Evidence map›Paper›PMID 40938448›Full record

ReviewNano-micro letters2025

Low-Temperature Electrolytes for Lithium-Ion Batteries: Current Challenges, Development, and Perspectives.

Yang Zhao, Limin Geng, Weijia Meng, Jiaye Ye

Abstract readReview
In one paragraph

Review in Nano-micro letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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.

Yang ZhaoXi'an Key Laboratory of Advanced Transport Power Machinery, School of Energy and Electrical Engineering, Chang'an University, Xi'an, 710064, People's Republic of China.
Limin GengXi'an Key Laboratory of Advanced Transport Power Machinery, School of Energy and Electrical Engineering, Chang'an University, Xi'an, 710064, People's Republic of China. genglimin@chd.edu.cn.
Weijia MengXi'an Key Laboratory of Advanced Transport Power Machinery, School of Energy and Electrical Engineering, Chang'an University, Xi'an, 710064, People's Republic of China. wj.meng@chd.edu.cn.
Jiaye YeSchool of Chemistry and Physics, Faculty of Science, Queensland University of Technology, Brisbane, QLD, 4001, Australia. jiaye.ye@qut.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lithium-ion batteries (LIBs), while dominant in energy storage due to high energy density and cycling stability, suffer from severe capacity decay, rate capability degradation, and lithium dendrite formation under low-temperature (LT) operation. Therefore, a more comprehensive and systematic understanding of LIB behavior at LT is urgently required. This review article comprehensively reviews recent advancements in electrolyte engineering strategies aimed at improving the low-temperature operational capabilities of LIBs. The study methodically examines critical performance-limiting mechanisms through fundamental analysis of four primary challenges: insufficient ionic conductivity under cryogenic conditions, kinetically hindered charge transfer processes, Li⁺ transport limitations across the solid-electrolyte interphase (SEI), and uncontrolled lithium dendrite growth. The work elaborates on innovative optimization approaches encompassing lithium salt molecular design with tailored dissociation characteristics, solvent matrix optimization through dielectric constant and viscosity regulation, interfacial engineering additives for constructing low-impedance SEI layers, and gel-polymer composite electrolyte systems. Notably, particular emphasis is placed on emerging machine learning-guided electrolyte formulation strategies that enable high-throughput virtual screening of constituent combinations and prediction of structure-property relationships. These artificial intelligence-assisted rational design frameworks demonstrate significant potential for accelerating the development of next-generation LT electrolytes by establishing quantitative composition-performance correlations through advanced data-driven methodologies.

Indexed as

Artificial intelligence-assisted designLithium-ion batteriesLow-temperature electrolyteSolid electrolyte interphaseSolvation structure

Identifiers

PMID40938448
PMCPMC12432001

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.