Terahertz Thermometry: Combining Hyperspectral Imaging and Temperature Mapping at Terahertz Frequencies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it