Using carbon nanodots as inexpensive and environmentally friendly sensitizers in mesoscopic solar cells
Bibliographic record
Abstract
We discuss the use of carbon nanodots (CNDs) as sensitizers in mesoscopic solar cells. The CNDs are synthesized using a one-step, bottom-up microwave approach with citric acid, urea, and formic acid as precursors in aqueous media. Their light-harvesting capabilities can be tuned by adjusting the synthetic parameters. Comprehensive spectroscopic and theoretical studies allow us to rationalize the nature of their absorption features. Promising power conversion efficiencies (η) of 0.24% can be achieved from these cheap and eco-friendly sensitizers by optimizing the solar-cell assembly process. Interestingly, we found that extending the light absorption towards longer wavelengths does not necessarily improve the performance of the solar cells, since the longer-wavelength absorption features hardly contribute to the cells' photo-action spectra, so that the overall power conversion efficiency is actually worse. The origin of the lower performance is corroborated in transient absorption spectroscopy and photovoltage decay measurements. Our work points, on one hand, to the limits of as-synthesized CNDs as photosensitizers and, on the other hand, to possible improvements.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".