Bottom‐Up Synthesis of Hexagonal Boron Nitride Nanoparticles with Intensity‐Stabilized Quantum Emitters
Bibliographic record
Abstract
Fluorescent nanoparticles are widely utilized in a large range of nanoscale imaging and sensing applications. While ultra-small nanoparticles (size ≤10 nm) are highly desirable, at this size range, their photostability can be compromised due to effects such as intensity fluctuation and spectral diffusion caused by interaction with surface states. In this article, a facile, bottom-up technique for the fabrication of sub-10-nm hexagonal boron nitride (hBN) nanoparticles hosting photostable bright emitters via a catalyst-free hydrothermal reaction between boric acid and melamine is demonstrated. A simple stabilization protocol that significantly reduces intensity fluctuation by ≈85% and narrows the emission linewidth by ≈14% by employing a common sol-gel silica coating process is also implemented. This study advances a promising strategy for the scalable, bottom-up synthesis of high-quality quantum emitters in hBN nanoparticles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".