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Record W1945378646 · doi:10.1002/cssc.201500143

AlF<sub>3</sub> Surface‐Coated Li[Li<sub>0.2</sub>Ni<sub>0.17</sub>Co<sub>0.07</sub>Mn<sub>0.56</sub>]O<sub>2</sub> Nanoparticles with Superior Electrochemical Performance for Lithium‐Ion Batteries

2015· article· en· W1945378646 on OpenAlexaff
Shuwei Sun, Yanfeng Yin, Ning Wan, Qing Wu, Xiaoping Zhang, Pan Du, Ying Bai, Xia Lu

Bibliographic record

VenueChemSusChem · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMcGill University
FundersInnovation Scientists and Technicians Troop Construction Projects of Henan ProvinceNational Natural Science Foundation of China
KeywordsNanoparticleMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

Li-rich layered cathode materials have already drawn considerable attention owing to their high capacity performance for Li-ion batteries (LIBs). In this work, layered Li-rich Li[Li0.2 Ni0.17 Co0.07 Mn0.56 ]O2 nanoparticles are surface-modified with AlF3 through a facile chemical deposition method. The AlF3 surface layers have little impact on the structure of the material and act as buffers to prevent the direct contact of the electrode with the electrolyte; thus, they enhance the electrochemical performance significantly. The 3 wt % AlF3 -coated Li-rich electrode exhibits the best cycling capability and has a considerably enhanced capacity retention of 83.1 % after 50 cycles. Moreover, the rate performance and thermal stability of the 3 wt % AlF3 -coated electrode are also clearly improved. Surface analysis indicates that the AlF3 coating layer can largely suppress the undesirable growth of solid electrolyte interphase (SEI) film and, therefore, stabilizes the structure upon cycling.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
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 score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0040.005
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0010.004
Open science0.0030.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0000.002

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 head0.222
Teacher spread0.207 · 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; both teacher heads agree on what is shown here.

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

Citations57
Published2015
Admission routes1
Has abstractyes

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