探讨面向能源电化学的新一代表征方法——从工况表征到人工智能

Exploring new generation of characterization approaches for energy electrochemistry-from operando to artificial intelligence

电化学科学与技术在新能源等领域扮演着日益重要的角色且面临巨大挑战,传统的原位和非原位表征方法难以满足具有大流量、高密度且动态变化的传荷、传能和传质的大型电化学能源器件的需求,无法全面获取有关各类界面结构与过程的能(量)–时(间)–空(间)相关信息.本文在讨论谱学电化学发展脉络和分析非原位、原位和工况三类表征的本质性区别的基础上,展望面向新型电化学能源器件/系统的新一代工况表征实验和理论方法,建议着重发展可实时检测关键反应中间物/产物的谱学和传感技术,全面跟踪各个界面和体相的动态变化特别是它们之间的耦合与串扰,进而解析整个系统内相互关联的复杂机制;强调能源器件工况表征技术进一步与兴起的人工智能技术优势互补,将实现各类工况表征和检测模块与器件的工况调控部分的系统结合.通过人工智能辅助,快速获取并分析工况表征数据后及时反馈至器件/系统的控制中心后做出调控工作参数决定,实现器件在工况下的检测–解析–控制的全闭环模式.基于人工智能驱动,将原本数个分立且耗时低效的操作模块有机地合为一体,不仅可优化系统和获取大量数据,而且有望指导生成全新的电化学能源器件,发展为未来研究范式,为发展能源电化学、化学测量学和界面科学乃至建立系统电化学等新方向提供新途径.

Electrochemical(EC)technology plays an increasingly important role in energy and related fields,which presents significant challenges as well as opportunities for the fundamental research of electrochemistry.Electrochemical devices such as those for electrolysis(e.g.,hydrogen production,chlor-alkali,aluminum),fuel cells,power batteries,energy storage batteries,often require a high working current density(such as larger than 1 A cm−2)and a high level of overpotential far from the electrochemical equilibrium(e.g.,±0.7 V).The operation conditions of such energy-conversion devices are complex and rapidly changing(e.g.,the fluctuation of solar energy and wind energy at the supply end and the start and brake of electric vehicles at the consumption end of energy),and thus put extremely high requirements for the conversion efficiency,safety,and lifespan properties of devices.It is unprecedently challenging to identify efficiency,failure and safety mechanism for EC energy devices,of which one key issue is to characterize various interface structures and processes of EC devices with large-flow,high-density,and dynamically-changing charge,energy,and mass transfers.The commonly used in-situ and ex-situ characterization techniques cannot fully obtain energy,time,and space information,and it is difficult for them to characterize the key interfacial processes under real working conditions for elucidating their complicate mechanism.It is therefore imperative to develop a new generation of characterization methods and theories for energy electrochemistry.The main direction is to establish operando characterization techniques for real devices,and form a complete set of measurement system integrating the three types of ex-situ,in-situ and operando techniques for systematically detecting key intermediates,products,all components and interfaces as well as their crosstalk and coupling in real EC energy devices,thus to facilitate a comprehensive understanding of the interconnected complicate mechanism to further guide optimization and even innovation of related techniques and devices.Based on a close combination with artificial intelligence(AI),operando measurement with various spectroscopies and sensors is expected to reach each interfaces and bulks and their dynamic changes in energy devices.More importantly,it is proposed to further integrate various kinds of operando measurement modules with real-time regulation of energy devices,by which the operando data can be immediately analyzed via AI,and control decisions are made accordingly and rapidly feed back to the regulation center,so as to realize an AI-driven loop of Operando–Measurement–Analysis–Control(AI-LOMAC)of the whole real device.Integrating the three key discrete,time-consuming,and inefficient operating modules into one module is highly challenging but promising to develop into a new research paradigm,and provide an innovative pathway for the development of energy electrochemistry,interface science,and related fields,and even igniting new directions such as systems electrochemistry.