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Record W4385803661 · doi:10.1149/1945-7111/acf000

Hydrothermally Deposited Carbon Coatings for Li-Ion Battery Active Materials

2023· article· en· W4385803661 on OpenAlexafffund
Jun Wang, Yidan Cao, M. N. Obrovac

Bibliographic record

VenueJournal of The Electrochemical Society · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceFaraday efficiencyCarbon fibersGraphiteLithium (medication)PorosityChemical engineeringCoatingBattery (electricity)Hydrothermal circulationIonLithium-ion batterySpecific surface areaElectrochemistryNanotechnologyComposite materialElectrodeChemistryComposite numberCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Hydrothermally deposited carbon coatings were evaluated for Li-ion battery materials. The applied coatings were found to be highly uniform and porous, with a density of 1.89 g ml−1 and a surface area of 370 m2 g−1. In lithium cells, the hydrothermally deposited carbon was found to have a reversible capacity of 230 mAh g−1 when cycled between 7 mV and 0.9 V. Carbon coatings applied hydrothermally onto graphite particles were found to be effective at reducing surface reactions and increasing coulombic efficiency during cycling in lithium cells. However, because of the high surface area and high porosity of the hydrothermal coating it likely has limited applicability in commercial Li-ion cells.

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)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

Explore more

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