Theoretical studies on tunable electronic structures and potential applications of two‐dimensional arsenene‐based materials
Bibliographic record
Abstract
Research efforts in the area of two‐dimensional (2D) arsenene‐based materials have been fueled up recently due to similarities in honeycomb atomic structures and differences in physical and chemical properties between arsenene and graphene. The pioneering prediction of monolayered arsenene in 2015 and successful synthesis of multilayered arsenene nanoribbons in 2016 have promoted intensive subsequent studies, especially in the theoretical aspect. Density functional theory computations not only revealed desirable fundamental band gap, structural stability, and high carrier mobility of various arsenene‐based materials but also suggested promising applications in future optoelectronic and thermoelectric devices, as well as in the quantum spin Hall devices via surface functionalization and modulation of interlayer interactions. With an aim to present a comprehensive review on the tunable electronic structures of 2D arsenene‐based materials, our focus is placed on the tailoring routes through surface functionalization to modify the electronic and optoelectronic properties of the arsenenes. An emphasis is also given to recent progress in designing topological states in arsenene monolayers. The challenges and outlooks are also laid out in aspects of experimental fabrication, device performance, and arsenene‐based chemical reactions. This article is categorized under: Structure and Mechanism > Computational Materials Science Electronic Structure Theory > Density Functional Theory
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".