Abstract A dual-axis tracking parabolic trough solar collector, using a certain straight-trough tube, was set up to experimentally investigate the heat collecting performance. An artificial neural network(ANN) model was developed. Experimental data were used to train and predict the mean temperature of Heat transfer fluid in the solar trough collector based on the developed model. The Levenberg-Marquardt (LM) method was also applied to optimize the weights and thresholds for the classic BP Newton algorithm, providing an ANN model with 9 hidden nodes and 30,000 training times. The predicted values are all in good agreement with the experimental data, with a mean relative error of 0.21% and a maximum error of 1.2%. In comparison, the mean relative error of the one-dimensional steady-state model reaches 1.07%. It indicates that the ANN exhibits excellent performance in predicting the export temperature of the solar collector with a specific flow rate of Heat transfer fluid. This ANN model is promising to predict the performance of solar trough collectors under different operating and environmental conditions.
{"title":"Performance Prediction of a Dual-axis Tracking Solar Trough Collector Based on Artificial Neural Network","authors":"Jue Li, Ting Xia, Ruofan Wang, Haijun Chen, Xiran Xu","doi":"10.1088/1742-6596/2636/1/012040","DOIUrl":"https://doi.org/10.1088/1742-6596/2636/1/012040","url":null,"abstract":"Abstract A dual-axis tracking parabolic trough solar collector, using a certain straight-trough tube, was set up to experimentally investigate the heat collecting performance. An artificial neural network(ANN) model was developed. Experimental data were used to train and predict the mean temperature of Heat transfer fluid in the solar trough collector based on the developed model. The Levenberg-Marquardt (LM) method was also applied to optimize the weights and thresholds for the classic BP Newton algorithm, providing an ANN model with 9 hidden nodes and 30,000 training times. The predicted values are all in good agreement with the experimental data, with a mean relative error of 0.21% and a maximum error of 1.2%. In comparison, the mean relative error of the one-dimensional steady-state model reaches 1.07%. It indicates that the ANN exhibits excellent performance in predicting the export temperature of the solar collector with a specific flow rate of Heat transfer fluid. This ANN model is promising to predict the performance of solar trough collectors under different operating and environmental conditions.","PeriodicalId":44008,"journal":{"name":"Journal of Physics-Photonics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135764055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1088/1742-6596/2632/1/012017
Yulin Liu, Xiaolu Liu, Chunguang Lu, Lei Song, Guoyu Cui, Haifeng Qian, Nick Nianxiong Tan
Abstract The intelligent vehicle designed in this paper can realize functions, such as safety detection, visual identification, remote control and manipulator grasping, and so on. Arduino MEGA is used as the main control board to send signal messages to drive vehicles. Wi-Fi module is used to receive messages to remote control vehicles. The ultrasonic and infrared module is used to realize object detection around vehicles. To realize complex route movement, raspberry pie is used for visual recognition and path planning. Data is sent to Arduino for judgment in real time. Finally, it is verified that the design effectively improves the path-planning ability and obstacle-avoidance function in a sample vehicle.
{"title":"Intelligent Vehicle Systematic Design Based on Arduino and Raspberry Pi","authors":"Yulin Liu, Xiaolu Liu, Chunguang Lu, Lei Song, Guoyu Cui, Haifeng Qian, Nick Nianxiong Tan","doi":"10.1088/1742-6596/2632/1/012017","DOIUrl":"https://doi.org/10.1088/1742-6596/2632/1/012017","url":null,"abstract":"Abstract The intelligent vehicle designed in this paper can realize functions, such as safety detection, visual identification, remote control and manipulator grasping, and so on. Arduino MEGA is used as the main control board to send signal messages to drive vehicles. Wi-Fi module is used to receive messages to remote control vehicles. The ultrasonic and infrared module is used to realize object detection around vehicles. To realize complex route movement, raspberry pie is used for visual recognition and path planning. Data is sent to Arduino for judgment in real time. Finally, it is verified that the design effectively improves the path-planning ability and obstacle-avoidance function in a sample vehicle.","PeriodicalId":44008,"journal":{"name":"Journal of Physics-Photonics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135765063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1088/1742-6596/2632/1/012024
Xianxian Wu, Yan Zhang, Bin Feng
Abstract This paper presents a novel approach for evaluating the pronunciation quality of English speech using continuous speech recognition technology. The research focuses on the application of artificial intelligence in speech recognition, utilizing web browsers on various terminal devices such as computers, mobile phones, and tablets to allow users to read the provided text aloud. The web program captures audio input from the microphone, records it in MP3 format, and uploads it to the server. The server employs the Whisper model to transcribe the audio into semantic text, which is then compared with the displayed text. By calculating the semantic distance and assessing the accuracy of pronunciation, the system provides an evaluation of pronunciation quality, marking correct and incorrect words. To achieve real-time processing, the compact tiny model is employed, and further optimization is performed using Ctranslate 2, resulting in significant performance improvements.
