Improving triplet-triplet-annihilation based upconversion systems by tuning their topological structure
Bibliographic record
Abstract
Materials capable to perform upconversion of light transform the photon spectrum and can be used to increase the efficiency of solar cells by upconverting sub-bandgap photons, increasing the density of photons able to generate an electron-hole pair in the cell. Incoherent solar radiation suffices to activate upconverters based on sensitized triplet-triplet annihilation, which makes them particularly suited for this task. This process requires two molecular species, sensitizers absorbing low energy photons, and emitters generating higher frequency photons. Successful implementations exist in solutions and solids. However, solid upconverters exhibit lower efficiency than those in solution, which poses a serious problem for real applications. In the present work, we suggest a new strategy to increase the efficiency of sensitized upconverters that exploits the solid nature of the material. We show that an upconversion model system with molecules distributed as clusters outperforms a system with a random distribution of molecules, as used in current upconverters. Our simulations reveal a high potential for improvement of upconverter systems by exploring different structural configurations of the molecules. The implementation of advanced structures can push the performance of solid upconverters further towards the theoretical limit and a step closer to technological application of low power upconversion.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".