Multibandgap quantum dot ensembles for solar-matched infrared energy harvesting
Bibliographic record
Abstract
Abstract As crystalline silicon solar cells approach in efficiency their theoretical limit, strategies are being developed to achieve efficient infrared energy harvesting to augment silicon using solar photons from beyond its 1100 nm absorption edge. Herein we report a strategy that uses multi-bandgap lead sulfide colloidal quantum dot (CQD) ensembles to maximize short-circuit current and open-circuit voltage simultaneously. We engineer the density of states to achieve simultaneously a large quasi-Fermi level splitting and a tailored optical response that matches the infrared solar spectrum. We shape the density of states by selectively introducing larger-bandgap CQDs within a smaller-bandgap CQD population, achieving a 40 meV increase in open-circuit voltage. The near-unity internal quantum efficiency in the optimized multi-bandgap CQD ensemble yielded a maximized photocurrent of 3.7 ± 0.2 mA cm−2. This provides a record for silicon-filtered power conversion efficiency equal to one power point, a 25% (relative) improvement compared to the best previously-reported results.
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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".