{"title":"English Pronunciation Quality Evaluation System Based on Continuous Speech Recognition Technology for Multi-Terminal","authors":"Xianxian Wu, Yan Zhang, Bin Feng","doi":"10.1088/1742-6596/2632/1/012024","DOIUrl":"https://doi.org/10.1088/1742-6596/2632/1/012024","url":null,"abstract":"Abstract This paper presents a novel approach for evaluating the pronunciation quality of English speech using continuous speech recognition technology. The research focuses on the application of artificial intelligence in speech recognition, utilizing web browsers on various terminal devices such as computers, mobile phones, and tablets to allow users to read the provided text aloud. The web program captures audio input from the microphone, records it in MP3 format, and uploads it to the server. The server employs the Whisper model to transcribe the audio into semantic text, which is then compared with the displayed text. By calculating the semantic distance and assessing the accuracy of pronunciation, the system provides an evaluation of pronunciation quality, marking correct and incorrect words. To achieve real-time processing, the compact tiny model is employed, and further optimization is performed using Ctranslate 2, resulting in significant performance improvements.","PeriodicalId":44008,"journal":{"name":"Journal of Physics-Photonics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135715699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1088/1742-6596/2645/1/012013
Shuicheng Gong, Fuhao Zhang, Gang Xun, Xuesong Li
Abstract With the wide application of Particle Image Velocimetry (PIV) technology in various engineering and research fields, the requirements for the accuracy, computational efficiency, and robustness of PIV algorithms are increasing. Although traditional algorithms have wide applicability, they suffer from low accuracy, large computational cost, and poor robustness. Recently, deep learning algorithms have provided new solutions, especially, convolutional neural networks with different structures, which have achieved good performance on synthetic PIV datasets. This paper proposes a structural improvement scheme for PIV convolutional neural network models. Experiments verify that the proposed method can significantly optimize the performance of the model on synthetic PIV datasets, providing a novel approach for improving other convolutional neural networks for PIV analysis.
{"title":"An Improved Convolutional Neural Network for Particle Image Velocimetry","authors":"Shuicheng Gong, Fuhao Zhang, Gang Xun, Xuesong Li","doi":"10.1088/1742-6596/2645/1/012013","DOIUrl":"https://doi.org/10.1088/1742-6596/2645/1/012013","url":null,"abstract":"Abstract With the wide application of Particle Image Velocimetry (PIV) technology in various engineering and research fields, the requirements for the accuracy, computational efficiency, and robustness of PIV algorithms are increasing. Although traditional algorithms have wide applicability, they suffer from low accuracy, large computational cost, and poor robustness. Recently, deep learning algorithms have provided new solutions, especially, convolutional neural networks with different structures, which have achieved good performance on synthetic PIV datasets. This paper proposes a structural improvement scheme for PIV convolutional neural network models. Experiments verify that the proposed method can significantly optimize the performance of the model on synthetic PIV datasets, providing a novel approach for improving other convolutional neural networks for PIV analysis.","PeriodicalId":44008,"journal":{"name":"Journal of Physics-Photonics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135715701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1088/1742-6596/2632/1/012036
Daixing Lu, Yang Zhang, Junjie Lu
Abstract Hydraulic cylinder replacement robot as a new type of engineering machinery has been increasingly used, but its end effector encounters vibrations in the process of clamping the object, so the accuracy of disassembling and assembling the cylinder will be reduced, thus reducing the replacement efficiency and affecting the user’s experience. To address this problem, virtual prototyping technology is used to study the cylinder disassembly process under real working conditions. We use the 3D modeling software Solidworks to construct a model of the cylinder replacement robot. After that, kinematic analysis of the model is carried out, then a dynamics model is built in multi-body dynamics simulation software ADAMS to simulate the process of the robot grasping the object, as a consequence, the trajectory of the end effector is calculated. A controlled dynamic model is established with Simulink and Adams by using the co-simulation technique, and optimization is carried out by using the model. Results show that the optimized control parameter can effectively reduce the end effector vibration and improve the stability and accuracy of the work.
