Photothermal Chemistry Based on Solar Energy: From Synergistic Effects to Practical Applications
Bibliographic record
Abstract
With the development of society, energy shortage and environmental problems have become more and more outstanding. Solar energy is a clean and sustainable energy resource, potentially driving energy conversion and environmental remediation reactions. Thus, solar-driven chemistry is an attractive way to solve the two problems. Photothermal chemistry (PTC) is developed to achieve full-spectral utilization of the solar radiation and drive chemical reactions more efficiently under relatively mild conditions. In this review, the mechanisms of PTC are summarized from the aspects of thermal and non-thermal effects, and then the interaction and synergy between these two effects are sorted out. In this paper, distinguishing and quantifying these two effects is discussed to understand PTC processes better and to design PTC catalysts more methodically. However, PTC is still a little far away from practical. Herein, several key points, which must be considered when pushing ahead with the engineering application of PTC, are proposed, along with some workable suggestions on the practical application. This review provides a unique perspective on PTC, focusing on the synergistic effects and pointing out a possible direction for practical application.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".