Heavy‐Metal‐Free Colloidal Quantum Dots: Progress and Opportunities in Solar Technologies
Bibliographic record
Abstract
Colloidal quantum dots (QDs) hold great promise as building blocks in solar technologies owing to their remarkable photostability and adjustable properties through the rationale involving size, atomic composition of core and shell, shapes, and surface states. However, most high-performing QDs in solar conversion contain hazardous metal elements, including Cd and Pb, posing significant environmental risks. Here, a comprehensive review of heavy-metal-free colloidal QDs for solar technologies, including photovoltaic (PV) devices, solar-to-chemical fuel conversion, and luminescent solar concentrators (LSCs), is presented. Emerging synthetic strategies to optimize the optical properties by tuning the energy band structure and manipulating charge dynamics within the QDs and at the QDs/charge acceptors interfaces, are analyzed. A comparative analysis of different synthetic methods is provided, structure-property relationships in these materials are discussed, and they are correlated with the performance of solar devices. This work is concluded with an outlook on challenges and opportunities for future work, including machine learning-based design, sustainable synthesis, and new surface/interface engineering.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".