Exploring new generation of characterization approaches for energy electrochemistry—from <italic>operando</italic> to artificial intelligence

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.