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Dual Catalytic and Self-Assembled Growth of Two-Dimensional Transition Metal Dichalcogenides Through Simultaneous Predeposition Process
No full textThe recent introduction of alkali metal halide catalysts for chemical vapor deposition (CVD) of transition metal dichalcogenides (TMDs) has enabled remarkable two-dimensional (2D) growth. However, the process development and growth mechanism require further exploration to enhance the effects of salts and understand the principles. Herein, simultaneous predeposition of a metal source (MoO3) and salt (NaCl) by thermal evaporation is adopted. As a result, remarkable growth behaviors such as promoted 2D growth, easy patterning, and potential diversity of target materials can be achieved. Step-by-step spectroscopy combined with morphological analyses reveals a reaction path for MoS2 growth in which NaCl reacts separately with S and MoO3 to form Na2SO4 and Na2Mo2O7 intermediates, respectively. These intermediates provide a favorable environment for 2D growth, including an enhanced source supply and liquid medium. Consequently, large grains of monolayer MoS2 are formed by self-assembly, indicating the merging of small equilateral triangular grains on the liquid intermediates. This study is expected to serve as an ideal reference for understanding the principles of salt catalysis and evolution of CVD in the preparation of 2D TMDs
A convolutional neural network model for SOH estimation of Li-ion batteries with physical interpretability
No full textPrevious machine learning models for state-of-health (SOH) estimation of Li-ion batteries have relied on prescribed statistical features. However, there is little theoretical understanding of the relationships between these features and SOH degradation patterns of the batteries. This study proposes a convolutional neural network model to estimate the future SOH value of Li-ion bat-teries in the early phases of qualification tests. First, capacity degradation data are transformed into two-dimensional images using recurrence plots and Gramian angular fields, highlighting the time-series features of the data. Second, five types of convolutional neural network models are developed to estimate the SOH values of Li-ion batteries for a certain cycle. Here, class activation maps are generated to present how the models arrive at their conclusions. Finally, the perfor-mance and reliability of the developed models are assessed under various experimental condi-tions. The proposed approach has the following two advantages: it automatically extracts important temporal features from the capacity degradation data for SOH estimation, and obtains the contribution of each temporal feature with respect to the estimation process. The experi-mental results on 379Li-ion batteries confirm that the proposed approach can reduce the time required for qualification tests to 50 cycles, under a 6% mean absolute percentage error
Statistical analysis of effects of test conditions on compressive strength of cement solidified radioactive waste
No full textRadioactive waste should be solidified before being disposed of in the repository to eliminate liquidity or dispersibility. Cement is a widely used solidifying media for radioactive waste, and cement solidified waste should satisfy the minimum compressive strength of the waste acceptance criteria of a radioactive repository. Although the compressive strength of waste should be measured by the test method provided by the waste acceptance criteria, the method differs depending on the operating repository of different countries. Considering the measured compressive strength changes depending on test conditions, the effect of test conditions should be analyzed to avoid overestimation or underestimation of the compressive strength during disposal. We selected test conditions such as the height-to-diameter ratio, loading rate, and porosity as the main factors affecting the compressive strength of cement solidified radioactive waste. Owing to the large variance in measured compressive strength, the effects of the test conditions were analyzed via statistical analyses using parametric and nonparametric methods. The results showed that the test condition of the lower loading rate, with a height-to-diameter ratio of two, reflected the actual cement content well, while the porosity showed no correlation. The compressive strength assessment method that reflects the large variance of strengths was suggested
Environment-Adaptable Edge-Computing Gas Sensor Device with Analog-Assisted Continual Learning Scheme
No full textThis paper presents a multi-gas sensor device whose structure is optimized for edge computing capability under internet of things (IoT) environments. Considering inherent sensor device characteristics susceptible to environmental factors like temperature and humidity, edge-computing capability for the on-site sensor calibration and pattern recognition (PR) is facilitated through a proposed analog-assisted continual learning scheme. An environment-adaptable continual learning (EACL) is proposed to combine multiple learning processes under different environments including chamber and on-site. Its computation burden is much relieved to be integrated into the edge device by adopting the analog-assisted structure, where a designed readout integrated circuit (ROIC) for automatic calibration normalizes gas-sensor data. For functional feasibility, an edge-computing IoT device prototype is manufactured with a fabricated ROIC and an in-house semiconductor-type sensor array, supporting wireless on-site monitoring platform interfaces. The environment-adaptable edge-computing capability is functionally verified through EACL-PR experiments on hazardous gases such as NO 2 and CO under environmental factor variations. The average PR accuracy of 97% is achieved on several kinds of mixture gas patterns. The analog-assisted operation is verified to reduce the training cycles by 3 times while the EACL itself achieves 25% better efficiency
Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals
Get PDFVibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effective method for predictive maintenance because it is used to troubleshoot instantaneous faults as well as periodic maintenance. Numerous studies conducted in this vein have been published in a variety of outlets. This review documents data-driven and recently published deep learning techniques for vibration-based condition monitoring. Numerous studies were obtained from two reputable indexing databases, Web of Science and Scopus. Following a thorough review, 59 studies were selected for synthesis. The selected studies are then systematically discussed to provide researchers with an in-depth view of deep learning-based fault diagnosis methods based on vibration signals. Additionally, a few remarks regarding future research directions are made, including graph-based neural networks, physics-informed ML, and a transformer convolutional network-based fault diagnosis method
Non-Fullerene-Based Inverted Organic Photovoltaic Device with Long-Term Stability
No full textIn this work, we developed the PM6:Y6-based inverted structure organic photovoltaic (i-OPV) with improved power conversion efficiency (PCE) and long-term stability by resolving the origins of the performance deterioration. The deep defects between the metal oxide-based electron transport layer and bulk-heterojunction photoactive layer interface were responsible for suboptimal PCE and facilitated degradation of devices. While the density of deep traps is increased during the storage of i-OPV, the penetrative oxygen-containing defects additionally generated shallow traps below the band-edge of Y6, causing an additional loss in the open-circuit voltage. The suppression of interfacial defects by chemical modification effectively improved the PCE and long-term stability of i-OPV. The modified i-OPV (mi-OPV) achieved a PCE of 17.42%, which is the highest value among the reported PM6:Y6-based i-OPV devices. Moreover, long-term stability was significantly improved: similar to 90% and similar to 80% retention of its initial PCE after 1200 h of air storage and illumination, respectively
The shapes of synthesized two-dimensional materials
No full textThe shapes of as-grown/as-etched two-dimensional (2D) islands are the most recognizable features of the chemical vapor deposition (CVD) process which reveal a lot of information about the chemical environment of growth/etching, the effect of the substrate and the growth/etching kinetics. Here, we summarize experimentally observed shapes of CVD-grown 2D materials from previous reports, theoretical understanding on the corresponding mechanisms and the simulations of the corresponding growth/etching processes. Each part of the review article is organized by combining the fundamental principles that govern the shapes of crystals during growth/etching and the related experimental observations to facilitate the readers to read and understand the content. The content of the review includes attachment-limited 2D materials growth, diffusion-limited 2D materials growth (both with and without influence of the substrate), etching and merging of 2D materials with and without considering the effect of the substrate. We also discuss growth and etching of multilayered 2D islands during CVD???a topic still under development. We wish this review will also guide the audience to understand the kinetics of 2D materials growth and to improve controllable synthesis of various 2D materials. ?? 2022 The Authors. SmartMat published by Tianjin University and John Wiley & Sons Australia, Ltd
Systems immunology of regulatory T cells: can one circuit explain it all?
No full textRegulatory T (Treg) cells play vital roles in immune homeostasis and response, including discrimination between self-and non-self-antigens, containment of immunopathology, and inflammation resolution. These diverse functions are orchestrated by cellular circuits involving Tregs and other cell types across space and time. Despite dramatic progress in our understanding of Treg biology, a quantitative framework capturing how Treg-containing circuits give rise to these diverse functions is lacking. Here, we propose that different facets of Treg func-tion can be interpreted as distinct operating regimes of the same underlying circuit. We discuss how a systems immunology approach, involving quantitative experiments, computational modeling, and machine learning, can advance our understanding of Treg function, and help identify general operating and design principles underlying immune regulation