Aqueous Zn batteries are promising candidates for grid-scale renewable energy storage.Foil electrodes have been widely investigated and applied as anode materials for aqueous Zn batteries,however,they suffer from limi...Aqueous Zn batteries are promising candidates for grid-scale renewable energy storage.Foil electrodes have been widely investigated and applied as anode materials for aqueous Zn batteries,however,they suffer from limited surface area and severe interfacial issues including metallic dendrites and corrosion side reactions,limiting the depth of discharge(DOD)of the foil electrode materials.Herein,a low-temperature replacement reaction is utilized to in-situ construct a three-dimensional(3D)corrosion-resistant interface for deeply rechargeable Zn foil electrodes.Specifically,the deliberate low-temperature environment controlled the replacement rate between polycrystalline Zn metal and oxalic acid,producing a Zn foil electrode with distinct 3D corrosion-resistant interface(3DCI-Zn),which differed from conventional two-dimensional(2D)protective structure and showed an order of magnitude higher surface area.Consequently,the 3DCI-Zn electrode exhibited dendrite-free and anticorrosion properties,and achieved stable plating/stripping performance for 1000 h at 10 mA cm^(-2)and 10 mAh cm^(-2)with a remarkable DOD of 79%.After pairing with a MnO2cathode with a high areal capacity of 4.2 mAh cm^(-2),the pouch cells delivered 168 Wh L^(-1)and a capacity retention of 89.7%after 100 cycles with a low negative/positive(N/P)ratio of 3:1.展开更多
Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and computer science.These researchers adopt ...Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and computer science.These researchers adopt various research paradigms to attack the same structure prediction problem:biochemists and physicists attempt to reveal the principles governing protein folding;mathematicians,especially statisticians,usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure,while computer scientists formulate protein structure prediction as an optimization problem-finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native structure.These research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman,namely,data modeling and algorithmic modeling.Recently,we have also witnessed the great success of deep learning in protein structure prediction.In this review,we present a survey of the efforts for protein structure prediction.We compare the research paradigms adopted by researchers from different fields,with an emphasis on the shift of research paradigms in the era of deep learning.In short,the algorithmic modeling techniques,especially deep neural networks,have considerably improved the accuracy of protein structure prediction;however,theories interpreting the neural networks and knowledge on protein folding are still highly desired.展开更多
With the rapid increase of observational,experimental and simulated data for stochastic systems,tremendous efforts have been devoted to identifying governing laws underlying the evolution of these systems.Despite the ...With the rapid increase of observational,experimental and simulated data for stochastic systems,tremendous efforts have been devoted to identifying governing laws underlying the evolution of these systems.Despite the broad applications of non-Gaussian fluctuations in numerous physical phenomena,the data-driven approaches to extracting stochastic dynamics with Levy noise are relatively few.In this work,we propose aWeak Collocation Regression(WCR)to explicitly reveal unknown stochastic dynamical systems,i.e.,the Stochastic Differential Equation(SDE)with bothα-stable Levy noise and Gaussian noise,from discrete aggregate data.This method utilizes the evolution equation of the probability distribution function,i.e.,the Fokker-Planck(FP)equation.With the weak form of the FP equation,the WCR constructs a linear system of unknown parameters where all integrals are evaluated by Monte Carlo method with the observations.Then,the unknown parameters are obtained by a sparse linear regression.For a SDE with Levy noise,the corresponding FP equation is a partial integro-differential equation(PIDE),which contains nonlocal terms,and is difficult to deal with.The weak form can avoid complicated multiple integrals.Our approach can simultaneously distinguish mixed noise types,even in multi-dimensional problems.Numerical experiments demonstrate that our method is accurate and computationally efficient.展开更多
基金financially supported by the National Natural Science Foundation of China (No.22205068,22109144)the“CUG Scholar”Scientific Research Funds at China University of Geosciences (Wuhan) (Project No.2022118)the Fundamental Research Funds for the Central Universities,China University of Geosciences (Wuhan) (No.162301202673)。
文摘Aqueous Zn batteries are promising candidates for grid-scale renewable energy storage.Foil electrodes have been widely investigated and applied as anode materials for aqueous Zn batteries,however,they suffer from limited surface area and severe interfacial issues including metallic dendrites and corrosion side reactions,limiting the depth of discharge(DOD)of the foil electrode materials.Herein,a low-temperature replacement reaction is utilized to in-situ construct a three-dimensional(3D)corrosion-resistant interface for deeply rechargeable Zn foil electrodes.Specifically,the deliberate low-temperature environment controlled the replacement rate between polycrystalline Zn metal and oxalic acid,producing a Zn foil electrode with distinct 3D corrosion-resistant interface(3DCI-Zn),which differed from conventional two-dimensional(2D)protective structure and showed an order of magnitude higher surface area.Consequently,the 3DCI-Zn electrode exhibited dendrite-free and anticorrosion properties,and achieved stable plating/stripping performance for 1000 h at 10 mA cm^(-2)and 10 mAh cm^(-2)with a remarkable DOD of 79%.After pairing with a MnO2cathode with a high areal capacity of 4.2 mAh cm^(-2),the pouch cells delivered 168 Wh L^(-1)and a capacity retention of 89.7%after 100 cycles with a low negative/positive(N/P)ratio of 3:1.
基金the National Key R&D Program of China(Grant No.2020YFA0907000)lthe National Natural Science Foundation of China(Grant Nos.32271297,62072435,31770775,and 31671369)for providing financial support for this study and publication charges.
文摘Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and computer science.These researchers adopt various research paradigms to attack the same structure prediction problem:biochemists and physicists attempt to reveal the principles governing protein folding;mathematicians,especially statisticians,usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure,while computer scientists formulate protein structure prediction as an optimization problem-finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native structure.These research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman,namely,data modeling and algorithmic modeling.Recently,we have also witnessed the great success of deep learning in protein structure prediction.In this review,we present a survey of the efforts for protein structure prediction.We compare the research paradigms adopted by researchers from different fields,with an emphasis on the shift of research paradigms in the era of deep learning.In short,the algorithmic modeling techniques,especially deep neural networks,have considerably improved the accuracy of protein structure prediction;however,theories interpreting the neural networks and knowledge on protein folding are still highly desired.
基金supported by the National Key R&D Program of China(Grant No.2021YFA0719200).
文摘With the rapid increase of observational,experimental and simulated data for stochastic systems,tremendous efforts have been devoted to identifying governing laws underlying the evolution of these systems.Despite the broad applications of non-Gaussian fluctuations in numerous physical phenomena,the data-driven approaches to extracting stochastic dynamics with Levy noise are relatively few.In this work,we propose aWeak Collocation Regression(WCR)to explicitly reveal unknown stochastic dynamical systems,i.e.,the Stochastic Differential Equation(SDE)with bothα-stable Levy noise and Gaussian noise,from discrete aggregate data.This method utilizes the evolution equation of the probability distribution function,i.e.,the Fokker-Planck(FP)equation.With the weak form of the FP equation,the WCR constructs a linear system of unknown parameters where all integrals are evaluated by Monte Carlo method with the observations.Then,the unknown parameters are obtained by a sparse linear regression.For a SDE with Levy noise,the corresponding FP equation is a partial integro-differential equation(PIDE),which contains nonlocal terms,and is difficult to deal with.The weak form can avoid complicated multiple integrals.Our approach can simultaneously distinguish mixed noise types,even in multi-dimensional problems.Numerical experiments demonstrate that our method is accurate and computationally efficient.
