2D Arsenene and Arsenic Materials: Fundamental Properties, Preparation, and Applications
Bibliographic record
Abstract
As emerging 2D materials, arsenene and arsenic materials have attracted rising interest in the past few years. The diverse crystalline phases, exotic electrical characteristics, and widespread applications of 2D arsenene and arsenic bring them great research value and utilization potential. Herein, the recent progress of 2D arsenene and arsenic is reviewed in terms of fundamental properties, preparation, and applications. The fundamental properties of 2D arsenene and arsenic, including the crystal phases, environmental stability, and electrical structure, from theoretical to experimental reports are first summarized. Then, the experimental processes for preparing 2D arsenene and arsenic, along with their respective advantages and disadvantages, are introduced including epitaxial growth, mechanical exfoliation, and liquid-phase exfoliation. Moreover, applications of 2D arsenene and arsenic are discussed, suggesting a wide range of applications of 2D arsenene and arsenic in field-effect transistors, sensors, catalysts, biological applications, and so on. Finally, some perspectives about the challenges and opportunities of promising 2D arsenene and arsenic are provided. This review provides a helpful guidance and stimulates more focus on future explorations and developments of 2D arsenene and arsenic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".