Unraveling the dipole field in ultrathin, porous, and defective carbon nitride nanosheets for record-high piezo-photocatalytic H2O2 production
Bibliographic record
Abstract
Piezo-photocatalysis is capable of concerting mechanical vibration into chemical energy, portraying a promising alternative technology for H 2 O 2 production. However, low mechanical energy conversion efficiency and constrained surface active sites hinder its practical application. Herein, ultrathin porous carbon nitride nanosheets with controlled carbon vacancies and oxygen doping (OCN-X, where X represents the calcination temperature) are synthesized by thermal oxidation etching to achieve unprecedented piezo-photocatalytic H 2 O 2 production. The carbon vacancies and oxygen doping cause the formation of asymmetric structure of triazine unit with a strong dipole field, which creates spontaneous polarization field to speed up directional electron transfer to the nitrogen active sites for effective piezo-photocatalysis. Meanwhile, the ultrathin and porous structure formed by hot-oxygen etching enhances the mechanical energy conversion efficiency and collaboratively induces adsorbed oxygen via indirect two-electron oxygen reduction reaction (ORR) transfer pathway to effectively produce H 2 O 2 . Consequently, without any co-catalysts, the as-prepared OCN-460 displays record-high piezo-photocatalytic H 2 O 2 production rate of 19.30 mmol g −1 h −1 , far outdistancing those previously reported for piezo-photocatalysts. Furthermore, it also still maintains a notable piezo-photocatalytic activity of 2.87 mmol g −1 h −1 in the pure water system. This work offers some new insights for the future design of an effective piezo-photocatalytic H 2 O 2 production system. • Ultrathin porous g-C 3 N 4 nanosheets with carbon vacancies and oxygen doping were prepared. • Record-high piezo-photocatalytic H 2 O 2 production was achieved. • Dipole field in ultrathin, porous, and defective g-C 3 N 4 nanosheets was revealed. • Piezo-photocatalytic mechanism was revealed by state-of-the-art characterizations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".