Identifying the Ground-State NP Sheet through a Global Structure Search in Two-Dimensional Space and Its Promising High-Efficiency Photovoltaic Properties
Bibliographic record
Abstract
Recently fabricated two-dimensional (2D) black phosphorene (BP) is considered to be a promising optoelectronic sheet, but its applications are hindered by the poor stability in air. Thus, it is desirable to investigate other BP-like 2D materials, which may have improved stability, while preserving the exceptional electronic properties of BP. Herein, using an efficient structure search method, we predicted a novel 2D BP-like material, namely, a honeycomb NP sheet (α-NP). Remarkably, its few-layer α-NP sheets possess not only a tunable direct bandgap under in-plane strain but also high mobility (×10 4 cm 2 V –1 s –1 ) and absorption coefficients (×10 5 cm –1 ). These advantageous characteristics endow the α-NP sheets as a very potential 2D material for efficient photovoltaic cell applications, as demonstrate by an estimated photovoltaic efficiency of ∼14%, when its thickness is at ∼1 μm. When combined with 2D MoTe 2, it can provide a type-II heterojunction solar cell with a conversion efficiency up to ∼16%. We also proposed a feasible strategy for mechanical cleavage to prepare the α-NP sheets from its bulk NP compound. Once prepared, the α-NP sheets may offer superior photovoltaic properties and facilitate the development of solar cells and optoelectronic 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".