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Record W2790375166 · doi:10.1007/s40243-018-0110-3

A novel elevated temperature pre-treatment for electrochemical capacity enhancement of graphene nanoflake-based anodes

2018· article· en· W2790375166 on OpenAlexafffund
Sandeep Bhattacharya, A.T. Alpas

Bibliographic record

VenueMaterials for Renewable and Sustainable Energy · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGrapheneMaterials scienceAnodeRaman spectroscopyElectrochemistryElectrolyteChemical engineeringCyclingNanotechnologyElectrodeChemistry

Abstract

fetched live from OpenAlex

Improvements in the specific capacity of graphene nanoflake-based anodes cycled vs. Li/Li + were investigated at two cycling temperatures of 25 and 50 °C. When cycled at 25 °C, the first cycle specific capacity of the graphene nanoflakes was 636 mA h g −1 , whereas cycling at 50 °C led to a 35% increase in the specific capacity to 856 mA h g −1 . High resolution SEM investigations revealed that the increased capacity of graphene cycled at 50 °C was accompanied by the formation of a uniform and continuous solid electrolyte interface (SEI). The strain generated in graphene anodes was reduced from 0.75% at 25 °C, to 0.12% at 50 °C, as determined by in situ Raman spectroscopy. This was attributed to the reduction in the extent of solvent co-intercalation at 50 °C. The results suggest that pre-cycling of Li-ion cell anodes containing graphene flakes at elevated temperatures would increase their specific capacity at the same charging/discharging current densities as that used for cycling at room temperature.

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 score0.103
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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