Plasma Thinning of Large Black Phosphorus Flakes
Bibliographic record
Abstract
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 layers1. 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 flakes2. 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)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".