Cu2S Nanocrystals and Their Superlattices
Bibliographic record
Abstract
We report the successful synthesis of monodispersed Cu2S nanocrystals and the subsequent formation of highly ordered nanocrystal superlattices. The synthesis is performed under ambient air conditions using simple experimental setups, making the process both accessible and scalable. By systematically tuning the reaction temperature and duration, we demonstrate precise control over the nanocrystal size, which is crucial in achieving uniformity and monodispersity. Furthermore, we uncover a previously unidentified nanocrystal growth mechanism that plays a key role in producing highly monodisperse Cu2S nanocrystals. This insight into the growth process enhances our fundamental understanding of nanocrystal formation and could be extended to the synthesis of other semiconductor nanomaterials. The self-assembly of these nanocrystals into superlattices is carefully examined using electron diffraction techniques, revealing the presence of pseudo-crystalline structures. The ordered arrangement of nanocrystals within these superlattices suggests strong interparticle interactions and opens up new possibilities to tailor their collective optical, electronic, and mechanical properties for potential applications in optoelectronics, nanomedicine, and energy storage.
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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".