Self-Focusing by Ostwald Ripening: A Strategy for Layer-by-Layer Epitaxial Growth on Upconverting Nanocrystals
Bibliographic record
Abstract
We demonstrate a novel epitaxial layer-by-layer growth on upconverting NaYF(4) nanocrystals (NCs) utilizing Ostwald ripening dynamics tunable both in thickness and composition. Injection of small sacrificial NCs (SNCs) as shell precursors into larger core NCs results in the rapid dissolution of the SNCs and their deposition onto the larger core NCs to yield core-shell structured NCs. Exploiting this NC size dependent dissolution/growth, the shell thickness can be controlled either by manipulating the number of SNCs injected or by successive injection of SNCs. In either of these approaches, the NCs self-focus from an initial bimodal distribution to a unimodal distribution (σ <5%) of core-shell NCs. The successive injection approach facilitates layer-by-layer epitaxial growth without the need for tedious multiple reactions for generating tunable shell thickness, and does not require any control over the injection rate of the SNCs, as is the case for shell growth by precursor injection.
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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".