High-Quality Green-Emitting Nanodiamonds Fabricated by HPHT Sintering of Polycrystalline Shockwave Diamonds
Bibliographic record
Abstract
We demonstrate a high-pressure, high-temperature sintering technique to form nitrogen-vacancy-nitrogen centres in nanodiamonds. Polycrystalline diamond nanoparticle precursors, with mean size of 25 nm, are produced by the shock wave from an explosion. These nanoparticles are sintered in the presence of ethanol, at a pressure of 7 GPa and temperature of 1300 °C, to produce substantially larger (3-4 times) diamond crystallites. The recorded spectral properties demonstrate the improved crystalline quality. The types of defects present are also observed to change; the characteristic spectral features of nitrogen-vacancy and silicon-vacancy centres present for the precursor material disappear. Two new characteristic features appear: (1) paramagnetic substitutional nitrogen (P1 centres with spin ½) with an electron paramagnetic resonance characteristic triplet hyperfine structure due to the I = 1 magnetic moment of the nitrogen nuclear spin and (2) the green spectral photoluminescence signature of the nitrogen-vacancy-nitrogen centres. This production method is a strong alternative to conventional high-energy particle beam irradiation. It can be used to easily produce purely green fluorescing nanodiamonds with advantageous properties for optical biolabelling applications.
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".