Litcius/Paper detail

A Perspective into <i>Operando</i> Methods for Probing Catalytic Interfaces

Olivia Alley, Yue Liu, Francesca M. Toma

2025The Journal of Physical Chemistry Letters5 citationsDOIOpen Access PDF

Abstract

High Resolution Image Download MS PowerPoint Slide Rational design of electrocatalysts for (photo)electrochemical (PEC) processes like hydrogen and oxygen evolution and CO 2 reduction reactions is aided by the recent improvements in capabilities of operando measurements, where morphology, composition, and/or function are probed during active catalysis. Through operando microscopy and spectroscopy, structure, catalytic microenvironment, oxidation state, adsorbates, and products can be measured to gain a better understanding of catalyst behavior and suggest possible improvements. Visualizing evolving catalyst morphologies, surface compositions, and electrochemical behavior also helps address many fundamental research questions for a better understanding of catalytic mechanisms. Correlating morphology with chemical identity or functional behavior using a variety of innovative microscopy methods is particularly promising for guiding development of next generation catalysts, and there are also many recent examples of using AI and robotics tools to innovate and speed development. In this Perspective, advances made over the past few years in operando imaging of catalysts relevant to solar fuels will be explored, followed by an outlook on technological developments in instrumentation, sample design, and computational power that may be applied to this field.

Topics & Concepts

NanotechnologyBiochemical engineeringPerspective (graphical)CatalysisFunction (biology)Computer scienceRational designRoboticsVariety (cybernetics)Fuel cellsArtificial intelligenceSample (material)Systems engineeringMaterials scienceProcess engineeringOxygen reduction reactionPower (physics)Characterization (materials science)Atomic force microscopyEngineeringElectrocatalysts for Energy ConversionCO2 Reduction Techniques and CatalystsMachine Learning in Materials Science