Perovskite Quantum Dot Solar Cells: An Overview of the Current Advances and Future Perspectives
Bibliographic record
Abstract
Perovskite quantum dots (PQDs) have revolutionized the field of perovskite solar cells in recent years. Using PQDs improves the operational stability of these devices, which is one of their main drawbacks for applications. This factor has motivated an intense search for new advances, from a fundamental aspect to improved performance in devices. Therefore, the developments obtained for PQD solar cells are discussed, presenting the challenges already overcome and the upcoming tendencies for research. Thus, the fundamental aspects of halide perovskite structures are first introduced. The advantages of their preparation as quantum dots are presented as well. The advances for post‐treatments (purification, passivation, and ligand exchange) are then discussed. Next, an in‐depth discussion of the PQD solar cell architectures is made, highlighting both the obsolete configurations and upcoming tendencies. A more specific view of the PQD compositions is then made, including lead‐free compositions and strategies for ionic substitution. Links of the photovoltaic performance are constructed with the devices’ architecture, post‐treatments, and perovskite composition, providing a wide‐ranging overview of these parameters for the devices’ efficiencies. Finally, the authors’ point of view about the future of PQD solar cell technology is presented, showing the main drawbacks, advantages, and opportunities for research.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".