Elucidating the Oxidation Process and Enhanced Stability of Black Phosphorus through NTCDA Passivation: A Molecular Dynamics Study
Bibliographic record
Abstract
Abstract Understanding the oxidation mechanisms of black phosphorus (BP) at the atomic scale is essential for developing effective passivation strategies to enhance its stability in ambient conditions. To explore this, the effects of O2 and H2O molecules on BP layers are elucidated using reactive force field (ReaxFF) molecular dynamics simulations at constant concentrations of molecules and room temperature. As a potential solution, the passivation efficacy of 1,4,5,8‐naphthalenetetracarboxylic dianhydride (NTCDA) is evaluated. The initial oxidation processes are analyzed through atomic structural changes, charge dynamics, and radial distribution functions. Moreover, the thickness of the oxidized BP layers is quantitatively determined. Results show that elevated O2 concentrations significantly accelerate oxidation and increase the thickness of the oxidized layers, while H2O has a weaker influence. The interaction between O⁻ and H⁺ ions in H2O reduces its interaction with BP, but O2 molecules cause H2O to become negatively charged, allowing it to interact with P⁺ ions. Importantly, passivating BP with NTCDA effectively mitigates oxidation, creating a protective layer that repels O2 molecules. Ultimately, this study reveals the initial oxidation and passivation processes of BP layers, offering crucial theoretical insights to guide experimental methods and practical applications in semiconductor 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".