Post‐Illumination Photoconductivity Enables Extension of Photo‐Catalysis after Sunset
Bibliographic record
Abstract
Abstract Cloud‐cover‐induced frequent and sharp dips in sunlight as well as diminished solar flux during the evenings of peak energy demand are major challenges in solar energy harvesting. Persistent and memory‐based photocatalysts that efficiently operate under low light fluxes beyond sunset, are a potential solution to address and mitigate these challenges. This review describes examples of persistent photocatalysis systems based on charge injection into multivalent charge storage materials that allow post‐illumination discharging and charge carrier generation that increase or maintain the catalysis rate. Persistent photocatalysis in defect‐laden charge storage materials with multivalent states combined with slow charge release associated with electronic persistent photoconductivity results in giant persistent photocatalysis that can last more than an hour after the illumination is shut off. Strategies are suggested to develop persistent photocatalysis by improved charge separation and present figures of merit for evaluating persistent photocatalysis efficiency and performance. Furthermore, this could enable the use of such material systems in environmental applications such as photocatalytic coatings for the remediation of air pollution that continue to function into the night and self‐cleaning applications that continue to disinfect during the night.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".