As the next generation of advanced energy storage devices,aqueous Zn ions batteries(AZIBs)still face many challenges,especially dendrites on the Zn metal anode and side reactions.Although an interface modification str...As the next generation of advanced energy storage devices,aqueous Zn ions batteries(AZIBs)still face many challenges,especially dendrites on the Zn metal anode and side reactions.Although an interface modification strategy has been applied to optimize the stability of Zn metal anodes and has shown some improvement,they are still far from meeting the requirements for practical applications.There is a lack of consideration for designing a multifunctional solid electrolyte interphase(SEI)which modifies the solvation/desolvation structure of Zn ion at the interface of Zn metal anodes.Herein,we constructed an amphiphilic SEI with hydrophilic and hydrophobic properties:N,S dual-doped graphene quantum dots(GQDs).The N,S dualdoped GQDs have been synthesized using a one-step hydrothermal approach and were utilized for Zn anode surface modification.When regulating the solvation structure of the Zn ion interface by N,S dual-doped GQDs,it also promotes its desolvation kinetics,optimizes the interfacial behavior of Zn ion deposition to prohibit Zn dendrite growth,and suppresses side reactions in the Zn anode surface.The Zn|Zn symmetric cell has achieved a long cycle life of more than 800 h at 5 mA cm-2.The Zn|V2O5 battery has achieved an excellent performance of more than 80%capacity retention after 1400 cycles at 1 A g^(-1).This provides another novel and cost-effective path for the SEI design of aqueous Zn-ion batteries.展开更多
The rising demand for energy storage solutions,especially in the electric vehicle and renewable energy sectors,highlights the importance of accurately predicting battery health to enhance their longevity and reliabili...The rising demand for energy storage solutions,especially in the electric vehicle and renewable energy sectors,highlights the importance of accurately predicting battery health to enhance their longevity and reliability.This article comprehensively examines various methods used to forecast battery health,including physics-based models,empirical models,and equivalent circuit models,among others.It delves into the promise of data-driven prognostics,utilizing both conventional machine learning and cuttingedge deep neural network techniques.The advantages and limitations of hybrid models are thoroughly analyzed,with a focus on the benefits of integrating diverse data sources to improve prognostic precision.Through practical case studies,the article showcases the effectiveness and flexibility of these approaches.It also critically addresses the challenges encountered in applying battery health prognostics in realworld scenarios,such as issues of scalability,complexity,and data anomalies.Despite these challenges,the article underscores the emerging opportunities brought about by recent technological,academic,and research advancements.These include the development of digital twin models for batteries,the use of data-centric AI and standardized benchmarking,the potential integration of blockchain technology for enhanced data security and transparency,and the synergy between edge and cloud computing to boost data analysis and processing.The primary goal of this article is to enrich the understanding of current battery health prognostic techniques and to inspire further research aimed at overcoming existing hurdles and tapping into new opportunities.It concludes with a visionary perspective on future research directions and potential developments in this evolving field,encouraging both researchers and practitioners to explore innovative solutions.展开更多
Addressing real-world challenges in battery diagnostics,particularly under incomplete or inconsistent boundary conditions,has proven difficult with traditional methodologies such as first-principles and atomistic calc...Addressing real-world challenges in battery diagnostics,particularly under incomplete or inconsistent boundary conditions,has proven difficult with traditional methodologies such as first-principles and atomistic calculations.Despite advances in data assimilation techniques,the overwhelming volume and diversity of data,coupled with the lack of universally accepted models,underscore the limitations of these traditional approaches.Recently,deep learning has emerged as a highly effective tool in overcoming persistent issues in battery diagnostics by adeptly managing expansive design spaces and discerning