A Facet‐Specific Quantum Dot Passivation Strategy for Colloid Management and Efficient Infrared Photovoltaics
Bibliographic record
Abstract
Colloidal nanocrystals combine size- and facet-dependent properties with solution processing. They offer thus a compelling suite of materials for technological applications. Their size- and facet-tunable features are studied in synthesis; however, to exploit their features in optoelectronic devices, it will be essential to translate control over size and facets from the colloid all the way to the film. Larger-diameter colloidal quantum dots (CQDs) offer the attractive possibility of harvesting infrared (IR) solar energy beyond absorption of silicon photovoltaics. These CQDs exhibit facets (nonpolar (100)) undisplayed in small-diameter CQDs; and the materials chemistry of smaller nanocrystals fails consequently to translate to materials for the short-wavelength IR regime. A new colloidal management strategy targeting the passivation of both (100) and (111) facets is demonstrated using distinct choices of cations and anions. The approach leads to narrow-bandgap CQDs with impressive colloidal stability and photoluminescence quantum yield. Photophysical studies confirm a reduction both in Stokes shift (≈47 meV) and Urbach tail (≈29 meV). This approach provides a ≈50% increase in the power conversion efficiency of IR photovoltaics compared to controls, and a ≈70% external quantum efficiency at their excitonic peak.
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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".