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Record W3157215232 · doi:10.1002/aenm.202100204

Determining the Criticality of Li‐Excess for Disordered‐Rocksalt Li‐Ion Battery Cathodes

2021· article· en· W3157215232 on OpenAlexaff
Jinhyuk Lee, Chao Wang, Rahul Malik, Yanhao Dong, Yimeng Huang, Dong‐Hwa Seo, Ju Li

Bibliographic record

VenueAdvanced Energy Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsCathodeMaterials sciencePercolation (cognitive psychology)DiffusionBattery (electricity)IonPercolation theoryThermodynamicsPhysical chemistryChemistryPhysicsConductivity

Abstract

fetched live from OpenAlex

Abstract The development of Li‐excess disordered‐rocksalt (DRX) cathodes for Li‐ion batteries and interpretation through the framework of percolation theory of Li diffusion have steered researchers to consider “Li‐excess” ( x > 1.1 in Li x TM 2− x O 2 ; TM = transition metal) as being critical to achieving high performance. It is shown that this is not necessary for Mn‐rich DRX‐cathodes demonstrated by Li 1.05 Mn 0.90 Nb 0.05 O 2 and Li 1.20 Mn 0.60 Nb 0.20 O 2 , which both deliver high capacity (>250 mAh g −1 ) regardless of their Li‐excess level. By contextualizing this finding within the broader space of DRX chemistries and confirming with first‐principles calculations, it is revealed that the percolation effect is not crucial at the nanoparticle scale. Instead, Li‐excess is necessary to lower the charging voltage (through the formation of condensed oxygen species upon oxygen oxidation) of certain DRX cathodes, which otherwise would experience difficulties in charging due to their very high TM‐redox potential. The findings reveal the dual roles of Li‐excess – modifying the cathode voltage in addition to promoting Li diffusion through percolation – that must be simultaneously considered to determine the criticality of Li‐excess for high‐capacity DRX cathodes.

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 score0.114
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 head0.014
GPT teacher head0.263
Teacher spread0.249 · 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

Citations64
Published2021
Admission routes1
Has abstractyes

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