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Record W4415535462 · doi:10.1002/adma.202514126

Manganese‐Based Spinel Cathodes: A Promising Frontier for Solid‐State Lithium‐Ion Batteries

2025· article· en· W4415535462 on OpenAlexaff
Yu Dou, Shiyuan Zhou, Jeremy I. G. Dawkins, Karim Zaghib, Khalil Amine, Gui‐Liang Xu, Sixu Deng

Bibliographic record

VenueAdvanced Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsConcordia University
Fundersnot available
KeywordsSpinelCathodeCommercializationElectrochemistryElectrolyteElectrochemical energy storage

Abstract

fetched live from OpenAlex

Abstract Recently, all‐solid‐state lithium‐ion batteries (ASSLIBs), which exhibit improved safety and enhanced energy density compared to conventional commercialized lithium‐ion batteries (LIBs), thereby have garnered extensive research interest. Among the promising cathode candidates, Mn‐based spinel cathodes LiMn 2 O 4 (LMO) and LiNi 0.5 Mn 1.5 O 4 (LNMO), with the unique characteristics of low cost, structural stability, and 3D Li‐ion diffusion channels, have demonstrated excellent performance in LIBs and presented great potential in ASSLIBs applications. However, several challenges, including structural degradations, poor interfacial contact, large interfacial resistance, and Mn‐dissolution/diffusion during the electrochemical cycling, hinder their practical applications and commercialization in the ASSLIBs. Particularly, the high‐voltage LNMO cathodes suffer from the challenge of electrochemical incompatibility with most of the solid‐state electrolytes (SSEs). Herein, the spinel structure, the electrochemical behavior, and the structural degradation of the LMO/LNMO are explored. The characteristics and recent progress of the mitigating strategies to the challenges of various SSEs, including polymer‐, oxide‐, composite‐, sulfide‐, halide‐, and LiPON‐based SSEs, are introduced when paired with LMO/LNMO. Finally, the directions for future research to advance Mn‐based spinel cathodes and fulfill the requirements of the next‐generation ASSLIBs are also discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.284
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2025
Admission routes1
Has abstractyes

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