Core/Shell Quantum Dots Solar Cells
Bibliographic record
Abstract
Abstract Semiconductor nanocrystals, the so‐called quantum dots (QDs), exhibit versatile optical and electrical properties. However, QDs possess high density of surface defects/traps due to the high surface‐to‐volume ratio, which act as nonradiative carrier recombination centers within the QDs, thereby deteriorating the overall solar cell performance. The surface passivation of QDs through the growth of an outer shell of different materials/compositions called “core/shell QDs” has proven to be an effective approach to reduce the surface defects and confinement potential, which can enable the broadening of the absorption spectrum, accelerate the carrier transfer, and reduce exciton recombination loss. Here, the recent research developments in the tailoring of the structure of core/shell QDs to tune exciton dynamics so as to improve solar cell performance are summarized. The role of band alignment of core and shell materials, core size, shell thickness/compositions, and interface engineering of core/thick shell called “giant” QDs on electron–hole spatial separation, carrier transport, and confinement potential, before and after grafting on the carrier scavengers (semiconductor/electrolyte), is described. Then, the solar cell performance based on core/shell QDs is introduced. Finally, an outlook for the rational design of core/shell QDs is provided, which can further promote the development of high‐efficiency and stable QD sensitized solar cells.
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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) | 0.002 | 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".