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Record W2982215019 · doi:10.1002/cey2.15

The application of carbon materials in nonaqueous Na‐O<sub>2</sub> batteries

2019· article· en· W2982215019 on OpenAlexafffund
Xiaoting Lin, Qian Sun, Kieran Doyle Davis, Ruying Li, Xueliang Sun

Bibliographic record

VenueCarbon Energy · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaWestern UniversityCanada Foundation for Innovation
KeywordsAnodeBattery (electricity)Carbon fibersMaterials scienceNanotechnologyElectrochemistryEnergy densityEnergy storageProcess engineeringElectrodeEngineering physicsChemistryComposite materialEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Na‐O2 batteries are advantageous as the candidates of next‐generation electric vehicles due to their ultrahigh theoretical energy density and have attracted enormous attention recently. Tremendous efforts have been devoted to improve the Na‐O2 battery performance by designing advanced electrodes with various carbon‐based materials. Carbon materials used in Na‐O2 batteries not only function as the air electrode to provide active sites and accommodate discharge products but also as Na anode protectors against dendrite growth and chemical/electrochemical corrosion. In this review, we mainly focus on the application of various carbon‐based materials in Na‐O2 batteries and highlight their advances. The scientific understanding on the fundamental design of the material microstructure and chemistry in relation to the battery performance are summarized. Finally, perspectives on enhancing the overall battery performance based on the optimization and rational design of carbon‐based cell components are also briefly anticipated.

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: none
Teacher disagreement score0.001
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 integrity0.0010.000
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 head0.003
GPT teacher head0.172
Teacher spread0.169 · 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

Citations60
Published2019
Admission routes2
Has abstractyes

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