Contactless near-field scanning thermoreflectance imaging
Bibliographic record
Abstract
Determining and imaging the thermal properties at the nanoscale is a demanding experimental challenge. So far, virtually any techniques used to image nanoscale thermal properties require to position the sample in contact with voluminous probes that act as undesirable thermal sinks and dramatically affect the measurements, in spite of poor interfacial thermal resistivity. Thermoreflectance, a contactless technique in which thermal conductivity is measured by optically probing the heat-induced changes in a sample, is extensively used for measuring the macroscopic and microscopic thermal properties of solids, but, so far, has been limited by diffraction in its applicability at the nanoscale. Here, we present near-field scanning thermoreflectance imaging (NeSTRI), a new scanning probe technique in which an aperture-type near-field optical microscope at sub-wavelength resolution is used to contactlessly determine the thermoreflectance of thin films. As a case study, NeSTRI is here applied to multilayer graphene thin films on glass substrates. Thermal conductivity of micrometre-size multilayer graphene platelets is determined and is consistent with previous macroscopic predictions. We also find that the thermal conductivity is locally higher at specific crystallographic edges of multilayer graphene platelets, which is indicative of the spatial resolution of our method. NeSTRI is uniquely suited to understanding the thermal properties of a large class of nanostructured and nanoscale systems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".