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Record W2023806038 · doi:10.1002/asia.201200004

Superior Electrochemical Performance of Sulfur/Graphene Nanocomposite Material for High‐Capacity Lithium–Sulfur Batteries

2012· article· en· W2023806038 on OpenAlex
Bei Wang, Kefei Li, Dawei Su, Hyo‐Jun Ahn, Guoxiu Wang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueChemistry - An Asian Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsnot available
FundersAustralian Research CouncilNational Research Foundation of KoreaRyerson University
KeywordsGrapheneNanosheetNanocompositeSulfurMaterials scienceLithium (medication)ElectrochemistryNanotechnologyChemical engineeringElectrodeChemistryMetallurgy

Abstract

fetched live from OpenAlex

Sulfur/graphene nanocomposite material has been prepared by incorporating sulfur into the graphene frameworks through a melting process. Field-emission scanning electron microscope analysis shows a homogeneous distribution of sulfur in the graphene nanosheet matrix. The sulfur/graphene nanocomposite exhibits a super-high lithium-storage capacity of 1580 mA h g(-1) and a satisfactory cycling performance in lithium-sulfur cells. The enhancement of the reversible capacity and cycle life could be attributed to the flexible graphene nanosheet matrix, which acts as a conducting medium and a physical buffer to cushion the volume change of sulfur during the lithiation and delithiation process. Graphene-based nanocomposites can significantly improve the electrochemical performance of lithium-sulfur 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.

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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.201
Teacher spread0.193 · 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