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Record W2743617051 · doi:10.1088/1361-6587/aa8480

Scalable graphene production from ethanol decomposition by microwave argon plasma torch

2017· article· en· W2743617051 on OpenAlex
C. Melero, Rocío Rincón, J. Muñoz, Gaixia Zhang, Shuhui Sun, Alondra Perez, O Royuela, Cristina González-Gago, M. D. Calzada

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

VenuePlasma Physics and Controlled Fusion · 2017
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersUniversidad de Córdoba
KeywordsGrapheneMaterials scienceSubstrate (aquarium)Environmentally friendlyPlasmaProcess engineeringPlasma torchNanotechnology

Abstract

fetched live from OpenAlex

Abstract A fast, efficient and simple method is presented for the production of high quality graphene on a large scale by using an atmospheric pressure plasma-based technique. This technique allows to obtain high quality graphene in powder in just one step, without the use of neither metal catalysts and nor specific substrate during the process. Moreover, the cost for graphene production is significantly reduced since the ethanol used as carbon source can be obtained from the fermentation of agricultural industries. The process provides an additional benefit contributing to the revalorization of waste in the production of a high-value added product like graphene. Thus, this work demonstrates the features of plasma technology as a low cost, efficient, clean and environmentally friendly route for production of high-quality graphene.

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 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 score0.016
Threshold uncertainty score0.962

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 studies0.0010.000
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 head0.013
GPT teacher head0.263
Teacher spread0.250 · 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