Ice‐Assisted Synthesis of Black Phosphorus Nanosheets as a Metal‐Free Photocatalyst: 2D/2D Heterostructure for Broadband H<sub>2</sub> Evolution
Bibliographic record
Abstract
Abstract A 2D/2D heterojunction of black phosphorous (BP)/graphitic carbon nitride (g‐C3N4) is designed and synthesized for photocatalytic H2 evolution. The ice‐assisted exfoliation method developed herein for preparing BP nanosheets from bulk BP, leads to high yield of few‐layer BP nanosheets (≈6 layers on average) with large lateral size at reduced duration and power for liquid exfoliation. The combination of BP with g‐C3N4 protects BP from oxidation and contributes to enhanced activity both under λ > 420 nm and λ > 475 nm light irradiation and to long‐term stability. The H2 production rate of BP/g‐C3N4 (384.17 µmol g−1 h−1) is comparable to, and even surpasses that of the previously reported, precious metal‐loaded photocatalyst under λ > 420 nm light. The efficient charge transfer between BP and g‐C3N4 (likely due to formed NP bonds) and broadened photon absorption (supported both experimentally and theoretically) contribute to the excellent photocatalytic performance. The possible mechanisms of H2 evolution under various forms of light irradiation is unveiled. This work presents a novel, facile method to prepare 2D nanomaterials and provides a successful paradigm for the design of metal‐free photocatalysts with improved charge‐carrier dynamics for renewable energy conversion.
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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".