2D materials.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Nanoscience & Nanotechnology
Ruochen Liu, Jae Gwang Kim, Prashant Dhakal, Wei Li, Jun Ma, Aolin Hou, Cory Merkel, Jingjing Qiu, Mark Zoran, Shiren Wang
Summary: In this paper, low-cost flexible carbon nanotube/polydimethylsiloxane (CNT/PDMS) nanocomposites were prepared by solution processing. The neuromorphic properties of these materials were investigated, and their excellent performance in terms of low power consumption, high working bending radius, and robustness under mechanical deformation were demonstrated.
ADVANCED COMPOSITES AND HYBRID MATERIALS
(2023)
Article
Thermodynamics
Yanyu Qiao, Zhichao Chen, Zheng Yu, Guan Shuo, Jiawei Li, Zhenhua Yuan, Lingyan Zeng, Zhengqi Li
Summary: The combustion characteristics of pyrolytic semi-coke (SC) were studied by analyzing its physical and chemical properties. It was found that SC has a larger specific surface area, pore volume, and fractal dimension compared to anthracite. The main combustion stage of SC consists of fixed carbon combustion and CaCO3 decomposition, and it has a lower ignition temperature than anthracite.
COMBUSTION SCIENCE AND TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Lijuan Zhang, Jiajun Liu, Dongming Li, Jinyuan Liu, Xiangkun Liu
Summary: In this paper, a segmentation network called MSAA-Net, which combines multi-scale features and an improved attention-aware U-Net, is proposed. The architecture improves the performance of U-Net and reduces computational costs by extracting features of different scales on a single feature layer and performing attention perception in the channel dimension.
SIGNAL IMAGE AND VIDEO PROCESSING
(2023)
Article
Chemistry, Physical
Zhe An, Jiayu Liu, Meng Cao, Jian Zhang, Yanru Zhu, Hongyan Song, Xu Xiang, Jing He
Summary: This study reports a highly efficient one-pot conversion method from ethanol to DEE, achieving high ethanol conversion and DEE selectivity using a Bi/BiCeOx bifunctional catalyst. Efficient catalysis is achieved on the interfacial Bi delta+-O-v-Ce-III sites through strong metal-support interaction.
Article
Materials Science, Multidisciplinary
Haidong Liang, Yuan Chen, Chengyuan Yang, Kenji Watanabe, Takashi Taniguchi, Goki Eda, Andrew A. Bettiol
Summary: This study demonstrates the generation of spin defects with high PL intensity and ODMR contrast using high-energy helium ion beams, while maintaining a small linewidth, thereby achieving good sensitivity. By comparing different fluences of helium irradiations, an optimal fluence is determined that can create spin defects without damaging the overall crystal lattice structure. Furthermore, with a focused beam, such spin defects can be created deterministically with nanometer precision.
ADVANCED OPTICAL MATERIALS
(2023)
Article
Engineering, Environmental
Francielle C. F. Marcos, Raphael S. Alvim, Lili Lin, Luis E. Betancourt, Davi D. Petrolini, Sanjaya D. Senanayake, Rita M. B. Alves, Jose M. Assaf, Jose A. Rodriguez, Reinaldo Giudici, Elisabete M. Assaf
Summary: The role of copper crystallization in enhancing methanol production via CO2 hydrogenation over CuZrO2 catalysts was investigated using a combination of experimental and computational studies. It was found that the intermediate steps of the catalyzed reaction might depend on the incorporation of copper in the zirconia sample. Catalysts containing only amorphous interfacial sites showed higher activity in the CO2-to-methanol hydrogenation process compared to catalysts with high crystallinity of copper.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Chemistry, Physical
Junwei Zheng, Qian Ding, Aina He, Yaqiang Dong, Lei Xie, Xubin Li, Xincai Liu, Jiawei Li
Summary: Fe-Si-B-Cu-Nb nanocrystalline ribbons with increased annealing process window and insensitivity to impurities and surface-crystallized layer were successfully prepared using industrial raw materials.
JOURNAL OF ALLOYS AND COMPOUNDS
(2023)
Article
Environmental Sciences
Xiaoyang Yang, Dongsheng Ji, Jiawei Li, Jun He, Chongshui Gong, Xiaojuan Xu, Zhe Wang, Yu Liu, Fang Bi, Zhongzhi Zhang, Yunbo Chen
Summary: Limited by the scarcity of in situ vertical observation data, the influences of biomass burning in Southeast Asia on major atmospheric carbonaceous compositions in downwind regions have not been thoroughly studied. Aircraft observations were conducted to obtain vertical distributions of black carbon (BC), carbon monoxide (CO), and carbon dioxide (CO2). Four types of profiles were identified. Simulations showed that considering the vertical BC distribution is crucial in estimating the radiative forcing (RF) and heating rate (HR) caused by BC.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Economics
Jiaming Liu, Chengzhang Li, Peng Ouyang, Jiajia Liu, Chong Wu
Summary: Financial distress prediction is crucial in the fintech field. This study utilizes tree-based gradient boosting models to predict financial distress for Chinese listed companies. The results show that these models have better predictive performance and provide insights on the significant relationships between financial indicators and financial distress.
