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Record W3045237655 · doi:10.1016/j.jmrt.2020.06.072

High-purity few-layer graphene from plasma pyrolysis of methane as conductive additive for LiFePO4 lithium ion battery

2020· article· en· W3045237655 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Materials Research and Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsWestern University
FundersFujian Provincial Department of Science and TechnologyNational Natural Science Foundation of China
KeywordsGrapheneMaterials scienceGraphene foamLithium (medication)Chemical engineeringLithium-ion batteryGraphene oxide paperCathodeBattery (electricity)Nanotechnology

Abstract

fetched live from OpenAlex

Graphene is one of the most attractive materials because of its outstanding properties. Here we report a straightforward and environmentally friendly process, in which the few-layer graphene is continuously prepared in gas phase by one-step pyrolysis of methane by alternative-current arc plasma under substrate free and atmospheric conditions. The graphene is generated in high purity with the yield of more than 2 g/h. No further operations such as centrifugation, purification, sonication, and drying are needed. The synthesized graphene powder mainly consists of 1–3 layered flakes. The electrochemical performance of the graphene as the cathode conductive filler for LiFePO4 lithium ion batteries is investigated. The most effective electron transporting network in lithium ion batteries is obtained under 2% of graphene, together with 1% of carbon black. Moreover, the addition of graphene increases the specific capacity of the cathode and shows a good rate performance.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.029
Threshold uncertainty score0.677

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.047
GPT teacher head0.316
Teacher spread0.269 · 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