Advanced Surface Engineering and Passivation Strategies of Quantum Dots for Breaking Efficiency Barrier of Clean Energy Technologies: A Comprehensive Review
Bibliographic record
Abstract
Abstract Colloidal quantum dots (QDs) have garnered significant attention for their unique potential in clean energy technologies, owing to their tunable optoelectronic properties. However, the presence of surface traps/defects is a major limitation, adversely affecting charge dynamics, optical properties, and overall device performance. This review presents a comprehensive understanding of the origin and intrinsic nature of these surface defects, focusing on how they influence the performance of QDs‐based devices. Recent advances in surface engineering strategies, including solid‐phase and solution‐phase ligand exchange strategies, are delved into, as they play a crucial role in mitigating the impact of surface defects and allow tuning of QDs structural and optoelectronic properties. Furthermore, the review addresses the role of purification procedures and sensitization techniques in enhancing the QDs properties. The recent progress in the development of surface‐engineered QDs‐based clean energy technologies, including photovoltaic (PV), luminescent solar concentrators (LSC) for electricity, and photoelectrochemical (PEC) for green hydrogen production, where improvements in both efficiency and stability of these technologies are thoroughly discussed. The practical challenges and hurdles in scaling up these approaches are also critically examined and future directions are explored to inspire further advances in the rational design of QDs for solar energy conversion and other optoelectronic technologies.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.000 |
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".