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Record W2743583124 · doi:10.1021/acsami.7b08704

Highly Nitrogen-Doped Three-Dimensional Carbon Fibers Network with Superior Sodium Storage Capacity

2017· article· en· W2743583124 on OpenAlexafffund
Wen Lei, Weiping Xiao, Jingde Li, Gaoran Li, Zexing Wu, Cuijuan Xuan, Dan Luo, Ya‐Ping Deng, Deli Wang, Zhongwei Chen

Bibliographic record

VenueACS Applied Materials & Interfaces · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
FundersWaterloo Institute for Nanotechnology, University of WaterlooChina Scholarship CouncilUniversity of WaterlooNatural Sciences and Engineering Research Council of CanadaGovernment of Jiangxi ProvinceNational Natural Science Foundation of China
KeywordsMaterials scienceAnodeDopantChemical engineeringDopingAdsorptionCarbon fibersNitrogenHydrogen storageBacterial celluloseAbsorption (acoustics)SodiumPorosityCelluloseNanotechnologyComposite materialOrganic chemistryElectrodeOptoelectronicsPhysical chemistry

Abstract

fetched live from OpenAlex

Inspired by the excellent absorption capability of spongelike bacterial cellulose (BC), three-dimensional hierarchical porous carbon fibers doped with an ultrahigh content of N (21.2 atom %) (i.e., nitrogen-doped carbon fibers, NDCFs) were synthesized by an adsorption–swelling strategy using BC as the carbonaceous material. When used as anode materials for sodium-ion batteries, the NDCFs deliver a high reversible capacity of 86.2 mAh g –1 even after 2000 cycles at a high current density of 10.0 A g –1 . It is proposed that the excellent Na + storage performance is mainly due to the defective surface of the NDCFs created by the high content of N dopant. Density functional theory (DFT) calculations show that the defect sites created by N doping can strongly “host” Na + and therefore contribute to the enhanced storage capacity.

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 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 score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.014
GPT teacher head0.216
Teacher spread0.202 · 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 teacher head, not a consensus.

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

Citations43
Published2017
Admission routes2
Has abstractyes

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