Photocatalytic Hydrogen Production Using Semiconductor (CdSe)<sub>13</sub> Clusters
Bibliographic record
Abstract
Atomically precise (CdSe) 13 clusters, the smallest CdSe semiconductors, represent a unique class of materials at the boundary between nanocrystals and molecules. Despite their promising potential, low structural stability limits their applications as photocatalysts. Herein, we report photocatalytic hydrogen production using atomically precise (CdSe) 13 clusters. To improve stability in aqueous environments, we induce self-assembly into suprastructures, making them suitable for water splitting. Our findings demonstrate that Co 2+ doping enhances the electrical properties of these clusters, while bipyridine serves as cocatalyst by interacting with Co 2+ dopants and providing catalytic active sites. Through the synergistic effects of Co 2+ doping and bipyridine, Co 2+ -doped (CdSe) 13 suprastructures achieve promising hydrogen evolution activity, surpassing those of undoped suprastructures or nanoclusters. Theoretical calculations confirm that Co 2+ doping and bipyridine incorporation lower the hydrogen adsorption energy, consistent with the experimental results. These results highlight the potential of semiconductor (CdSe) 13 clusters as photocatalysts for sustainable hydrogen production.
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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".