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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 OpenAlexaff
Wenlong Cai, Gaoran Li, Kailong Zhang, Guannan Xiao, Can Wang, Kefen Ye, Zhongwei Chen, Yongchun Zhu, Yitai Qian

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.

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 score0.002
Threshold uncertainty score0.003

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 head0.015
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

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

Citations240
Published2017
Admission routes1
Has abstractyes

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