Pulsed axial epitaxy of colloidal quantum dots in nanowires enables facet-selective passivation
Bibliographic record
Abstract
Epitaxially stacking colloidal quantum dots in nanowires offers a route to selective passivation of defective facets while simultaneously enabling charge transfer to molecular adsorbates - features that must be combined to achieve high-efficiency photocatalysts. This requires dynamical switching of precursors to grow, alternatingly, the quantum dots and nanowires - something not readily implemented in conventional flask-based solution chemistry. Here we report pulsed axial epitaxy, a growth mode that enables the stacking of multiple CdS quantum dots in ZnS nanowires. The approach relies on the energy difference of incorporating these semiconductor atoms into the host catalyst, which determines the nucleation sequence at the catalyst-nanowire interface. This flexible synthetic strategy allows precise modulation of quantum dot size, number, spacing, and crystal phase. The facet-selective passivation of quantum dots in nanowires opens a pathway to photocatalyst engineering: we report photocatalysts that exhibit an order-of-magnitude higher photocatalytic hydrogen evolution rates than do plain CdS quantum dots.
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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) | 0.000 | 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".