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Record W3080457519 · doi:10.1002/anie.202010531

Accurate Control of Initial Coulombic Efficiency for Lithium‐rich Manganese‐based Layered Oxides by Surface Multicomponent Integration

2020· article· en· W3080457519 on OpenAlexaff
Dong Luo, Xiaokai Ding, Jianming Fan, Zuhao Zhang, Peizhi Liu, Xiaohua Yang, Junjie Guo, Shuhui Sun, Zhan Lin

Bibliographic record

VenueAngewandte Chemie International Edition · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsSpinelFaraday efficiencyLithium (medication)Materials scienceManganeseOxygenScanning electron microscopeChemical engineeringTransmission electron microscopyOxygen evolutionAnalytical Chemistry (journal)ChemistryNanotechnologyElectrochemistryElectrodeComposite materialMetallurgyChromatography

Abstract

fetched live from OpenAlex

Abstract Low initial Coulombic efficiency (ICE) is an obstacle for practical application of Li‐rich Mn‐based layered oxides (LLOs), which is closely related with the irreversible oxygen evolution owing to the overoxidized reaction of surface labile oxygen. Here we report a NH 4 F‐assisted surface multicomponent integration technology to accurately control the ICE, by which oxygen vacancies, spinel‐layered coherent structure, and F‐doping are skillfully integrated on the surface of treated LLOs microspheres. Though the regulation on the removed amount of labile oxygen by surface integrated structure, the ICE of LLOs cathodes can adjust from starting value to 100 %. X‐ray absorption spectroscopy, refined X‐ray diffraction, and scanning transmission electron microscopy show that the removed labile oxygen mainly comes from Li 2 MnO 3 ‐like structure. Even operating at a high cut‐off voltage of 5 V, the capacity retention of integrated sample at 200 mA g −1 is still larger than 98 % after 100 cycles.

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: none
Teacher disagreement score0.708
Threshold uncertainty score0.715

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 head0.024
GPT teacher head0.292
Teacher spread0.268 · 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

Citations190
Published2020
Admission routes1
Has abstractyes

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