Real-Time Ratiometric Optical Nanoscale Thermometry
Bibliographic record
Abstract
All-optical nanothermometry has become a powerful, remote tool for measuring nanoscale temperatures in applications ranging from medicine to nano-optics and solid-state nanodevices. The key features of any candidate nanothermometer are brightness, sensitivity, and (signal, spatial, and temporal) resolution. Here, we demonstrate a real-time, diamond-based nanothermometry technique with excellent sensitivity (1.8% K –1 ) and record-high resolution (5.8 × 10 4 K Hz –1/2 W cm –2 ) based on codoped nanodiamonds. The distinct performance of our approach stems from two factors: (i) temperature sensors─nanodiamonds cohosting two group IV color centers─engineered to emit spectrally separated Stokes and anti-Stokes fluorescence signals under excitation by a single laser source and (ii) a parallel detection scheme based on filtering optics and high-sensitivity photon counters for fast readout. We demonstrate the performance of our method by monitoring temporal changes in the local temperature of a microcircuit and a MoTe 2 field-effect transistor. Our work advances a powerful, alternative strategy for time-resolved temperature monitoring and mapping of micro-/nanoscale devices such as microfluidic channels, nanophotonic circuits, and nanoelectronic devices, as well as complex biological environments such as tissues and cells.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".