Reducing the dissolution of Mn from LiMn_(2)O_(4)(LMO)and enhancing the stability of film electrodes are critical and challenging for Li+ions selective extraction via electrochemically switched ion exchange technology...Reducing the dissolution of Mn from LiMn_(2)O_(4)(LMO)and enhancing the stability of film electrodes are critical and challenging for Li+ions selective extraction via electrochemically switched ion exchange technology.In this work,we prepared a nitrogen-doped carbon cladding LMO(C-N@LMO)by polymerization of polypyrrole and high-temperature annealing in the N2 gas to achieve the above purpose.The modified C-N@LMO film electrode exhibited lower Mn dissolution and better cyclic stability than the LMO film electrode.The dissolution ratio of Mn from the C-N@LMO film electrode decreased by 42%compared to the LMO film electrode after 10 cycles.The cladding layer not only acted as a protective layer but also functioned as a conductive shell,accelerating the migration rate of Li+ions.The intercalation equilibrium time of the C-N@LMO film electrode reached within an hour during the extraction of Li+ions,which was 33%less compared to the pure LMO film electrode.Meanwhile,the C-N@LMO film electrode retained evident selectivity toward Li+ions,and the separation factor was 118.38 for Li+toward Mg2+in simulated brine.Therefore,the C-N@LMO film electrode would be a promising candidate for the recovery of Li+ions from salt lakes.展开更多
The performance degradation is a crucial factor affecting the commercialization of proton exchange membrane electrolyzer.However,it is difficult to establish a mechanism model incorporating all degradation categories ...The performance degradation is a crucial factor affecting the commercialization of proton exchange membrane electrolyzer.However,it is difficult to establish a mechanism model incorporating all degradation categories due to their different time and spatial scales.In this paper,the data-driven method is employed to predict the electrolyzer voltage variation over time based on a convolutional neural network-long short term memory(CNNLSTM)model.First,two datasets including constant operation for 1140 h and start-stop load for 660 h are collected from the durability tests.Second,the data-driven models are trained through the experimental data and the model hyper-parameters are optimized.Finally,the electrolyzer degradation in the next few hundred hours is predicted,and the prediction accuracy is compared with other time-series algorithms.The results show that the model can predict the degradation precisely on both datasets,with the R2 higher than 0.98.Compared to con-ventional models,the algorithm shows better fitting characteristic to the experimental data,especially as the prediction time increases.For constant and start-stop operations,the electrolyzers degradate by 4.5%and 2.5%respectively after 1000 h.The proposed method shows great potential for real-time monitoring in the electrolyzer system.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.U21A20303,22078217 and U20A20141).
文摘Reducing the dissolution of Mn from LiMn_(2)O_(4)(LMO)and enhancing the stability of film electrodes are critical and challenging for Li+ions selective extraction via electrochemically switched ion exchange technology.In this work,we prepared a nitrogen-doped carbon cladding LMO(C-N@LMO)by polymerization of polypyrrole and high-temperature annealing in the N2 gas to achieve the above purpose.The modified C-N@LMO film electrode exhibited lower Mn dissolution and better cyclic stability than the LMO film electrode.The dissolution ratio of Mn from the C-N@LMO film electrode decreased by 42%compared to the LMO film electrode after 10 cycles.The cladding layer not only acted as a protective layer but also functioned as a conductive shell,accelerating the migration rate of Li+ions.The intercalation equilibrium time of the C-N@LMO film electrode reached within an hour during the extraction of Li+ions,which was 33%less compared to the pure LMO film electrode.Meanwhile,the C-N@LMO film electrode retained evident selectivity toward Li+ions,and the separation factor was 118.38 for Li+toward Mg2+in simulated brine.Therefore,the C-N@LMO film electrode would be a promising candidate for the recovery of Li+ions from salt lakes.
基金financial supports of National Key Research and Development Program of China(No.2021YFB4000100)National Natural Science Foundation of China(No.52322604).
文摘The performance degradation is a crucial factor affecting the commercialization of proton exchange membrane electrolyzer.However,it is difficult to establish a mechanism model incorporating all degradation categories due to their different time and spatial scales.In this paper,the data-driven method is employed to predict the electrolyzer voltage variation over time based on a convolutional neural network-long short term memory(CNNLSTM)model.First,two datasets including constant operation for 1140 h and start-stop load for 660 h are collected from the durability tests.Second,the data-driven models are trained through the experimental data and the model hyper-parameters are optimized.Finally,the electrolyzer degradation in the next few hundred hours is predicted,and the prediction accuracy is compared with other time-series algorithms.The results show that the model can predict the degradation precisely on both datasets,with the R2 higher than 0.98.Compared to con-ventional models,the algorithm shows better fitting characteristic to the experimental data,especially as the prediction time increases.For constant and start-stop operations,the electrolyzers degradate by 4.5%and 2.5%respectively after 1000 h.The proposed method shows great potential for real-time monitoring in the electrolyzer system.
