Dynamics of photoconversion processes: the energetic cost of lifetime gain in photosynthetic and photovoltaic systems
Bibliographic record
Abstract
The continued development of solar energy conversion technologies relies on an improved understanding of their limitations. In this review, we focus on a comparison of the charge carrier dynamics underlying the function of photovoltaic devices with those of both natural and artificial photosynthetic systems. The solar energy conversion efficiency is determined by the product of the rate of generation of high energy species (charges for solar cells, chemical fuels for photosynthesis) and the energy contained in these species. It is known that the underlying kinetics of the photophysical and charge transfer processes affect the production yield of high energy species. Comparatively little attention has been paid to how these kinetics are linked to the energy contained in the high energy species or the energy lost in driving the forward reactions. Here we review the operational parameters of both photovoltaic and photosynthetic systems to highlight the energy cost of extending the lifetime of charge carriers to levels that enable function. We show a strong correlation between the energy lost within the device and the necessary lifetime gain, even when considering natural photosynthesis alongside artificial systems. From consideration of experimental data across all these systems, the emprical energetic cost of each 10-fold increase in lifetime is 87 meV. This energetic cost of lifetime gain is approx. 50% greater than the 59 meV predicted from a simple kinetic model. Broadly speaking, photovoltaic devices show smaller energy losses compared to photosynthetic devices due to the smaller lifetime gains needed. This is because of faster charge extraction processes in photovoltaic devices compared to the complex multi-electron, multi-proton redox reactions that produce fuels in photosynthetic devices. The result is that in photosynthetic systems, larger energetic costs are paid to overcome unfavorable kinetic competition between the excited state lifetime and the rate of interfacial reactions. We apply this framework to leading examples of photovoltaic and photosynthetic devices to identify kinetic sources of energy loss and identify possible strategies to reduce this energy loss. The kinetic and energetic analyses undertaken are applicable to both photovoltaic and photosynthetic systems allowing for a holistic comparison of both types of solar energy conversion approaches.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".