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Record W4409683995 · doi:10.1021/acsnano.5c01904

Solid-Solution Phase Transition Induced by Surface Electrochemico–Mechanical Interactions for High-Voltage Sodium-Layered Oxide Cathodes

2025· article· en· W4409683995 on OpenAlexaff
Zibin Liang, Chuying Ouyang, Longze Li, Sulan Cheng, Yuhao Wang, Min Lin, Liangjie Xu, Bo Xu, Xinxin Zhang, Bingkun Guo, Xiaonan Luo, Kai Wu

Bibliographic record

VenueACS Nano · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsL'Alliance Boviteq
FundersNational Key Research and Development Program of ChinaNatural Science Foundation of Fujian Province
KeywordsMaterials scienceOxideCathodePhase transitionChemical engineeringSodiumNanotechnologyChemistryPhysical chemistryThermodynamicsMetallurgy

Abstract

fetched live from OpenAlex

The phase transition behaviors of sodium-layered oxide (SLO) cathodes play an important role in their electrochemical performances at high voltages. Specifically, SLOs experience a phase transition from the P to OP intergrowth phase with Na-deficient O layers, leading to sluggish Na extraction/insertion kinetics, severe strain formation, and high reactive activity with electrolytes. In addition to the normally used phase engineering strategies such as bulk doping, we demonstrate here that a Mn-gradient surface layer can significantly tune the bulk phase transition from the OP intergrowth to O3 solid-solution transition, evidenced by in situ XRD and Cryo-STEM analyses. The Mn-rich surface has asynchronous Na extraction properties compared to the bulk at high voltages, suppressing the nucleation and growth of the OP intergrowth phase as the generated stress cannot be well released while facilitating the formation of a stressless O3 solid-solution phase. Benefiting from the O3 solid-solution phase change behaviors, the SLO with the Mn-rich surface shows much improved electrochemical performances at high voltages up to 4.3 V in terms of energy density, rate performance, energy conversion efficiency, and cycling stability.

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 categoriesnone
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 score0.384
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.015
GPT teacher head0.311
Teacher spread0.296 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2025
Admission routes1
Has abstractyes

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