Promoting Charge Separation in Semiconductor Nanocrystal Superstructures for Enhanced Photocatalytic Activity
Bibliographic record
Abstract
Abstract Increasing the lifetime of photoexcited charge carriers in metal oxide semiconductor nanocrystals is essential for the promotion of their photocatalytic efficiency. In this context, different strategies are developed for tailoring the structural and electronic properties of semiconductors at the single nanocrystal level, mainly including band‐structure engineering, doping, catalyst‐support interaction tuning, and cocatalysts decoration. Recently, an alternative strategy for prolonging the lifetime of photoexcited charge carriers is discovered at the nanocrystal superstructure level. By assembling semiconductor nanocrystals into ordered superstructures, a new pathway is created for the spatial separation of charge carriers between neighboring nanocrystals within the superstructure network. The inter‐nanocrystal charge transfer in the superstructures enables the increased charge separation efficiency of photogenerated electron–hole pairs, prolongs their lifetime and, in turn, improves the photocatalytic activity. In this review, recent developments of nanocrystal‐superstructure‐based photocatalysts and their applications in catalyzing different reactions are summarized. Several perspectives in terms of challenges and future research in this area are highlighted.
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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".