Sodium lanthanide fluoride core-shell nanocrystals: A general perspective on epitaxial shell growth
Bibliographic record
Abstract
Understanding the structural characteristics and growth mechanism(s) are essential for generating core-shell nano-heterostructures with distinctive properties. Especially in lanthanide-based nanocrystals, rational design of the core-shell composition can be utilized to enhance/tune the optical properties of the final nanostructure, or can be used to integrate multiple functional applications (e.g., luminescent/magnetic). In this article, we review the progress in our current understanding of the epitaxial shell growth in sodium lanthanide fluoride (NaLnF4) nanocrystals. In order to understand epitaxial shell growth the core nanocrystals have to be uniform, and to date the synthesis of high quality near uniform size/shape dispersion controlled synthesis of lanthanide-based nanocrystals has been achieved mainly with this class of nanocrystals. The progress in core-shell synthesis and the epitaxial shell growth mechanism in this class of nanocrystals (NaLnF4) are reviewed, and a general perspective is provided on the core-shell morphology based on different characterization techniques. While there has been tremendous progress in studying the impact of core-shell structures in various functional applications, this review also highlights, in our view, the still limited understanding of ways to control the core-shell morphology and it emphasizes some important, unanswered questions that remain to be addressed to maximize their performance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".