Probing Buried Interfaces in Batteries: Toward Operando Visibility and Quantitative Diagnosis

TL;DR

This paper explores how to better observe and understand hidden battery interfaces to improve battery performance and stability.

Contribution

The paper introduces a roadmap for transforming interface visibility into quantitative analysis using multimodal measurements and AI.

Findings

Buried interfaces strongly influence battery performance but are hard to probe directly.

Advances in operando techniques now allow dynamic and chemically specific interface observation.

Integration of multimodal data with models and AI can enable quantitative diagnosis of interfaces.

Abstract

The evolution of buried interfaces, the hidden junctions
where
distinct phases exchange charge, mass, and mechanical response under
nonequilibrium conditions, strongly influences the performance and
stability of functional devices such as batteries, but they remain
difficult to probe directly. This perspective summarizes the types
of buried interfaces that form within battery electrodes and their
electrochemical function in the device, and it discusses how advances
in operando probes, cell architectures, and multimodal
and correlative strategies have enabled dynamic and chemically specific
visibility of their evolution. Despite this progress, operando signals remain challenging to interpret because they are affected
by, for example, beam damage-induced changes, variations in operando cell geometry, and intrinsic sample-to-sample differences,
which together limit quantitative insight. Building on these considerations,
the perspective examines how operando visibility
can be transformed into quantitative diagnosis by integrating multimodal
measurements with physically informed interface models and data-driven
analysis. The final section outlines a roadmap for reproducible and
quantitative operando analysis, centered on standardized
cell architectures, long-term autonomous measurements, and artificial
intelligence approaches that incorporate physical constraints. In
summary, these developments define a pathway from operando visibility to quantitative diagnosis and provide a foundation for
advancing interface characterization and quantitative analysis in
batteries and related energy materials.

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