The problem of mine water source has always been an important hidden danger in mine safety production.The water source under the mine working face may lead to geological disasters,such as mine collapse and water disas...The problem of mine water source has always been an important hidden danger in mine safety production.The water source under the mine working face may lead to geological disasters,such as mine collapse and water disaster.The research background of mine water source identification involves many fields such as mining production,environmental protection,resource utilization and technological progress.It is a comprehensive and interdisciplinary subject,which helps to improve the safety and sustainability of mine production.Therefore,timely and accurate identification and control of mine water source is very important to ensure mine production safety.Laser-Induced Fluorescence(LIF)technology,characterized by high sensitivity,specificity,and spatial resolution,overcomes the time-consuming nature of traditional chemical methods.In this experiment,sandstone water and old air water were collected from the Huainan mining area as original samples.Five types of mixed water samples were prepared by varying their proportions,in addition to the two original water samples,resulting in a total of seven different water samples for testing.Four preprocessing methods,namely,MinMaxScaler,StandardScaler,Standard Normal Variate(SNV)transformation,and Centering Transformation(CT),were applied to preprocess the original spectral data to reduce noise and interference.CT was determined as the optimal preprocessing method based on class discrimination,data distribution,and data range.To maintain the original data features while reducing the data dimension,including the original spectral data,five sets of data were subjected to Principal Component Analysis(PCA)and Linear Discriminant Analysis(LDA)dimensionality reduction.Through comparing the clustering effect and Fisher's ratio of the first three dimensions,PCA was identified as the optimal dimensionality reduction method.Finally,two neural network models,CT+PCA+CNN and CT+PCA+ResNet,were constructed by combining Convolutional Neural Networks(CNN)and Residual Neural Networks(ResNet),respectively.When selecting the neural network models,the training time,number of iterative parameters,accuracy,and cross-entropy loss function in the classification problem were compared to determine the model best suited for water source data.The results indicated that CT+PCA+ResNet was the optimal approach for water source identification in this study.展开更多
Cation-disordered rocksalt oxides(DRX)have been identified as promising cathode materials for high energy density applications owing to their variable elemental composition and cationic-anionic redox activity.However,...Cation-disordered rocksalt oxides(DRX)have been identified as promising cathode materials for high energy density applications owing to their variable elemental composition and cationic-anionic redox activity.However,their practical implementation has been impeded by unwanted phenomena such as irrepressible transition metal migration/dissolution and O_(2)/CO_(2)evolution,which arise due to parasitic reactions and densification-degradation mechanisms during extended cycling.To address these issues,a micron-sized DRX cathode Li_(1.2)Ni_(1/3)Ti_(1/3)W_(2/15)O_(1.85)F_(0.15)(SLNTWOF)with F substitution and ultrathin LiF coating layer is developed by alcohols assisted sol-gel method.Within this fluorination-induced integrated structure design(FISD)strategy,in-situ F substitution modifies the activity/reversibility of the cationic-anionic redox reaction,while the ultrathin LiF coating and single-crystal structure synergistically mitigate the cathode/electrolyte parasitic reaction and densification-degradation mechanism.Attributed to the multiple modifications and size effect in the FISD strategy,the SLNTWOF sample exhibits reversible cationic-anionic redox chemistry with a meliorated reversible capacity of 290.3 mA h g^(-1)at 0.05C(1C=200 mA g^(-1)),improved cycling stability of 78.5%capacity retention after 50 cycles at 0.5 C,and modified rate capability of 102.8 mA h g^(-1)at 2 C.This work reveals that the synergistic effects between bulk structure modification,surface regulation,and engineering particle size can effectively modulate the distribution and evolution of cationic-anionic redox activities in DRX cathodes.展开更多
Despite of the higher energy density and inexpensive characteristics,commercialization of layered oxide cathodes for sodium ion batteries(SIBs)is limited due to the lack of structural stability at the high voltage.Her...Despite of the higher energy density and inexpensive characteristics,commercialization of layered oxide cathodes for sodium ion batteries(SIBs)is limited due to the lack of structural stability at the high voltage.Herein,the one-step electrochemical in-situ Li doping and LiF coating are successfully achieved to obtain an advanced Na0.79Lix[Li_(0.13)Ni_(0.20)Mn_(0.67)]O_(2)@LiF(NaLi-LNM@LiF)cathode with superlattice structure.The results demonstrate that the Li^(+)doped into the alkali metal layer by electrochemical cycling act as"pillars"in the form of Li-Li dimers to stabilize the layered structure.The supplementation of Li to the superlattice structure inhibits the dissolution of transition metal ions and lattice mismatch.Furthermore,the in-situ LiF coating restrains side reactions,reduces surface cracks,and greatly improves the cycling stability.The electrochemical in-situ modification strategy significantly enhances the electrochemical performance of the half-cell.The NaLi-LNM@LiF exhibits high reversible specific capacity(170.6 m A h g^(-1)at 0.05 C),outstanding capacity retention(92.65%after 200 cycles at 0.5 C)and excellent rate performance(80 mA h g^(-1)at 7 C)in a wide voltage range of 1.5-4.5 V.This novel method of in-situ modification by electrochemical process will provide a guidance for the rational design of cathode materials for SIBs.展开更多
基金the Collaborative Innovation Center of Mine Intelligent Equipment and Technology,Anhui University of Science&Technology(CICJMITE202203)National Key R&D Program of China(2018YFC0604503)Anhui Province Postdoctoral Research Fund Funding Project(2019B350).
