Optical and electronic spin properties of fluorescent micro- and nanodiamonds upon prolonged ultrahigh-temperature annealing
Bibliographic record
Abstract
High-temperature annealing is a promising but still mainly unexplored method for enhancing spin properties of negatively charged nitrogen-vacancy (NV) centers in diamond particles. After high-energy irradiation, the formation of NV centers in diamond particles is typically accomplished via annealing at temperatures in the range of 800–900 °C for 1–2 h to promote vacancy diffusion. Here, we investigate the effects of conventional annealing (900 °C for 2 h) against annealing at a much higher temperature of 1600 °C for the same annealing duration for particles ranging in size from 100 nm to 15 μm using electron paramagnetic resonance and optical characterization. At this high temperature, the vacancy-assisted diffusion of nitrogen can occur. Previously, the annealing of diamond particles at this temperature was performed over short time scales because of concerns of particle graphitization. Our results demonstrate that particles that survive this prolonged 1600 °C annealing show increased NV T1 and T2 electron spin relaxation times in 1 and 15 μm particles, due to the removal of fast relaxing spins. Additionally, this high-temperature annealing also boosts magnetically induced fluorescence contrast of NV centers for particle sizes ranging from 100 nm to 15 μm. At the same time, the content of NV centers is decreased fewfold and reaches a level of <0.5 ppm. The results provide guidance for future studies and the optimization of high-temperature annealing of fluorescent diamond particles for applications relying on the spin properties of NV centers in the host crystals.
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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) | 0.001 | 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".