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Record W2001555331 · doi:10.1021/am403685w

Energetics and Kinetics of Li Intercalation in Irradiated Graphene Scaffolds

2013· article· en· W2001555331 on OpenAlexaff

Bibliographic record

VenueACS Applied Materials & Interfaces · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsGrapheneEnergeticsKineticsDiffusionAdsorptionIntercalation (chemistry)Density functional theory

Abstract

fetched live from OpenAlex

In the present study, we investigate the irradiation-defects hybridized graphene scaffold as one potential building material for the anode of Li-ion batteries. Designating the Wigner V2(2) defect as a representative, we illustrate the interplay of Li atoms with the irradiation defects in graphene scaffolds. We examine the adsorption energetics and diffusion kinetics of Li in the vicinity of a Wigner V2(2) defect using density functional theory calculations. The equilibrium Li adsorption sites at the defect are identified and shown to be energetically preferable to the adsorption sites on pristine (bilayer) graphene. Meanwhile, the minimum energy paths and corresponding energy barriers for Li migration at the defect are determined and computed. We find that, while the defect is shown to exhibit certain trapping effects on Li motions on the graphene surface, it appears to facilitate the interlayer Li diffusion and enhance the charge capacity within its vicinity, because of the reduced interlayer spacing and characteristic symmetry associated with the defect. Our results provide critical assessment for the application of irradiated graphene scaffolds in Li-ion batteries.

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 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 score0.003
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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.

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

Citations25
Published2013
Admission routes1
Has abstractyes

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