Terahertz Thermometry: Combining Hyperspectral Imaging and Temperature Mapping at Terahertz Frequencies
Bibliographic record
Abstract
Abstract The accurate and non‐invasive determination of multiple physical parameters, with well‐defined spatial resolution, is crucial for applications in manufacturing, chemistry, medicine and biology. Specifically, the ability to simultaneously measure both temperature and spectral signatures is still experimentally unavailable. To this end, we propose a mapping technique for biological systems, which exploits a linear correlation between terahertz wave reflectivity and temperature, and allows to spatially and spectrally resolve thermal distributions. This method is applied to a model biological system in two relevant cases where in one example, nanoplasmonic‐induced photothermal effects are imaged gaining new insights into collective heating phenomena. In the second example, we demonstrate a joint thermal‐hyperspectral imaging approach to chemically map the presence of a model drug formulation and simultaneously investigate its thermal stability in our biological system. This concept can be easily extended and widely applied to all materials that demonstrate a measurable change in their dielectric properties.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".