Theoretical framework and experimental methodology to elucidate the supersaturation dynamics of nanocrystal growth
Bibliographic record
Abstract
Supersaturation is the fundamental parameter driving crystal formation, yet its dynamics in the growth of colloidal nanocrystals (NCs) remain poorly understood. Here, we demonstrate an approach to characterize supersaturation during classical NC growth. We develop a framework that relates noninvasive measurements of the temporal, size-dependent optical properties of growing NCs to the supersaturation dynamics underlying their growth. Using this approach, we investigate the seed-mediated growth of colloidal Au nanocubes, identifying a triphasic sequence of supersaturation dynamics: rapid monomer consumption, sustained supersaturation, and then gradual monomer depletion. These NCs undergo different shape evolutions in different phases of the supersaturation dynamics. As shown with the Au nanocubes, elucidated supersaturation profiles enable the prediction of growth profiles of NCs. We then apply these insights to rationally modulate NC shape evolutions, decreasing the yield of impurity products. Our findings reveal that the supersaturation dynamics of NC growth can be more complex than previously understood. As our approach is applicable to many types of NCs undergoing classical growth, this work presents an initial step towards more deeply interpreting the phenomena governing nanoscale crystal growth and provides insight for the rational design of NCs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".