Two-dimensional boron–nitrogen–carbon monolayers with tunable direct band gaps
Bibliographic record
Abstract
The search for new candidate semiconductors with direct band gaps of ∼1.4 eV has attracted significant attention, especially among the two-dimensional (2D) materials, which have become potential candidates for next-generation optoelectronics. Herein, we systematically studied 2D B(x)/2N(x/2)C(1-x) (0 < x < 1) compounds in particular focus on the four stoichiometric B(x)/2N(x/2)C(1-x) (x = 2/3, 1/2, 2/5 and 1/3) using a recently developed global optimization method (CALYPSO) in conjunction with density functional theory. Furthermore, we examine more stoichiometries by the cluster expansion technique based on a hexagonal lattice. The results reveal that all monolayer B(x)/2N(x/2)C(1-x) stoichiometries adopt a planar honeycomb character and are dynamically stable. Remarkably, electronic structural calculations show that most of B(x)/2N(x/2)C(1-x) phases possess direct band gaps within the optical range, thereby they can potentially be used in high-efficiency conversion of solar energy to electric power, as well as in p-n junction photovoltaic modules. The present results also show that the band gaps of B(x)/2N(x/2)C(1-x) can be widely tuned within the optical range by changing the concentration of carbon, thus allowing the fast development of band gap engineered materials in optoelectronics. These new findings may enable new approaches to the design of microelectronic devices.
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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".