2D Arsenene and Arsenic Materials: Fundamental Properties, Preparation, and Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it