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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".