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Record W2898444199 · doi:10.1002/aenm.201802105

Surface Doping to Enhance Structural Integrity and Performance of Li‐Rich Layered Oxide

2018· article· en· W2898444199 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

VenueAdvanced Energy Materials · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsWestern University
FundersArmy Research OfficeNational Natural Science Foundation of China
KeywordsMaterials scienceOxideCathodeDopingIonChemical engineeringNiobium oxideOxygenLayer (electronics)Surface layerNanotechnologyChemical physicsOptoelectronicsPhysical chemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract The Li‐rich layer‐structured oxides are regarded as one of the most promising cathode materials for their high energy density but suffer from severe problems such as capacity fading, poor rate performance, and continuous potential dropping. These issues are addressed here by surface doping of niobium (Nb) and other heavy ions in a Li‐rich Mn‐based layered oxide, Li 1.2 Mn 0.54 Ni 0.13 Co 0.13 O 2 . The doped ions are verified to be located in the Li‐layer near the oxide surface; they bind the slabs via the strong NbO bonds and “inactivate” the surface oxygen, enhancing the structural stability. The specific capacity of the modified oxide reaches 320 mAh g −1 in the initial cycle, 94.5% of which remains after 100 cycles. More importantly, the average discharge potential drops only by 136 mV in this process. The findings of this study illustrate the importance of inactivating the surface oxygen in suppressing the cation mixing in the bulk, providing an effective strategy for designing high‐performance Li‐rich 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.

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 score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.009
GPT teacher head0.263
Teacher spread0.253 · 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