We took Co_(0.2)Ni_(0.8)-MOF-74 with bimetallic synergistic effect as the basic material,and selected rare earth ions Ho,Gd,and Er with ion radii close to Co and Ni as the research objects for doping.The influence of ...We took Co_(0.2)Ni_(0.8)-MOF-74 with bimetallic synergistic effect as the basic material,and selected rare earth ions Ho,Gd,and Er with ion radii close to Co and Ni as the research objects for doping.The influence of rare earth ion doping amount and doping type on the eNRR performance of the catalyst was explored.The experimental results show that the ammonia yield rate and Faraday efficiency doped with Co_(0.2)Ni_(0.8)-MOF-0.5Ho are the highest,reaching 1.28×10^(-10)mol·s^(-1)·cm^(-2)/39.8%,which is higher than the1.12×10^(-10)mol·s^(-1)·cm^(-2)/32.2%of Co_(0.2)Ni_(0.8)-MOF-74,and is about 14.3%/23.7%higher than that without doping,respectively.And the stability of Co_(0.2)Ni_(0.8)-MOF-0.5 Ho is good(after 80 hours of continuous testing,the current density did not significantly decrease).This is mainly due to doping,which gives Co_(0.2)Ni_(0.8)-MOF-74 a larger specific surface area and catalytic active sites.The catalyst doped at the same time has more metal cation centers,which increases the electron density of the metal centers and enhances the corresponding eNRR performance.展开更多
Microseismic source location is crucial for the early warning of rockburst risks.However,the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy.Intelligent techni...Microseismic source location is crucial for the early warning of rockburst risks.However,the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy.Intelligent techniques,such as the full convolutional neural network(FCNN),can capture spatial information but struggle with complex microseismic sequence.Combining the FCNN with the long shortterm memory(LSTM)network enables better time-series signal classification by integrating multiscale information and is therefore suitable for waveform location.The LSTM-FCNN model does not require extensive data preprocessing and it simplifies the microseismic source location through feature extraction.In this study,we utilized the LSTM-FCNN as a regression learning model to locate the seismic focus.Initially,the method of short-time-average/long-time-average(STA/LTA)arrival time picking was employed to augment spatiotemporal information.Subsequently,oversampling the on-site data was performed to address the issue of data imbalance,and finally,the performance of LSTM-FCNN was tested.Meanwhile,we compared the LSTM-FCNN model with previous deep-learning models.Our results demonstrated remarkable location capabilities with a mean absolute error(MAE)of only 7.16 m.The model can realize swift training and high accuracy,thereby significantly improving risk warning of rockbursts.展开更多
基金Funded by the Central Government Guides Local Funds for Scientific and Technological Development(No.2023ZYQ004)the Hunan Provincial Natural Science Foundation of China(No.2021JJ50036)the Hunan Provincial Key Research and Development Plan(No.2023GK2083)。
文摘We took Co_(0.2)Ni_(0.8)-MOF-74 with bimetallic synergistic effect as the basic material,and selected rare earth ions Ho,Gd,and Er with ion radii close to Co and Ni as the research objects for doping.The influence of rare earth ion doping amount and doping type on the eNRR performance of the catalyst was explored.The experimental results show that the ammonia yield rate and Faraday efficiency doped with Co_(0.2)Ni_(0.8)-MOF-0.5Ho are the highest,reaching 1.28×10^(-10)mol·s^(-1)·cm^(-2)/39.8%,which is higher than the1.12×10^(-10)mol·s^(-1)·cm^(-2)/32.2%of Co_(0.2)Ni_(0.8)-MOF-74,and is about 14.3%/23.7%higher than that without doping,respectively.And the stability of Co_(0.2)Ni_(0.8)-MOF-0.5 Ho is good(after 80 hours of continuous testing,the current density did not significantly decrease).This is mainly due to doping,which gives Co_(0.2)Ni_(0.8)-MOF-74 a larger specific surface area and catalytic active sites.The catalyst doped at the same time has more metal cation centers,which increases the electron density of the metal centers and enhances the corresponding eNRR performance.
基金financial support of the Fundamental Research Funds for the Central Universities(Grant No.2022XSCX35)the National Natural Science Foundation of China(Grant Nos.51934007 and 52104230).
文摘Microseismic source location is crucial for the early warning of rockburst risks.However,the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy.Intelligent techniques,such as the full convolutional neural network(FCNN),can capture spatial information but struggle with complex microseismic sequence.Combining the FCNN with the long shortterm memory(LSTM)network enables better time-series signal classification by integrating multiscale information and is therefore suitable for waveform location.The LSTM-FCNN model does not require extensive data preprocessing and it simplifies the microseismic source location through feature extraction.In this study,we utilized the LSTM-FCNN as a regression learning model to locate the seismic focus.Initially,the method of short-time-average/long-time-average(STA/LTA)arrival time picking was employed to augment spatiotemporal information.Subsequently,oversampling the on-site data was performed to address the issue of data imbalance,and finally,the performance of LSTM-FCNN was tested.Meanwhile,we compared the LSTM-FCNN model with previous deep-learning models.Our results demonstrated remarkable location capabilities with a mean absolute error(MAE)of only 7.16 m.The model can realize swift training and high accuracy,thereby significantly improving risk warning of rockbursts.
