Wet Chemistry Vitrification and Metal‐to‐Semiconductor Transition of 2D Gray Arsenene Nanoflakes
Bibliographic record
Abstract
Abstract To manipulate the electrical and optical properties of 2D materials via engineering their phases and crystallinity is of great significance for the construction of nanodevices with versatile functions. Herein, the controllable transformation of semimetallic gray arsenene nanoflakes into semiconducting vitreous arsenene nanoflakes via a wet chemistry vitrification method is reported. Experimental studies and theoretical simulations reveal that the vitrification of gray arsenene nanoflakes is attributed to the consumption of arsenic atoms by aqueous HF via the trigger of dissolved oxygen, resulting in a significant variation of band structure rendered by the formation of atomic structure disorderliness and arsenic atom defects/vacancies. Unlike the semimetallic features of pristine gray arsenene nanoflakes, the as‐prepared vitreous arsenene nanoflakes exhibit a strong photoluminescence peak centered at 635 nm corresponding to an optical band gap of 1.95 eV, and the field‐effect transistors based on vitreous arsenene nanoflakes also exhibit definitely p‐type semiconducting characteristics with a carrier mobility of ≈159.1 cm2 V−1 s−1. The wet chemistry induced vitrification of gray arsenene nanoflakes presents an efficient strategy to regulate the electrical and optical properties of arsenene nanoflakes, providing new insights for the interface and band structure engineering of 2D nanomaterials.
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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) | 0.001 | 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".