intricate,multidimensional correlations.This approach resolves challenges previously deemed insurmountable,especially with lost,irregular,or noisy data through the design of specialized network architectures that adhere to physical invariants.However,gaps remain between academic advancements and their practical applications,including challenges in explainability and the computational costs associated with AI-driven solutions.Emerging technologies such as explainable artificial intelligence(XAI),AI for IT operations(AIOps),lifelong machine learning to mitigate catastrophic forgetting,and cloud-based digital twins open new opportunities for intelligent battery life-cycle assessment.In this perspective,we outline these challenges and opportunities,emphasizing the potential of innovative technologies to transform battery diagnostics,as demonstrated by our recent practice and the progress made in the field.This includes promising achievements in both academic and industry field demonstrations in modeling and fore-casting the dynamics of multiphysics and multiscale battery systems.These systems feature inhomogeneous cascades of scales,informed by our physical,electrochemical,observational,empirical,and/or mathematical understanding of the battery system.Through data assimilation efforts,meticulous craftsmanship,and elaborate implementations-and by considering the wealth and spatio-temporal heterogeneity of available data-such AI-based intelligent learning philosophies have great potential to achieve better accuracy,faster training,and improved generalization.展开更多
Solar-driven hydrogen production from seawater attracts great interest for its emerging role in decarbonizing global energy consumption. Given the complexity of natural seawater content, photocatalytic vapor splitting...Solar-driven hydrogen production from seawater attracts great interest for its emerging role in decarbonizing global energy consumption. Given the complexity of natural seawater content, photocatalytic vapor splitting offers a low-cost and safe solution, but with a very low solar-to-hydrogen conversion efficiency. With a focus on cutting-edge photothermal–photocatalytic device design and system integration, the recent research advances on vapor splitting from seawater, as well as industrial implementations in the past decades were reviewed. In addition, the design strategies of the key processes were reviewed, including vapor temperature and pressure control during solar thermal vapor generation from seawater, capillary-fed vaporization with salt repellent, and direct photocatalytic vapor splitting for hydrogen production. Moreover, the existing laboratory-scale and industrial-scale systems, and the integration principles and remaining challenges in the future seawater-to-hydrogen technology were discussed.展开更多
基金supported by the University of Waterloo and the Natural Science and Engineering Research Council of Canada。
文摘As the next generation of advanced energy storage devices,aqueous Zn ions batteries(AZIBs)still face many challenges,especially dendrites on the Zn metal anode and side reactions.Although an interface modification strategy has been applied to optimize the stability of Zn metal anodes and has shown some improvement,they are still far from meeting the requirements for practical applications.There is a lack of consideration for designing a multifunctional solid electrolyte interphase(SEI)which modifies the solvation/desolvation structure of Zn ion at the interface of Zn metal anodes.Herein,we constructed an amphiphilic SEI with hydrophilic and hydrophobic properties:N,S dual-doped graphene quantum dots(GQDs).The N,S dualdoped GQDs have been synthesized using a one-step hydrothermal approach and were utilized for Zn anode surface modification.When regulating the solvation structure of the Zn ion interface by N,S dual-doped GQDs,it also promotes its desolvation kinetics,optimizes the interfacial behavior of Zn ion deposition to prohibit Zn dendrite growth,and suppresses side reactions in the Zn anode surface.The Zn|Zn symmetric cell has achieved a long cycle life of more than 800 h at 5 mA cm-2.The Zn|V2O5 battery has achieved an excellent performance of more than 80%capacity retention after 1400 cycles at 1 A g^(-1).This provides another novel and cost-effective path for the SEI design of aqueous Zn-ion batteries.
基金funded by the Independent Innovation Projects of the Hubei Longzhong Laboratory(2022ZZ-24)the Central Government to Guide Local Science and Technology Development fund Projects of Hubei Province(2022BGE267).