{"title":"Vibration Reduction of Robot End Effector Based on Co-simulation Method","authors":"Daixing Lu, Yang Zhang, Junjie Lu","doi":"10.1088/1742-6596/2632/1/012036","DOIUrl":"https://doi.org/10.1088/1742-6596/2632/1/012036","url":null,"abstract":"Abstract Hydraulic cylinder replacement robot as a new type of engineering machinery has been increasingly used, but its end effector encounters vibrations in the process of clamping the object, so the accuracy of disassembling and assembling the cylinder will be reduced, thus reducing the replacement efficiency and affecting the user’s experience. To address this problem, virtual prototyping technology is used to study the cylinder disassembly process under real working conditions. We use the 3D modeling software Solidworks to construct a model of the cylinder replacement robot. After that, kinematic analysis of the model is carried out, then a dynamics model is built in multi-body dynamics simulation software ADAMS to simulate the process of the robot grasping the object, as a consequence, the trajectory of the end effector is calculated. A controlled dynamic model is established with Simulink and Adams by using the co-simulation technique, and optimization is carried out by using the model. Results show that the optimized control parameter can effectively reduce the end effector vibration and improve the stability and accuracy of the work.","PeriodicalId":44008,"journal":{"name":"Journal of Physics-Photonics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135716626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1088/1742-6596/2632/1/012025
Xin Deng, Lijun Zhao, Ruifeng Li
Abstract Multi-pedestrian tracking is one of the hot topics in computer vision. For an intelligent mobile robot, multi-pedestrian tracking from a first-person perspective can provide information for navigating through a crowd and ensure safety. Most of the existing methods cannot deal with occlusion and trajectory overlap well. In this paper, a multi-pedestrian tracking method fusing two-stage matching is proposed. Firstly, the detection and the corresponding feature values of the pedestrians are obtained by a multi-task learning network based on CenterNet. Then the detected pedestrians are matched with feature values by greedy strategy. When dealing with the reappearance of pedestrians caused by occlusion or trajectory overlap, the sample database is established to update the samples in real time. The color histogram and HOG feature are calculated for each sample. When the pedestrian disappears, the direction of disappearance and the last position is recorded for the selection of trajectory. Finally, the KM algorithm is used for cross-frame matching. Our method is compared with some recent methods on MOT data sets. The result shows that our method has a significant improvement in the main evaluation index MOTA.
{"title":"Multi-pedestrian Tracking Method Fusing Two-stage Matching","authors":"Xin Deng, Lijun Zhao, Ruifeng Li","doi":"10.1088/1742-6596/2632/1/012025","DOIUrl":"https://doi.org/10.1088/1742-6596/2632/1/012025","url":null,"abstract":"Abstract Multi-pedestrian tracking is one of the hot topics in computer vision. For an intelligent mobile robot, multi-pedestrian tracking from a first-person perspective can provide information for navigating through a crowd and ensure safety. Most of the existing methods cannot deal with occlusion and trajectory overlap well. In this paper, a multi-pedestrian tracking method fusing two-stage matching is proposed. Firstly, the detection and the corresponding feature values of the pedestrians are obtained by a multi-task learning network based on CenterNet. Then the detected pedestrians are matched with feature values by greedy strategy. When dealing with the reappearance of pedestrians caused by occlusion or trajectory overlap, the sample database is established to update the samples in real time. The color histogram and HOG feature are calculated for each sample. When the pedestrian disappears, the direction of disappearance and the last position is recorded for the selection of trajectory. Finally, the KM algorithm is used for cross-frame matching. Our method is compared with some recent methods on MOT data sets. The result shows that our method has a significant improvement in the main evaluation index MOTA.","PeriodicalId":44008,"journal":{"name":"Journal of Physics-Photonics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135716633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1088/1742-6596/2632/1/012013
Yiming Chen
Abstract Insulators are important components for achieving electrical insulation and mechanical support, but they are prone to various defects in harsh operating environments, which can damage their mechanical strength and insulation performance. This article proposes the Shuffle YOLOv7 model based on the YOLOv7 algorithm for insulator defect detection, aiming to solve the weakness of low precision in traditional object detection algorithms when facing complex backgrounds and small-sized defects. To address the issue of low attention to flashover faults in traditional algorithms, the ShuffleAttention fusion attention mechanism is supplied to concentrate on both intra-channel and inter-channel deep features, and the original PANet structure is replaced with a pyramid which has a bidirectional feature fusion structure to enhance the network’s feature extraction ability. The Focal-EIOU LOSS optimization method focuses on high-quality prior boxes to improve model accuracy, and the effectiveness of the optimization method is verified through ablation experiments. These results of the experiment show that the proposed algorithm achieves varying degrees of performance improvement in terms of precision, recall, average precision, and overall loss compared to mainstream object detection algorithms in detecting insulator damage and flashover.
