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Record W3024884602 · doi:10.1149/ma2020-0110820mtgabs

Plasma Thinning of Large Black Phosphorus Flakes

2020· article· en· W3024884602 on OpenAlexaff
Valérie Lefebvre, Léonard Schué, Christophe Clément, Richard Martel

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsPolytechnique MontréalUniversité de Montréal
Fundersnot available
KeywordsPhosphoreneMonolayerMaterials scienceBand gapRaman spectroscopyPhosphorusBlack phosphorusPlasmaChemical engineeringOptoelectronicsNanotechnologyOpticsMetallurgy

Abstract

fetched live from OpenAlex

The growing interest over the 2D materials has led to the discovery of exfoliated black phosphorus (bP), a semiconductor with a thickness-dependent bandgap. The direct bandgap energy varies from 0.3 eV for bulk materials to 1.9 eV for the monolayer of black phosphorus. The fabrication of thin layers is, however, difficult because the layers photo-oxidize in air into phosphoric acid with kinetic that gets faster for thinner layers 1 . Inspired by a new method to produce thin layers of black phosphorus using oxygen plasma, we work to optimise the process for making large few-layers bP flakes 2 . Using ICP-RIE oxygen plasma, exfoliated flakes of black phosphorus were etched in a controlled way and Raman spectroscopy was used to evaluate the quality of the flakes after plasma treatments. The results show no sign of high-degradation of the bP crystal structure after the plasma treatment. Here we show that the method can be adapted to produce high quality thin flakes of black phosphorus towards incorporation into electronic and optoelectronic applications. 1 Favron, A., Gaufrès, E., Fossard, F. et al. Photooxidation and quantum confinement effects in exfoliated black phosphorus. Nature Mater 14, 826–832 (2015) 2 Pei, J., Gai, X., Yang, J. et al. Producing air-stable monolayers of phosphorene and their defect engineering. Nat Commun 7, 10450 (2016)

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 score0.036
Threshold uncertainty score0.869

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)0.0000.001

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 head0.022
GPT teacher head0.257
Teacher spread0.235 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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