文摘The rising demand for energy storage solutions,especially in the electric vehicle and renewable energy sectors,highlights the importance of accurately predicting battery health to enhance their longevity and reliability.This article comprehensively examines various methods used to forecast battery health,including physics-based models,empirical models,and equivalent circuit models,among others.It delves into the promise of data-driven prognostics,utilizing both conventional machine learning and cuttingedge deep neural network techniques.The advantages and limitations of hybrid models are thoroughly analyzed,with a focus on the benefits of integrating diverse data sources to improve prognostic precision.Through practical case studies,the article showcases the effectiveness and flexibility of these approaches.It also critically addresses the challenges encountered in applying battery health prognostics in realworld scenarios,such as issues of scalability,complexity,and data anomalies.Despite these challenges,the article underscores the emerging opportunities brought about by recent technological,academic,and research advancements.These include the development of digital twin models for batteries,the use of data-centric AI and standardized benchmarking,the potential integration of blockchain technology for enhanced data security and transparency,and the synergy between edge and cloud computing to boost data analysis and processing.The primary goal of this article is to enrich the understanding of current battery health prognostic techniques and to inspire further research aimed at overcoming existing hurdles and tapping into new opportunities.It concludes with a visionary perspective on future research directions and potential developments in this evolving field,encouraging both researchers and practitioners to explore innovative solutions.
文摘Addressing real-world challenges in battery diagnostics,particularly under incomplete or inconsistent boundary conditions,has proven difficult with traditional methodologies such as first-principles and atomistic calculations.Despite advances in data assimilation techniques,the overwhelming volume and diversity of data,coupled with the lack of universally accepted models,underscore the limitations of these traditional approaches.Recently,deep learning has emerged as a highly effective tool in overcoming persistent issues in battery diagnostics by adeptly managing expansive design spaces and discerning intricate,multidimensional correlations.This approach resolves challenges previously deemed insurmountable,especially with lost,irregular,or noisy data through the design of specialized network architectures that adhere to physical invariants.However,gaps remain between academic advancements and their practical applications,including challenges in explainability and the computational costs associated with AI-driven solutions.Emerging technologies such as explainable artificial intelligence(XAI),AI for IT operations(AIOps),lifelong machine learning to mitigate catastrophic forgetting,and cloud-based digital twins open new opportunities for intelligent battery life-cycle assessment.In this perspective,we outline these challenges and opportunities,emphasizing the potential of innovative technologies to transform battery diagnostics,as demonstrated by our recent practice and the progress made in the field.This includes promising achievements in both academic and industry field demonstrations in modeling and fore-casting the dynamics of multiphysics and multiscale battery systems.These systems feature inhomogeneous cascades of scales,informed by our physical,electrochemical,observational,empirical,and/or mathematical understanding of the battery system.Through data assimilation efforts,meticulous craftsmanship,and elaborate implementations-and by considering the wealth and spatio-temporal heterogeneity of available data-such AI-based intelligent learning philosophies have great potential to achieve better accuracy,faster training,and improved generalization.
基金the Department of Chemical Engineering at the University of Waterloo,Canada Research Chair Tier I-Zero-Emission Vehicles and Hydrogen Energy Systems(Grant No.950-232215)the Natural Sciences and Engineering Research Council of Canada(NSERC),Discovery Grants Program,RGPIN-2020-04149 and RGPIN-2021-02453.
文摘Solar-driven hydrogen production from seawater attracts great interest for its emerging role in decarbonizing global energy consumption. Given the complexity of natural seawater content, photocatalytic vapor splitting offers a low-cost and safe solution, but with a very low solar-to-hydrogen conversion efficiency. With a focus on cutting-edge photothermal–photocatalytic device design and system integration, the recent research advances on vapor splitting from seawater, as well as industrial implementations in the past decades were reviewed. In addition, the design strategies of the key processes were reviewed, including vapor temperature and pressure control during solar thermal vapor generation from seawater, capillary-fed vaporization with salt repellent, and direct photocatalytic vapor splitting for hydrogen production. Moreover, the existing laboratory-scale and industrial-scale systems, and the integration principles and remaining challenges in the future seawater-to-hydrogen technology were discussed.
