Structural Evolutions of ZnS Nanoparticles in Hydrated and Bare States
Bibliographic record
Abstract
Suitable optoelectronic properties and the nontoxic nature of ZnS quantum dots capacitate exciting applications for these nanomaterials especially in the field of biomedical imaging. However, the structural stability of ZnS nanoparticles has been shown to be challenging since they potentially are prone to autonomous structural evolutions in ambient conditions. Thus, it is essential to build an understanding about the structural evolution of ZnS nanoparticles, especially in aqueous environment, before implementing them for in vivo applications. In this study we compared the structure of ZnS nanoparticles relaxed in a vacuum and in water using a classical molecular dynamics method. Structural analyses showed that the previously observed three-phase structure of bare nanoparticles is not formed in the hydrated state. The bulk of hydrated nanoparticles has more crystalline structure; however, the dynamic heterogeneity in their surface relaxation makes them more polar compared to bare nanoparticles. This heterogeneity is more severe in hydrated wurtzite nanoparticles, causing them to show larger dipole moments. Analyzing the structure of water in the first hydration shell of the surface atoms shows that water is mainly adsorbed to the nanoparticles’ surface through Zn–O interaction, which causes the structure of water in the first hydration shell to be discontinuous.
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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".