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Record W2766209405 · doi:10.1002/adma.201703764

Nanoscale Manipulation of Spinel Lithium Nickel Manganese Oxide Surface by Multisite Ti Occupation as High‐Performance Cathode

2017· article· en· W2766209405 on OpenAlexafffund
Biwei Xiao, Hanshuo Liu, Jian Liu, Qian Sun, Biqiong Wang, K. Karthikeyan, Yang Zhao, Mohammad Norouzi Banis, Yulong Liu, Ruying Li, Tsun‐Kong Sham, Gianluigi A. Botton, Mei Cai, Xueliang Sun

Bibliographic record

VenueAdvanced Materials · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMcMaster UniversityWestern University
FundersNatural Sciences and Engineering Research Council of CanadaGeneral Motors of CanadaWestern UniversityCanada Foundation for InnovationCanadian Light Source
KeywordsMaterials scienceSpinelAtomic layer depositionSurface modificationCathodeFaraday efficiencyAnnealing (glass)Chemical engineeringElectrolyteNickelManganeseOxideLithium (medication)NanotechnologyMetallurgyLayer (electronics)ElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

Abstract A novel two‐step surface modification method that includes atomic layer deposition (ALD) of TiO2 followed by post‐annealing treatment on spinel LiNi0.5Mn1.5O4 (LNMO) cathode material is developed to optimize the performance. The performance improvement can be attributed to the formation of a TiMn2O4 (TMO)‐like spinel phase resulting from the reaction of TiO2 with the surface LNMO. The Ti incorporation into the tetrahedral sites helps to combat the impedance growth that stems from continuous irreversible structural transition. The TMO‐like spinel phase also alleviates the electrolyte decomposition during electrochemical cycling. 25 ALD cycles of TiO2 growth are found to be the optimized parameter toward capacity, Coulombic efficiency, stability, and rate capability enhancement. A detailed understanding of this surface modification mechanism has been demonstrated. This work provides a new insight into the atomic‐scale surface structural modification using ALD and post‐treatment, which is of great importance for the future design of cathode materials.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.013
GPT teacher head0.268
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations156
Published2017
Admission routes2
Has abstractyes

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