Core‐Shell Colloidal Quantum Dots for Energy Conversion
Bibliographic record
Abstract
Abstract Colloidal quantum dots (QDs) are promising building blocks in optoelectronic devices, mainly due to their size/shape/composition‐tunable properties. Core–shell QDs, in particular, offer enhanced stability, mitigated photoluminescence blinking, and suppressed non‐radiative recombination compared to plain QDs, making them highly promising for energy conversion applications such as photovoltaic devices, luminescent solar concentrators, solar‐driven hydrogen production, and light‐emitting diodes. Here, a comprehensive analysis of core–shell QDs in energy conversion technologies is provided. Emerging design strategies are explored and various synthetic methods focusing on optimizing band structure, band alignment, and optical properties are critically explored. Insights into the structure‐property relationship are discussed, highlighting recent advancements and the most effective strategies to enhance energy conversion performance. The review is concluded by addressing key challenges and proposing future research directions, emphasizing the need for rational design, precise synthesis, effective surface engineering, and the integration of machine learning to achieve optimized properties for technological applications.
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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".