JOURNAL OF FORECASTING
(2023)
Article
Nanoscience & Nanotechnology
Gabriel R. Jaffe, Keenan J. Smith, Kenji Watanabe, Takashi Taniguchi, Max G. Lagally, Mark A. Eriksson, Victor W. Brar
Summary: We measure the cross-plane thermal conductivity of hBN flakes exfoliated from bulk crystals and find that submicrometer thick flakes exhibit high thermal conductivities exceeding previously reported bulk values by more than 60%. We also find that introducing planar twist interfaces limits the maximum phonon mean free paths and decreases the cross-plane thermal conductivity of the stack.
ACS APPLIED MATERIALS & INTERFACES
(2023)
Article
Engineering, Electrical & Electronic
Yudeng Lin, Jianshi Tang, Bin Gao, Qingtian Zhang, He Qian, Huaqiang Wu
Summary: Deep learning models implemented using memristors offer high scalability and energy efficiency for resource-constrained edge computing applications. However, the inherent physical randomness of memristors leads to significant performance degradation. In this study, a unified architecture incorporating a Bayesian-based training method and lightweight transfer scheme is proposed to address the robustness, energy, and time consumption issues caused by memristor variations. Experimental results demonstrate that this architecture can double the speed and energy efficiency of deploying deep learning models.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Materials Science, Multidisciplinary
Najia Jiang, Jian Tang, Woyu Zhang, Yi Li, Na Li, Xiuzhen Li, Xi Chen, Renrui Fang, Zeyu Guo, Fei Wang, Jun Wang, Zhi Li, Congli He, Guangyu Zhang, Zhongrui Wang, Dashan Shang
Summary: This research introduces a bioinspired reservoir computing (RC) system in a sensor that can adaptively recognize visual stimuli and maintain high performance, efficiency, and low training costs in a wide range of illumination conditions. By utilizing voltage-tunable photoresponses of the MoS2-based phototransistor array, the system demonstrates both scotopic and photopic adaptation functions with a recognition accuracy of 91%. The horizontal modulation (HM) block enables real-time adaptive behavior under changing illumination conditions, resulting in a recognition accuracy of 90.64% (14.21% improvement over conventional RC systems). These findings pave the way for a reconfigurable in-sensor RC system with broad applications and enhanced performance for efficient artificial vision systems at the edge.
ADVANCED OPTICAL MATERIALS
(2023)
Article
Chemistry, Multidisciplinary
Binghe Xie, Zijie Ji, Jiaxin Wu, Ruan Zhang, Yunmin Jin, Kenji Watanabe, Takashi Taniguchi, Zhao Liu, Xinghan Cai
Summary: Inelastic electron tunneling (IET) accompanied by energy transfer is widely used to study collective modes in solid-state materials. By directly observing IET in a graphene-based vertical tunnel junction device, characteristic features are linked to phonon-assisted IET, demonstrating a promising method for probing low-energy excitations in graphene-based van der Waals heterostructures.
Article
Materials Science, Multidisciplinary
Clarisse Fournier, Kenji Watanabe, Takashi Taniguchi, Julien Barjon, Stephanie Buil, Jean-Pierre Hermier, Aymeric Delteil
Summary: The ability to identify and characterize homogeneous and inhomogeneous dephasing processes is crucial in solid-state quantum optics. A combination of resonant laser excitation and second-order photon correlations allows access to fast dynamics. The color center in hexagonal boron nitride experiences spectral diffusion at a characteristic time scale of tens of microseconds while emitting Fourier-limited single photons between spectral jumps.
Article
Chemistry, Multidisciplinary
Caique Serati de Brito, Paulo E. Faria Junior, Talieh S. Ghiasi, Josep Ingla-Aynes, Cesar Ricardo Rabahi, Camila Cavalini, Florian Dirnberger, Samuel Manas-Valero, Kenji Watanabe, Takashi Taniguchi, Klaus Zollner, Jaroslav Fabian, Christian Schueller, Herre S. J. van der Zant, Yara Galvao Gobato
Summary: This study presents magneto photoluminescence investigations of monolayer MoSe2 on layered A-type antiferromagnetic semiconductor CrSBr. The results reveal a clear influence of CrSBr magnetic order on the optical properties of MoSe2, including anomalous linear-polarization dependence, changes of exciton/trion energies, magnetic-field dependence of PL intensities, and asymmetric magnetic proximity interaction in the valley g-factor. First-principles calculations suggest a broken-gap (type-III) band alignment in MoSe2/CrSBr, facilitating charge transfer processes. This work establishes the potential of antiferromagnetic-nonmagnetic interfaces in controlling the valley and excitonic properties of TMDs, with implications for opto-spintronics device development.
Article
Computer Science, Artificial Intelligence
Xiang Shi, Yinpeng Liu, Jiawei Liu, Qikai Cheng, Wei Lu
Summary: Scientific papers are crucial for academic communication, but many of them lack in-depth research and present core content ambiguously, hindering the progression of science and technology. To address this challenge, the INTEGrity vERification (INTEGER) task is introduced to help researchers assess the integrity of their papers and verify the clarity of each knowledge unit. A multi-task learning model utilizing Tucker decomposition and span-level attention mechanism is proposed to accurately identify terms and their integrity. Experimental results show the effectiveness of the model.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
