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Record W3157823467 · doi:10.1515/ntrev-2021-0020

Improvement of long-term cycling performance of high-nickel cathode materials by ZnO coating

2021· article· en· W3157823467 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNanotechnology Reviews · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsWestern University
Fundersnot available
KeywordsMaterials scienceElectrolyteCathodeCoatingCalcinationChemical engineeringElectrochemistryNickelLithium (medication)ElectrodeComposite materialMetallurgyCatalysisChemistry

Abstract

fetched live from OpenAlex

Abstract The high-nickel cathode material of LiNi 0.8 Co 0.15 Al 0.05 O 2 (LNCA) has a prospective application for lithium-ion batteries due to the high capacity and low cost. However, the side reaction between the electrolyte and the electrode seriously affects the cycling stability of lithium-ion batteries. In this work, Ni 2+ preoxidation and the optimization of calcination temperature were carried out to reduce the cation mixing of LNCA, and solid-phase Al-doping improved the uniformity of element distribution and the orderliness of the layered structure. In addition, the surface of LNCA was homogeneously modified with ZnO coating by a facile wet-chemical route. Compared to the pristine LNCA, the optimized ZnO-coated LNCA showed excellent electrochemical performance with the first discharge-specific capacity of 187.5 mA h g −1 , and the capacity retention of 91.3% at 0.2C after 100 cycles. The experiment demonstrated that the improved electrochemical performance of ZnO-coated LNCA is assigned to the surface coating of ZnO which protects LNCA from being corroded by the electrolyte during 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.

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 score0.048
Threshold uncertainty score0.913

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 head0.016
GPT teacher head0.263
Teacher spread0.247 · 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