Nanocluster formation of a metallic platinum (Pt) coating, on manganese oxide inorganic membranes impregnated with multiwall carbon nanotubes (K-OMS-2/MWCNTs), applied by reactive spray deposition technology (RSDT) is...Nanocluster formation of a metallic platinum (Pt) coating, on manganese oxide inorganic membranes impregnated with multiwall carbon nanotubes (K-OMS-2/MWCNTs), applied by reactive spray deposition technology (RSDT) is discussed. RSDT applies thin films of Pt nanoclusters on the substrate;the thickness of the film can be easily controlled. The K-OMS-2/MWCNTs fibers were enclosed by the thin film of Pt. X-ray diffraction (XRD), scanning electron microscopy/X-ray energy dispersive spectroscopy (SEM/XEDS), focus ion beam/scanning electron microscopy (FIB/SEM), transmission electron microscopy (TEM), and X-ray 3D micro-tomography (MicroXCT) which have been used to characterize the resultant Pt/K-OMS-2/MWCNTs membrane. The non-destructive characterization technique (MicroXCT) resolves the Pt layer on the upper layer of the composite membrane and also shows that the membrane is composed of sheets superimposed into stacks. The nanostructured coating on the composite membrane material has been evaluated for carbon monoxide (CO) oxidation. The functionalized Pt/K-OMS-2/MWCNTs membranes show excellent conversion (100%) of CO to CO2 at a lower temperature 200℃ compared to the uncoated K-OMS-2/MWCNTs. Moreover, the Pt/K-OMS-2/MWCNTs membranes show outstanding stability, of more than 4 days, for CO oxidation at 200℃.展开更多
Lithium-ion batteries(LIBs)are the most important electrochemical energy storage devices due to their high energy den-sity,long cycle life,and low cost.During the past decades,many review papers outlining the advantag...Lithium-ion batteries(LIBs)are the most important electrochemical energy storage devices due to their high energy den-sity,long cycle life,and low cost.During the past decades,many review papers outlining the advantages of state-of-the-art LIBs have been published,and extensive efforts have been devoted to improving their specific energy density and cycle life performance.These papers are primarily focused on the design and development of various advanced cathode and anode electrode materials,with less attention given to the other important components of the battery.The“nonelectroconductive”components are of equal importance to electrode active materials and can significantly affect the performance of LIBs.They could directly impact the capacity,safety,charging time,and cycle life of batteries and thus affect their commercial application.This review summarizes the recent progress in the development of nonaqueous electrolytes,binders,and separa-tors for LIBs and discusses their impact on the battery performance.In addition,the challenges and perspectives for future development of LIBs are discussed,and new avenues for state-of-the-art LIBs to reach their full potential for a wide range of practical applications are outlined.展开更多
Polymer electrolyte fuel cells will be an essential technology of the emerging hydrogen economy.However,optimizing their cost and performance necessitates understanding of how different parameters affect their operati...Polymer electrolyte fuel cells will be an essential technology of the emerging hydrogen economy.However,optimizing their cost and performance necessitates understanding of how different parameters affect their operation.This optimization problem involves numerous interrelated design and operational parameters.However,developing the required understanding through experimental studies alone would be inefficient.Physical modelling is a much-needed complement to experiment but is constrained by simplifying assumptions that diminish the models’predictive capabilities.As a supplement to experiment and physical modelling,we employ a data-based assessment that leverages machine learning techniques to support and enhance decisionmaking.We first evaluate the predictive accuracy of various machine learning models,including artificial neural networks,to predict the polarization behavior of polymer electrolyte fuel cells,harnessing an extensive experimental dataset.We then apply explainable artificial intelligence techniques,including Gini feature importance and Shapley additive explanations value analyses,to understand how these models incorporate data into the prediction process.Probabilistic analyses can help identify relationships between predictions and feature values.We demonstrate that insights derived from Shapley additive explanations value analysis are consistent with literature data on the thermodynamics and kinetics of relevant electrochemical reaction and transport processes.Our study highlights the potential of interpretable and explainable tools to offer a holistic analysis of the impacts of various interrelated operational and design parameters on the performance of the fuel cell.In the future,such explainable tools could help identify gaps in experimental data and pinpoint research priorities.展开更多