{"title":"Insulator Defect Detection Method upon Fused Attention Mechanism and Bidirectional Feature Fusion","authors":"Yiming Chen","doi":"10.1088/1742-6596/2632/1/012013","DOIUrl":"https://doi.org/10.1088/1742-6596/2632/1/012013","url":null,"abstract":"Abstract Insulators are important components for achieving electrical insulation and mechanical support, but they are prone to various defects in harsh operating environments, which can damage their mechanical strength and insulation performance. This article proposes the Shuffle YOLOv7 model based on the YOLOv7 algorithm for insulator defect detection, aiming to solve the weakness of low precision in traditional object detection algorithms when facing complex backgrounds and small-sized defects. To address the issue of low attention to flashover faults in traditional algorithms, the ShuffleAttention fusion attention mechanism is supplied to concentrate on both intra-channel and inter-channel deep features, and the original PANet structure is replaced with a pyramid which has a bidirectional feature fusion structure to enhance the network’s feature extraction ability. The Focal-EIOU LOSS optimization method focuses on high-quality prior boxes to improve model accuracy, and the effectiveness of the optimization method is verified through ablation experiments. These results of the experiment show that the proposed algorithm achieves varying degrees of performance improvement in terms of precision, recall, average precision, and overall loss compared to mainstream object detection algorithms in detecting insulator damage and flashover.","PeriodicalId":44008,"journal":{"name":"Journal of Physics-Photonics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135716771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1088/1742-6596/2632/1/012019
Xiao Hu, Shenfu Pan, Dongdong Li, Long Feng, Yuan Zhao
Abstract In recent years, with the development of sensors, communication networks, and deep learning, drones have been widely used in the field of object detection, tracking, and positioning. However, there are inefficient task execution and some complex algorithms still need to rely on large servers, which is intolerable in rescue and traffic scheduling tasks. Designing fast algorithms that can run on the airborne computer can effectively solve the problem. In this paper, an object detection and location system for drones is proposed. We combine the improved object detection algorithm ST-YOLO based on YOLOX and Swin Transformer with the visual positioning algorithm and deploy it on the airborne end by using TensorRT to realize the detection and location of objects during the flight of the drone. Field experiments show that the established system and algorithm are effective.
{"title":"An airborne object detection and location system based on deep inference","authors":"Xiao Hu, Shenfu Pan, Dongdong Li, Long Feng, Yuan Zhao","doi":"10.1088/1742-6596/2632/1/012019","DOIUrl":"https://doi.org/10.1088/1742-6596/2632/1/012019","url":null,"abstract":"Abstract In recent years, with the development of sensors, communication networks, and deep learning, drones have been widely used in the field of object detection, tracking, and positioning. However, there are inefficient task execution and some complex algorithms still need to rely on large servers, which is intolerable in rescue and traffic scheduling tasks. Designing fast algorithms that can run on the airborne computer can effectively solve the problem. In this paper, an object detection and location system for drones is proposed. We combine the improved object detection algorithm ST-YOLO based on YOLOX and Swin Transformer with the visual positioning algorithm and deploy it on the airborne end by using TensorRT to realize the detection and location of objects during the flight of the drone. Field experiments show that the established system and algorithm are effective.","PeriodicalId":44008,"journal":{"name":"Journal of Physics-Photonics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135716777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1088/1742-6596/2632/1/012005
Jiuxin Hu, Zhihao Pan, Zhiyong Li, Jin Tang
Abstract Registration is a critical task in the field of point clouds, aiming to align data acquired at different times or from different viewpoints for accurate matching. Deep learning methods have made important progress in point cloud registration tasks. However, most existing approaches do not handle the non-overlapping parts of point clouds, resulting in poor performance in low-overlap and noisy scenarios. We propose a registration model called OPSNet, which achieves optimal alignment transformation estimation and overlapping region prediction through an iterative process. OPSNet consists of modules including global feature extraction, overlapping region prediction segmentation, and alignment registration. By utilizing a segmentation algorithm to deal with the non-overlapping parts of data, OPSNet reduces the adverse effects caused by non-overlapping regions in point cloud registration. The model learns feature representations and performs iterative optimization to achieve precise point cloud alignment. We conduct comprehensive experiments on common point cloud registration datasets and compare OPSNet with several classical point cloud registration methods. The experimental results display that OPSNet achieves outstanding performance in terms of rotation and translation errors, outperforming other methods. Additionally, we evaluate the registration performance under different overlap ratios and find that OPSNet can achieve better registration results even in low-overlap scenarios.
