Evidence map›Paper›PMID 42157774›Full record

ReviewAdvanced materials (Deerfield Beach, Fla.)2026

Dynamic Smart Membranes: Real-Time Perception, Self-Response, and AI-Driven Optimization for High-Safety Lithium-Based Batteries.

Botao Yuan, Yuhui He, Guowei Wang, Yunfa Dong, Jiaqi Huang, Zaiping Guo, Jiecai Han, Weidong He

Abstract readReview
In one paragraph

Review in Advanced materials (Deerfield Beach, Fla.), 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

8 authors.

Botao YuanNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin, China.ORCID https://orcid.org/0009-0003-5548-556X
Yuhui HeNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin, China.
Guowei WangNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin, China.
Yunfa DongNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin, China.
Jiaqi HuangAdvanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China.ORCID https://orcid.org/0000-0001-7394-9186
Zaiping GuoSchool of Chemical Engineering, The University of Adelaide, Adelaide, Australia.
Jiecai HanNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin, China.
Weidong HeNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin, China.ORCID https://orcid.org/0000-0001-8242-2888

Funding

China Postdoctoral Science Foundation BX20230477Chongqing Technology Innovation and Application Development Special Key Project CSTB2023TIAD-KPX0010Chongqing Technology Innovation and Application Development Special Major Project CSTB2023TIAD-STX0033National Natural Science Foundation of China 52402238Natural Science Foundation of Chongqing, China CSTB2022NSCQ-MSX0246 CSTB2022NSCQ-MSX1572Science Foundation of National Key Laboratory of Science and Technology on Advanced Composites in Special Environments
6 · The paper itself

Abstract

Lithium-based batteries are fundamental to modern energy storage systems, yet their safety remains a critical challenge due to risks such as thermal runaway, dendrite-induced short circuits, and interfacial degradation. Conventional composite membranes, including composite separators and solid electrolytes, have been developed to improve thermal stability, mechanical strength, and ionic conductivity. However, these static materials lack the ability to dynamically respond to real-time operational stresses such as local temperature spikes, mechanical deformation, or evolving electrochemical conditions, leading to persistent safety limitations. To overcome these issues, smart membranes have emerged as a transformative solution, integrating real-time perception, self-responsive mechanisms, and artificial intelligence (AI) to enhance battery safety proactively. This review systematically addresses the three primary safety issues of conventional composite membranes, including thermal instability, mechanical failure, and ion transport limitations, through detailing how smart membranes leverage embedded sensors for continuous perception, employ self-protection and self-healing functionalities to mitigate risks, and utilize AI for material optimization and failure prediction. Furthermore, we discuss the industrial viability of smart membranes, highlighting challenges related to cost, scalability, and integration, while outlining future directions including multifunctional coupling, wireless sensing, and advanced material designs. Smart membranes represent a transformative advance toward autonomous, safe, and durable next-generation lithium batteries.

Indexed as

dynamic capabilitylithium‐based batteriessafetysmart membranes

Identifiers

PMID42157774
PMCPMC13310114

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.