Artificial Intelligence(AI)has revolutionized technological development globally,delivering relatively more accurate and reliable solutions to critical challenges across various research domains.This impact is particu...Artificial Intelligence(AI)has revolutionized technological development globally,delivering relatively more accurate and reliable solutions to critical challenges across various research domains.This impact is particularly notable within the field of materials science and engineering,where artificial intelligence has catalyzed the discovery of new materials,enhanced design simulations,influenced process controls,and facilitated operational analysis and predictions of material properties and behaviors.Consequently,these advancements have stream-lined the synthesis,simulation,and processing procedures,leading to material optimization for diverse appli-cations.A key area of interest within materials science is the development of hydrogen-based electrochemical systems,such as fuel cells and electrolyzers,as clean energy solutions,known for their promising high energy density and zero-emission operations.While artificial intelligence shows great potential in studying both fuel cells and electrolyzers,existing literature often separates them,with a clear gap in comprehensive studies on electrolyzers despite their similarities.This review aims to bridge that gap by providing an integrated overview of artificial intelligence’s role in both technologies.This review begins by explaining the fundamental concepts of artificial intelligence and introducing commonly used artificial intelligence-based algorithms in a simplified and clearly comprehensible way,establishing a foundational knowledge base for further discussion.Subsequently,it explores the role of artificial intelligence in materials science,highlighting the critical applications and drawing on examples from recent literature to build on the discussion.The paper then examines how artificial intelligence has propelled significant advancements in studying various types of fuel cells and electrolyzers,specifically emphasizing proton exchange membrane(PEM)based systems.It thoroughly explores the artificial intelligence tools and techniques for characterizing,manufacturing,testing,analyzing,and optimizing these systems.Additionally,the review critically evaluates the current research landscape,pinpointing progress and prevailing challenges.Through this thorough analysis,the review underscores the fundamental role of artificial intelligence in advancing the generation and utilization of clean energy,illustrating its transformative potential in this area of research.展开更多
文摘Nanocluster formation of a metallic platinum (Pt) coating, on manganese oxide inorganic membranes impregnated with multiwall carbon nanotubes (K-OMS-2/MWCNTs), applied by reactive spray deposition technology (RSDT) is discussed. RSDT applies thin films of Pt nanoclusters on the substrate;the thickness of the film can be easily controlled. The K-OMS-2/MWCNTs fibers were enclosed by the thin film of Pt. X-ray diffraction (XRD), scanning electron microscopy/X-ray energy dispersive spectroscopy (SEM/XEDS), focus ion beam/scanning electron microscopy (FIB/SEM), transmission electron microscopy (TEM), and X-ray 3D micro-tomography (MicroXCT) which have been used to characterize the resultant Pt/K-OMS-2/MWCNTs membrane. The non-destructive characterization technique (MicroXCT) resolves the Pt layer on the upper layer of the composite membrane and also shows that the membrane is composed of sheets superimposed into stacks. The nanostructured coating on the composite membrane material has been evaluated for carbon monoxide (CO) oxidation. The functionalized Pt/K-OMS-2/MWCNTs membranes show excellent conversion (100%) of CO to CO2 at a lower temperature 200℃ compared to the uncoated K-OMS-2/MWCNTs. Moreover, the Pt/K-OMS-2/MWCNTs membranes show outstanding stability, of more than 4 days, for CO oxidation at 200℃.
基金Mexichem Fluor,Inc.(4161950),General Manager Growth,Fluor Business Groups,Boston,MA.The authors would also like to express their gratitude to Dr.Haoran Yu and Mr.Piyush Jibhakate for the useful discussions related to the content of this review paper.
