Role of Interfacial Engineering of “Giant” Core–Shell Quantum Dots
Bibliographic record
Abstract
Structural engineering of the shell layer in “giant” core–shell quantum dots (QDs) offers precise control over the spatial carrier separation and other optoelectronic properties by forming a quasi-type-II band alignment. We report the synthesis of highly stable “giant” CdSe–CdS QDs with different CdS shell thicknesses (2.2–4.8 nm) and alloyed Pb x Cd 1– x S interfacial layers at the CdSe–CdS interface. The “giant” core–shell QDs with alloyed interfacial layers show a broader absorption spectrum, faster carrier transfer rate, and higher hole leakage into the shell region, compared to CdSe–CdS QDs with similar size, as confirmed by optical, transient photoluminescence decay measurements and theoretical simulations. As a proof of concept, the as-synthesized “giant” core-alloyed shell QD (denoted as CdSe@CPS-13)-sensitized solar cells (QDSCs) yield a power conversion efficiency (PCE) of 4.15%, which is 77% higher than the PCE of QDSCs based on “giant” CdSe–CdS QDs with comparable size and shell thickness. These results show that interfacial engineering is an effective methodology to tailor the optical and electronic properties of core–shell QDs with great potential to enhance the performance of photovoltaic and other solar-energy-driven devices.
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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".