{"title":"OPSNet: Point Cloud Registration Based on Overlapping Predictive Segmentation","authors":"Jiuxin Hu, Zhihao Pan, Zhiyong Li, Jin Tang","doi":"10.1088/1742-6596/2632/1/012005","DOIUrl":"https://doi.org/10.1088/1742-6596/2632/1/012005","url":null,"abstract":"Abstract Registration is a critical task in the field of point clouds, aiming to align data acquired at different times or from different viewpoints for accurate matching. Deep learning methods have made important progress in point cloud registration tasks. However, most existing approaches do not handle the non-overlapping parts of point clouds, resulting in poor performance in low-overlap and noisy scenarios. We propose a registration model called OPSNet, which achieves optimal alignment transformation estimation and overlapping region prediction through an iterative process. OPSNet consists of modules including global feature extraction, overlapping region prediction segmentation, and alignment registration. By utilizing a segmentation algorithm to deal with the non-overlapping parts of data, OPSNet reduces the adverse effects caused by non-overlapping regions in point cloud registration. The model learns feature representations and performs iterative optimization to achieve precise point cloud alignment. We conduct comprehensive experiments on common point cloud registration datasets and compare OPSNet with several classical point cloud registration methods. The experimental results display that OPSNet achieves outstanding performance in terms of rotation and translation errors, outperforming other methods. Additionally, we evaluate the registration performance under different overlap ratios and find that OPSNet can achieve better registration results even in low-overlap scenarios.","PeriodicalId":44008,"journal":{"name":"Journal of Physics-Photonics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135716778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1088/1742-6596/2645/1/012017
None XinWang, Qi Chao Yang, Hai tao Wang, Yu Zheng, Geng hang Zhong, Jiang wei Zhao
Abstract Two-dimensional sulfide has been widely recognized as a promising new type of catalyst to replace precious metals due to its adjustable electronic structure, low cost, and high stability. In this paper, monolayer molybdenum disulfide (MoS 2 ) and layer-controlled tungsten disulfide (WS 2 ) were successfully prepared by chemical vapor deposition (CVD). The two prepared materials’ morphology, structure, and thickness were investigated. The catalytic performance of two-dimensional sulfides was studied under an acidic environment. The results exhibit good catalytic performance toward hydrogen evolution with 63.6 mV/dec low Tafel slope of MoS 2 and 72.8 mV/dec of WS 2 .
{"title":"Reaction Controllable preparation and electrocatalytic performance of two-dimensional sulfides","authors":"None XinWang, Qi Chao Yang, Hai tao Wang, Yu Zheng, Geng hang Zhong, Jiang wei Zhao","doi":"10.1088/1742-6596/2645/1/012017","DOIUrl":"https://doi.org/10.1088/1742-6596/2645/1/012017","url":null,"abstract":"Abstract Two-dimensional sulfide has been widely recognized as a promising new type of catalyst to replace precious metals due to its adjustable electronic structure, low cost, and high stability. In this paper, monolayer molybdenum disulfide (MoS 2 ) and layer-controlled tungsten disulfide (WS 2 ) were successfully prepared by chemical vapor deposition (CVD). The two prepared materials’ morphology, structure, and thickness were investigated. The catalytic performance of two-dimensional sulfides was studied under an acidic environment. The results exhibit good catalytic performance toward hydrogen evolution with 63.6 mV/dec low Tafel slope of MoS 2 and 72.8 mV/dec of WS 2 .","PeriodicalId":44008,"journal":{"name":"Journal of Physics-Photonics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135716779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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