Evidence map›Paper›PMID 42440177›Full record

ReviewNano-micro letters2026

In Situ Stress and Strain Detection in Lithium-ion Batteries: A Review of Optical, Acoustical, and Electrical Techniques.

Xing'an Luo, Yueying Zhang, Yingjian Yu, Wenbo Liu

Abstract readReview
In one paragraph

Review in Nano-micro letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Xing'an Luo *College of Physics Science and Technology, Kunming University, Kunming, 650214, People's Republic of China.
Yueying Zhang *School of Mechanical Engineering, Sichuan University, Chengdu, 610065, People's Republic of China.
Yingjian YuCollege of Physics Science and Technology, Kunming University, Kunming, 650214, People's Republic of China. yuyingjiankmu@163.com.
Wenbo LiuSchool of Mechanical Engineering, Sichuan University, Chengdu, 610065, People's Republic of China. liuwenbo_8338@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lithium-ion batteries (LIBs) have emerged as the key devices for energy storage, powering a global shift toward electrification in transportation and portable electronics. However, the relentless pursuit of higher energy densities for LIBs is fundamentally challenged by chemo-mechanical degradation, a process during which electrodes undergo volume changes during cycling, which in turn precipitates internal stress and limits battery life span. While the importance of these chemo-mechanical phenomena is widely recognized, a comprehensive review that systematically categorizes the diverse landscape of in situ detection techniques for stress and strain detection has been conspicuously absent. Herein, we address this gap by introducing a framework that organizes these techniques based on their physical modality: optical, acoustic, and electrical. Within this framework, we critically evaluate key methods, including optical laser beam position detection and multi-beam optical stress sensor, acoustic ultrasonic wave and acoustic emission, and electrical resistance strain gauge and in situ expansion meter. For each method, we detail its working principle, compare its advantages, and showcase key research applications that provide insight into improving battery performance. Finally, we outline the main challenges and future directions for in situ stress and strain detection in lithium-ion batteries.

Indexed as

In situ detectionLithium-ion batteryStrainStress evolutionVolume expansion

Identifiers

PMID42440177
PMCPMC13365110

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.