Bright Quantum-Grade Fluorescent Nanodiamonds
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Optically accessible spin-active nanomaterials are promising as quantum nanosensors for probing biological samples. However, achieving bioimaging-level brightness and high-quality spin properties for these materials is challenging and hinders their application in quantum biosensing. Here, we demonstrate bright fluorescent nanodiamonds (NDs) containing 0.6–1.3-ppm negatively charged nitrogen-vacancy (NV) centers by spin-environment engineering via enriching spin-less 12 C-carbon isotopes and reducing substitutional nitrogen spin impurities. The NDs, readily introduced into cultured cells, exhibited improved optically detected magnetic resonance (ODMR) spectra; peak splitting ( E ) was reduced by 2–3 MHz, and microwave excitation power required was 20 times lower to achieve a 3% ODMR contrast, comparable to that of conventional type-Ib NDs. They show average spin-relaxation times of T 1 = 0.68 ms and T 2 = 3.2 μs (1.6 ms and 5.4 μs maximum) that were 5- and 11-fold longer than those of type-Ib, respectively. Additionally, the extended T 2 relaxation times of these NDs enable shot-noise-limited temperature measurements with a sensitivity of approximately 0.28 K / Hz . The combination of bulk-like NV spin properties and enhanced fluorescence significantly improves the sensitivity of ND-based quantum sensors for biological applications.
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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".