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Record W2768219267 · doi:10.1002/adfm.201704865

Conductive Nanocrystalline Niobium Carbide as High‐Efficiency Polysulfides Tamer for Lithium‐Sulfur Batteries

2017· article· en· W2768219267 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 Functional Materials · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Science Foundation of Anhui ProvinceNational Natural Science Foundation of China
KeywordsMaterials scienceNanocrystalline materialSulfurElectrochemistryCarbideLithium (medication)NanotechnologyElectrical conductorBattery (electricity)Chemical engineeringElectrodeComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Abstract Rational design of functional interlayer is highly significant in pursuit of high‐performance Li‐S batteries. Herein, a nanocrystalline niobium carbide (NbC) is developed via a facile and scalable autoclave technology, which is, for the first time, employed as the advanced interlayer material for Li‐S batteries. Combining the merits of strong polysulfides (PS) anchoring with high electric conductivity, the NbC‐coated membrane enables efficiently tamed PS shuttling and fast sulfur electrochemistry, achieving outstanding cyclability with negligible capacity fading rate of 0.037% cycle −1 over 1500 cycles, superb rate capability up to 5 C, high areal capacity of 3.6 mA h cm −2 under raised sulfur loading, and reliable operation even in soft‐package cells. This work offers a facile and effective method of promoting Li‐S batteries for practical application.

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 score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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