Perovskite Quantum Dots for Improvement in Efficiency of Perovskite Solar Cells: Recent Advances and Prospects
Bibliographic record
Abstract
Abstract Quantum dots (QDs) have emerged as transformative materials for enhancing the performance and stability of perovskite solar cells (PSCs), leveraging their tunable bandgap, energy level alignment, and interfacial engineering capabilities. This review focuses on three critical research frontiers. First, the interfacial defect passivation of QDs represents one of the primary themes of this review. Multifunctional ligands or dopants passivate perovskite surface defects, reducing the trap‐state density and extending the carrier lifetime. Ligand‐engineered QDs also form hydrophobic barriers, improving the device stability. The bandgap tuning and energy level alignment of QDs are critical factors influencing the performance of PSCs. Narrow‐bandgap QDs further enable multiple exciton generation, overcoming the Shockley‐Queisser limit of traditional single‐junction solar cells. In addition, QDs, as multifunctional interlayers, play a pivotal role in enhancing the performance of both single‐junction and tandem solar cells. Future research directions focus on ternary/binary QD compositions for achieving spectral complementarity in tandem cells, bifunctional ligand designs for charge transport, and surface chemistry engineering to integrate defect passivation with ultrafast carrier injection, aiming to obtain PCEs exceeding 25% while addressing stability challenges.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".