Slow Photons for Photocatalysis and Photovoltaics
Bibliographic record
Abstract
Solar light is widely recognized as one of the most valuable renewable energy sources for the future. However, the development of solar-energy technologies is severely hindered by poor energy-conversion efficiencies due to low optical-absorption coefficients and low quantum-conversion yield of current-generation materials. Huge efforts have been devoted to investigating new strategies to improve the utilization of solar energy. Different chemical and physical strategies have been used to extend the spectral range or increase the conversion efficiency of materials, leading to very promising results. However, these methods have now begun to reach their limits. What is therefore the next big concept that could efficiently be used to enhance light harvesting? Despite its discovery many years ago, with the potential for becoming a powerful tool for enhanced light harvesting, the slow-photon effect, a manifestation of light-propagation control due to photonic structures, has largely been overlooked. This review presents theoretical as well as experimental progress on this effect, revealing that the photoreactivity of materials can be dramatically enhanced by exploiting slow photons. It is predicted that successful implementation of this strategy may open a very promising avenue for a broad spectrum of light-energy-conversion technologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".