Highly Stable Colloidal “Giant” Quantum Dots Sensitized Solar Cells
Bibliographic record
Abstract
Colloidal quantum dots (QDs) are widely studied due to their promising optoelectronic properties. This study explores the application of specially designed and synthesized “giant” core/shell CdSe/(CdS)x QDs with variable CdS shell thickness, while keeping the core size at 1.65 nm, as a highly efficient and stable light harvester for QD sensitized solar cells (QDSCs). The comparative study demonstrates that the photovoltaic performance of QDSCs can be significantly enhanced by optimizing the CdS shell thickness. The highest photoconversion efficiency (PCE) of 3.01% is obtained at optimum CdS shell thickness ≈1.96 nm. To further improve the PCE and fully highlight the effect of core/shell QDs interface engineering, a CdSexS1−x interfacial alloyed layer is introduced between CdSe core and CdS shell. The resulting alloyed CdSe/(CdSexS1−x)5/(CdS)1 core/shell QD‐based QDSCs yield a maximum PCE of 6.86%, thanks to favorable stepwise electronic band alignment and improved electron transfer rate with the incorporation of CdSexS1−x interfacial layer with respect to CdSe/(CdS)6 core/shell. In addition, QDSCs based on “giant” core/CdS‐shell or alloyed core/shell QDs exhibit excellent long‐term stability with respect to bare CdSe‐based QDSCs. The giant core/shell QDs interface engineering methodology offers a new path to improve PCE and the long‐term stability of liquid junction QDSCs.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".