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Record W4407080934 · doi:10.1002/adfm.202423717

Design High‐Entropy Core‐Shell Nickel‐Rich Cobalt‐Free Cathode Material Toward High Performance Lithium Batteries

2025· article· en· W4407080934 on OpenAlexaff
Boyang Zhao, Xia Sun, Hongwei Bi, Tingzhou Yang, Haipeng Li, Dan Luo, Yongguang Zhang, Zhongwei Chen

Bibliographic record

VenueAdvanced Functional Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
FundersSuzhou Institute of Nanotechnology, Chinese Academy of SciencesNatural Science Foundation of Hebei ProvinceChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsMaterials scienceCathodeDissolutionElectrochemistryCobaltEnergy storageTransition metalChemical engineeringNickelHigh entropy alloysNanotechnologyCapacity lossElectrodeMetallurgyMicrostructureThermodynamics

Abstract

fetched live from OpenAlex

Abstract The structural instability of lithium‐based transition metal layered oxides during electrochemical cycling‐exacerbated by phenomena such as metal dissolution and phase transitions‐induces rapid capacity degradation, thus constraining their applicability in high‐energy‐density lithium batteries. While coating these materials can bolster stability, the employment of electrochemically inactive coatings may inadvertently undermine energy storage performance, presenting a significant trade‐off. In response to this challenge, an innovative core‐shell cathode architecture is presented, wherein high entropy doped LiNi 1/6 Mn 1/6 Al 1/6 Ti 1/6 Mo 1/6 Ta 1/6 O 2 serves as the shell and nickel‐rich cobalt‐free LiNi 0.89 Mn 0.11 O 2 constitutes the core, synthesized through a simple two‐step co‐precipitation methodology (designated as LHECNM). This high‐entropy shell preserves the core's electrochemical performance while effectively mitigating phase transformations and transition metal ion dissolution, thereby enhancing structural robustness. Moreover, the core‐shell configuration significantly diminishes the energy barrier for Li + diffusion, facilitating superior ion transport dynamics. Consequently, LHECNM demonstrates remarkable electrochemical performance, achieving a discharge capacity of 201.57 mAh g −1 , a commendable rate capability up to 5C, and an impressive 92% capacity retention over prolonged cycling. This investigation elucidates a promising paradigm for the design of high‐entropy cathode materials, offering profound insights for the advancement of future energy storage technologies.

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), Insufficient payload (model declined to judge)
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 score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.232
Teacher spread0.212 · 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

Citations28
Published2025
Admission routes1
Has abstractyes

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