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Record W4411235338 · doi:10.1002/aenm.202501405

A Novel Quantification Method for High Voltage Structural Evolution in Sodium and Lithium Layered Oxides

2025· article· en· W4411235338 on OpenAlexafffund
Libin Zhang, J. R. Dahn, Penghao Xiao, Michael Metzger

Bibliographic record

VenueAdvanced Energy Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceLithium (medication)SodiumChemical engineeringNanotechnologyMetallurgy

Abstract

fetched live from OpenAlex

Abstract High‐voltage cycling of layered alkali transition metal oxides in Li‐ and Na‐ion cells increases energy density but greatly diminishes their lifetime. One of the most common Na‐ion cathode materials, O3‐type Na[Ni 1/3 Fe 1/3 Mn 1/3 ]O 2 (NFM111), exhibits a well understood reversible octahedral to prismatic (O3↔P3) phase transition below 4.0 V, but its structural changes at higher voltage remain unclear. In this study, the structural evolution of NFM111 upon high voltage cycling is investigated by in‐situ X‐ray diffraction (XRD). It is found that the P3 phase that persists upon charging to 4 V transforms into a new octahedral/prismatic (OP) hybrid structure after just one charge to 4.3 V, with no recovery of the P3 phase in further cycles. A novel XRD‐analysis method, centered on the stacking factor “ z ”, is developed to quantify the stacking fractions in the OP hybrid structure and track their evolution during cycling, revealing structural hysteresis in the OP phase. This method is extended to the O3/O1 stacking, providing deeper understanding of high‐voltage phase transitions in Li x NiO 2 and Li x CoO 2 (x < 0.25) by revisiting previously reported in‐situ XRD data. Together, it is demonstrated that the “z‐factor” method is widely applicable to probing the high‐voltage phase transitions in both Li and Na layered oxides.

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: none
Teacher disagreement score0.492
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.011
GPT teacher head0.274
Teacher spread0.264 · 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

Citations5
Published2025
Admission routes2
Has abstractyes

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