文摘The problem of mine water source has always been an important hidden danger in mine safety production.The water source under the mine working face may lead to geological disasters,such as mine collapse and water disaster.The research background of mine water source identification involves many fields such as mining production,environmental protection,resource utilization and technological progress.It is a comprehensive and interdisciplinary subject,which helps to improve the safety and sustainability of mine production.Therefore,timely and accurate identification and control of mine water source is very important to ensure mine production safety.Laser-Induced Fluorescence(LIF)technology,characterized by high sensitivity,specificity,and spatial resolution,overcomes the time-consuming nature of traditional chemical methods.In this experiment,sandstone water and old air water were collected from the Huainan mining area as original samples.Five types of mixed water samples were prepared by varying their proportions,in addition to the two original water samples,resulting in a total of seven different water samples for testing.Four preprocessing methods,namely,MinMaxScaler,StandardScaler,Standard Normal Variate(SNV)transformation,and Centering Transformation(CT),were applied to preprocess the original spectral data to reduce noise and interference.CT was determined as the optimal preprocessing method based on class discrimination,data distribution,and data range.To maintain the original data features while reducing the data dimension,including the original spectral data,five sets of data were subjected to Principal Component Analysis(PCA)and Linear Discriminant Analysis(LDA)dimensionality reduction.Through comparing the clustering effect and Fisher's ratio of the first three dimensions,PCA was identified as the optimal dimensionality reduction method.Finally,two neural network models,CT+PCA+CNN and CT+PCA+ResNet,were constructed by combining Convolutional Neural Networks(CNN)and Residual Neural Networks(ResNet),respectively.When selecting the neural network models,the training time,number of iterative parameters,accuracy,and cross-entropy loss function in the classification problem were compared to determine the model best suited for water source data.The results indicated that CT+PCA+ResNet was the optimal approach for water source identification in this study.
基金supported by the National Key R&D Program of China(2021YFB2401800)the National Natural Science Foundation of China(22179008,21875022)+2 种基金the Natural Science Foundation of Chongqing,China(cstc2020jcyj-msxmX0589,cstc2020jcyjmsxmX0654)the support from Beijing Institute of Technology Research Fund Program for Young Scholarsthe 4B7B beamlines radiation equipment of Beijing Synchrotron Radiation Facility(2021-BEPC-PT-005924,2021-BEPC-PT-005967)。
文摘Cation-disordered rocksalt oxides(DRX)have been identified as promising cathode materials for high energy density applications owing to their variable elemental composition and cationic-anionic redox activity.However,their practical implementation has been impeded by unwanted phenomena such as irrepressible transition metal migration/dissolution and O_(2)/CO_(2)evolution,which arise due to parasitic reactions and densification-degradation mechanisms during extended cycling.To address these issues,a micron-sized DRX cathode Li_(1.2)Ni_(1/3)Ti_(1/3)W_(2/15)O_(1.85)F_(0.15)(SLNTWOF)with F substitution and ultrathin LiF coating layer is developed by alcohols assisted sol-gel method.Within this fluorination-induced integrated structure design(FISD)strategy,in-situ F substitution modifies the activity/reversibility of the cationic-anionic redox reaction,while the ultrathin LiF coating and single-crystal structure synergistically mitigate the cathode/electrolyte parasitic reaction and densification-degradation mechanism.Attributed to the multiple modifications and size effect in the FISD strategy,the SLNTWOF sample exhibits reversible cationic-anionic redox chemistry with a meliorated reversible capacity of 290.3 mA h g^(-1)at 0.05C(1C=200 mA g^(-1)),improved cycling stability of 78.5%capacity retention after 50 cycles at 0.5 C,and modified rate capability of 102.8 mA h g^(-1)at 2 C.This work reveals that the synergistic effects between bulk structure modification,surface regulation,and engineering particle size can effectively modulate the distribution and evolution of cationic-anionic redox activities in DRX cathodes.
基金financially supported by the National Natural Science Foundation of China(51972023)。
文摘Despite of the higher energy density and inexpensive characteristics,commercialization of layered oxide cathodes for sodium ion batteries(SIBs)is limited due to the lack of structural stability at the high voltage.Herein,the one-step electrochemical in-situ Li doping and LiF coating are successfully achieved to obtain an advanced Na0.79Lix[Li_(0.13)Ni_(0.20)Mn_(0.67)]O_(2)@LiF(NaLi-LNM@LiF)cathode with superlattice structure.The results demonstrate that the Li^(+)doped into the alkali metal layer by electrochemical cycling act as"pillars"in the form of Li-Li dimers to stabilize the layered structure.The supplementation of Li to the superlattice structure inhibits the dissolution of transition metal ions and lattice mismatch.Furthermore,the in-situ LiF coating restrains side reactions,reduces surface cracks,and greatly improves the cycling stability.The electrochemical in-situ modification strategy significantly enhances the electrochemical performance of the half-cell.The NaLi-LNM@LiF exhibits high reversible specific capacity(170.6 m A h g^(-1)at 0.05 C),outstanding capacity retention(92.65%after 200 cycles at 0.5 C)and excellent rate performance(80 mA h g^(-1)at 7 C)in a wide voltage range of 1.5-4.5 V.This novel method of in-situ modification by electrochemical process will provide a guidance for the rational design of cathode materials for SIBs.