文摘Lithium-ion batteries(LIBs)are the most important electrochemical energy storage devices due to their high energy den-sity,long cycle life,and low cost.During the past decades,many review papers outlining the advantages of state-of-the-art LIBs have been published,and extensive efforts have been devoted to improving their specific energy density and cycle life performance.These papers are primarily focused on the design and development of various advanced cathode and anode electrode materials,with less attention given to the other important components of the battery.The“nonelectroconductive”components are of equal importance to electrode active materials and can significantly affect the performance of LIBs.They could directly impact the capacity,safety,charging time,and cycle life of batteries and thus affect their commercial application.This review summarizes the recent progress in the development of nonaqueous electrolytes,binders,and separa-tors for LIBs and discusses their impact on the battery performance.In addition,the challenges and perspectives for future development of LIBs are discussed,and new avenues for state-of-the-art LIBs to reach their full potential for a wide range of practical applications are outlined.
基金partial financial support from the European Union’s Horizon Europe Research and Innovation programme,project DECODE under Grant Agreement No 101135537the grant for research exchange provided by Center for Advanced Simulation and analytics(CASA),Simulation and Data Science Lab for Energy Materials(SDL-EM)at the Forschungszentrum Jülich GmbH,taken place during Summer 2024.
文摘Polymer electrolyte fuel cells will be an essential technology of the emerging hydrogen economy.However,optimizing their cost and performance necessitates understanding of how different parameters affect their operation.This optimization problem involves numerous interrelated design and operational parameters.However,developing the required understanding through experimental studies alone would be inefficient.Physical modelling is a much-needed complement to experiment but is constrained by simplifying assumptions that diminish the models’predictive capabilities.As a supplement to experiment and physical modelling,we employ a data-based assessment that leverages machine learning techniques to support and enhance decisionmaking.We first evaluate the predictive accuracy of various machine learning models,including artificial neural networks,to predict the polarization behavior of polymer electrolyte fuel cells,harnessing an extensive experimental dataset.We then apply explainable artificial intelligence techniques,including Gini feature importance and Shapley additive explanations value analyses,to understand how these models incorporate data into the prediction process.Probabilistic analyses can help identify relationships between predictions and feature values.We demonstrate that insights derived from Shapley additive explanations value analysis are consistent with literature data on the thermodynamics and kinetics of relevant electrochemical reaction and transport processes.Our study highlights the potential of interpretable and explainable tools to offer a holistic analysis of the impacts of various interrelated operational and design parameters on the performance of the fuel cell.In the future,such explainable tools could help identify gaps in experimental data and pinpoint research priorities.
基金supported by the U.S.National Science Foundation(NSF)under The Faculty Early Career Development(CAREER)Program(Grant#2046060).
文摘Artificial Intelligence(AI)has revolutionized technological development globally,delivering relatively more accurate and reliable solutions to critical challenges across various research domains.This impact is particularly notable within the field of materials science and engineering,where artificial intelligence has catalyzed the discovery of new materials,enhanced design simulations,influenced process controls,and facilitated operational analysis and predictions of material properties and behaviors.Consequently,these advancements have stream-lined the synthesis,simulation,and processing procedures,leading to material optimization for diverse appli-cations.A key area of interest within materials science is the development of hydrogen-based electrochemical systems,such as fuel cells and electrolyzers,as clean energy solutions,known for their promising high energy density and zero-emission operations.While artificial intelligence shows great potential in studying both fuel cells and electrolyzers,existing literature often separates them,with a clear gap in comprehensive studies on electrolyzers despite their similarities.This review aims to bridge that gap by providing an integrated overview of artificial intelligence’s role in both technologies.This review begins by explaining the fundamental concepts of artificial intelligence and introducing commonly used artificial intelligence-based algorithms in a simplified and clearly comprehensible way,establishing a foundational knowledge base for further discussion.Subsequently,it explores the role of artificial intelligence in materials science,highlighting the critical applications and drawing on examples from recent literature to build on the discussion.The paper then examines how artificial intelligence has propelled significant advancements in studying various types of fuel cells and electrolyzers,specifically emphasizing proton exchange membrane(PEM)based systems.It thoroughly explores the artificial intelligence tools and techniques for characterizing,manufacturing,testing,analyzing,and optimizing these systems.Additionally,the review critically evaluates the current research landscape,pinpointing progress and prevailing challenges.Through this thorough analysis,the review underscores the fundamental role of artificial intelligence in advancing the generation and utilization of clean energy,illustrating its transformative potential in this area of research.
