Enhanced conversion efficiency in Si solar cells employing photoluminescent down-shifting CdSe/CdS core/shell quantum dots
Bibliographic record
Abstract
Abstract Silicon solar cells have captured a large portion of the total market of photovoltaic devices mostly due to their relatively high efficiency. However, Silicon exhibits limitations in ultraviolet absorption because high-energy photons are absorbed at the surface of the solar cell, in the heavily doped region, and the photo-generated electron-hole pairs need to diffuse into the junction region, resulting in significant carrier recombination. One of the alternatives to improve the absorption range involves the use of down-shifting nano-structures able to interact with the aforementioned high energy photons. Here, as a proof of concept, we use downshifting CdSe/CdS quantum dots to improve the performance of a silicon solar cell. The incorporation of these nanostructures triggered improvements in the short circuit current density (J sc , from 32.5 to 37.0 mA/cm 2 ). This improvement led to a ∼13% increase in the power conversion efficiency (PCE), from 12.0 to 13.5%. Our results demonstrate that the application of down-shifting materials is a viable strategy to improve the efficiency of Silicon solar cells with mass-compatible techniques that could serve to promote their widespread utilization.
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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".