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Record W4309809261 · doi:10.1021/acsomega.2c04291

An Economical and Scalable Method to Synthesize Graphitic-Like Films

2022· article· en· W4309809261 on OpenAlexafffund
Minh-Hai Tran, Ian R. Booth, Bruno G. daFonseca, Peter Berrang, Jeremy E. Wulff, Alexandre G. Brolo

Bibliographic record

VenueACS Omega · 2022
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Victoria
FundersMitacs
KeywordsMaterials scienceRaman spectroscopyChemical engineeringCoatingHalogenPyrolysisReagentSubstrate (aquarium)Sublimation (psychology)NanotechnologyOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

An economical and facile method to synthesize a precursor for carbon films and materials has been developed. This precursor can be easily coated onto substrates without binder reagents and then converted into a graphitic-like structure after mild thermal treatment. This approach potentially allows the coating of glass surfaces of different shapes and forms, such as the inside of a glass tube, for instance. The precursor consists of tetrahedral halocarbyne units which randomly combine through single electron transfer with organometallic compounds to create a poly(carbyne)-like polymeric material. Advanced characterization tools reveal that the synthesized product (poly(halocarbyne) or PXC, where X indicate the presence of halogens, is composed mostly of carbon, hydrogen, and a variable percentage of residual halocarbon groups. Therefore, it possesses good solubility in organic solvents and can be coated on any complex substrate. The coated PXC material produced here was annealed under mild conditions, leading to the production of a graphitic-like film on a glass substrate. The chemical homogeneity of the carbon material of the film was confirmed by Raman spectroscopy.

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)0.001
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 score0.236
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.018
GPT teacher head0.303
Teacher spread0.285 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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