Solar light harvesting by energy transfer: from ecology to coherence
Bibliographic record
Abstract
Over billions of years, evolutionary pressure has facilitated the development of sophisticated and diverse light-harvesting systems in photosynthetic organisms, enabling them to adapt to a variety of habitats and light conditions. Today, driven by the need for cheap and efficient solar power, we turn to photosynthetic organisms and their light-active supramolecular assemblies for bio-inspiration. By studying natural systems, we can learn more about the basic principles behind the fine-tuned functionalities of light capture and energy transfer on the molecular level. In this review, we use ideas from evolutionary ecology and quantum mechanics to elucidate the parameters that underpin the efficient and robust light-harvesting machinery of natural light-harvesting systems. The family of antenna proteins of cryptophyte algae serves as an example to illustrate the evolutionary diversification process on a structural and consequently a photophysical level. Two-dimensional electronic spectroscopy experiments reveal the existence of coherence among vibronic levels in the initial response of light-harvesting proteins to femtosecond optical excitation. We discuss what implications quantum transport processes might have for light harvesting.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".