Lithium metal anodes are of great interest for advanced high-energy density batteries such as lithiumair, lithium-sulfur and solid-state batteries, due to their low electrode potential and ultra-high theoretical capac...Lithium metal anodes are of great interest for advanced high-energy density batteries such as lithiumair, lithium-sulfur and solid-state batteries, due to their low electrode potential and ultra-high theoretical capacity. There are, however, several challenges limiting their practical applications, which include low coulombic efficiency, the uncontrollable growth of dendrites and poor rate capability. Here, a rational design of 3D structured lithium metal anodes comprising of in-situ growth of cobalt-decorated nitrogen-doped carbon nanotubes on continuous carbon nanofibers is demonstrated via electrospinning.The porous and free-standing scaffold can enhance the tolerance to stresses resulting from the intrinsic volume change during Li plating/stripping, delivering a significant boost in both charge/discharge rates and stable cycling performance. A binary Co-Li alloying phase was generated at the initial discharge process, creating more active sites for the Li nucleation and uniform deposition. Characterization and density functional theory calculations show that the conductive and uniformly distributed cobalt-decorated carbon nanotubes with hierarchical structure can effectively reduce the local current density and more easily absorb Li atoms, leading to more uniform Li nucleation during plating. The current work presents an advance on scalable and cost-effective strategies for novel electrode materials with 3D hierarchical microstructures and mechanical flexibility for lithium metal anodes.展开更多
Hydrogen plays a vital role in achieving NetZero emissions as a carbon-free energy carrier.However,its production,especially green hydrogen generated from renewable sources,is hindered by low efficiency and limited yi...Hydrogen plays a vital role in achieving NetZero emissions as a carbon-free energy carrier.However,its production,especially green hydrogen generated from renewable sources,is hindered by low efficiency and limited yield,primarily due to the performance of the catalysts used.Developing efficient catalysts typically involves extensive experimental work and trial-and-error processes.For instance,screening for effective catalysts still heavily relies on human-lab-work,a process that is time-consuming.Facing this critical challenge,machine learning(ML)emerges as a promising solution.ML,a core component of data mining and analysis that uses statistical algorithms without explicit instructions,can rationalize the design of catalysts through the use of big data,including DFT results.This approach makes a significant shift from traditional trial-and-error approaches to more computationally driven strategies,offering a more effective path to uncovering vital methodologies for catalyst development.This review aims to capture and evaluate the impact of ML algorithms that have driven progress in catalyst research over the past three years.It presents an overview of the existing ML algorithms,exploring their specific functionalities,benefits,and limitations.Besides,this review also considers prospective solutions and future directions for applying ML to enhance the efficiency of green hydrogen production,particularly through electrochemical and biological processes.展开更多
基金kindly supported by the National Natural Science Foundation of China (No. U1864213)the EPSRC Joint UK-India Clean Energy center (JUICE) (EP/P003605/1)+2 种基金the EPSRC Multi-Scale Modelling project (EP/S003053/1)the Innovate UK for Advanced Battery Lifetime Extension (ABLE) projectthe EPSRC for funding under EP/S000933/1。
文摘Lithium metal anodes are of great interest for advanced high-energy density batteries such as lithiumair, lithium-sulfur and solid-state batteries, due to their low electrode potential and ultra-high theoretical capacity. There are, however, several challenges limiting their practical applications, which include low coulombic efficiency, the uncontrollable growth of dendrites and poor rate capability. Here, a rational design of 3D structured lithium metal anodes comprising of in-situ growth of cobalt-decorated nitrogen-doped carbon nanotubes on continuous carbon nanofibers is demonstrated via electrospinning.The porous and free-standing scaffold can enhance the tolerance to stresses resulting from the intrinsic volume change during Li plating/stripping, delivering a significant boost in both charge/discharge rates and stable cycling performance. A binary Co-Li alloying phase was generated at the initial discharge process, creating more active sites for the Li nucleation and uniform deposition. Characterization and density functional theory calculations show that the conductive and uniformly distributed cobalt-decorated carbon nanotubes with hierarchical structure can effectively reduce the local current density and more easily absorb Li atoms, leading to more uniform Li nucleation during plating. The current work presents an advance on scalable and cost-effective strategies for novel electrode materials with 3D hierarchical microstructures and mechanical flexibility for lithium metal anodes.
基金support from Energy Innovation Centre,Warwick Manufacturing Group at the University of Warwick.
文摘Hydrogen plays a vital role in achieving NetZero emissions as a carbon-free energy carrier.However,its production,especially green hydrogen generated from renewable sources,is hindered by low efficiency and limited yield,primarily due to the performance of the catalysts used.Developing efficient catalysts typically involves extensive experimental work and trial-and-error processes.For instance,screening for effective catalysts still heavily relies on human-lab-work,a process that is time-consuming.Facing this critical challenge,machine learning(ML)emerges as a promising solution.ML,a core component of data mining and analysis that uses statistical algorithms without explicit instructions,can rationalize the design of catalysts through the use of big data,including DFT results.This approach makes a significant shift from traditional trial-and-error approaches to more computationally driven strategies,offering a more effective path to uncovering vital methodologies for catalyst development.This review aims to capture and evaluate the impact of ML algorithms that have driven progress in catalyst research over the past three years.It presents an overview of the existing ML algorithms,exploring their specific functionalities,benefits,and limitations.Besides,this review also considers prospective solutions and future directions for applying ML to enhance the efficiency of green hydrogen production,particularly through electrochemical and biological processes.
