Spatially Resolved Quantum Sensing with High-Density Bubble-Printed Nanodiamonds
Bibliographic record
Abstract
) centers in nanodiamonds have emerged as a versatile platform for a wide range of applications, including bioimaging, photonics, and quantum sensing. However, the widespread adoption of nanodiamonds in practical applications has been hindered by the challenges associated with patterning them into high-resolution features with sufficient throughput. In this work, we overcome these limitations by introducing a direct laser-writing bubble printing technique that enables the precise fabrication of two-dimensional nanodiamond patterns. The printed nanodiamonds exhibit a high packing density and strong photoluminescence emission, as well as robust optically detected magnetic resonance (ODMR) signals. We further harness the spatially resolved ODMR of the nanodiamond patterns to demonstrate the mapping of two-dimensional temperature gradients using high frame rate widefield lock-in fluorescence imaging. This capability paves the way for integrating nanodiamond-based quantum sensors into practical devices and systems, opening new possibilities for applications involving high-resolution thermal imaging and biosensing.
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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) | 0.000 | 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".