Meso‐Microporous Nanosheet‐Constructed 3DOM Perovskites for Remarkable Photocatalytic Hydrogen Production
Bibliographic record
Abstract
Abstract Three‐dimensionally ordered macroporous (3DOM) structures have been widely utilized to largely enhance a photocatalytic activity. However, the common nanoparticles‐constructed 3DOM photocatalysts possess numerous grain boundaries, unavoidably leading to a fast recombination of photogenerated electrons and holes. Herein, for the first time, a hierarchically two‐dimensional (2D) meso‐microporous perovskite nanosheet‐constructed 3DOM CaTiO 3 to significantly reduce the grain boundaries is designed and fabricated. Using carbon quantum dots (CQDs) as a metal‐free co‐catalyst, the 3DOM CQDs‐CaTiO 3 exhibits an outstanding photocatalytic activity for hydrogen generation of 0.13 mmol h −1 (20 mg photocatalyst) with remarkable apparent quantum efficiency (QAY) of 14.55% at 365 nm monochromatic light. This unprecedented performance is endowed by the synergy of a macro‐meso‐microporosity architecture, a large surface area, enhanced light harvesting, and improved charge carriers separation and transport. Density functional theory calculations and finite difference time‐domain simulations further reveal the mechanism behind the enhanced separation of photogenerated electrons and holes. The present work demonstrates a trial on rationally designing meso‐microporous nanosheet‐constructed 3DOM perovskites for solar driven hydrogen production.
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 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".