Near-field scanning thermoreflectance imaging (NeSTRI) as a nano-optical technique for contactlessly mapping the thermal conductivity of 2D materials at the nanoscale
Bibliographic record
Abstract
To date, virtually all techniques used to image the thermal properties of 2D materials and thin films at the nanoscale have required to position the sample in contact with probes that act as undesirable thermal sinks and dramatically affect the measurements. Thermoreflectivity, an optical technique in which thermal transport properties are measured by contactlessly probing the heat-induced changes in reflectivity at the air-sample interface, has been utilized to image and map the thermal conductivity of solids at the macroscopic and microscopic level, but, so far, has been diffraction-limited in its applicability at the nanoscale. In this paper, we show how our group has tackled such an issue by coupling thermoreflectivity mapping with near-field scanning optical microscopy (NSOM) in a pump-probe nano-optical technique [Nanoscale 9 (2017) 4097]. We show that our technique is successful in investigating the local impact on the thermal conductivity of edges and wrinkles of non-ideal domains of 2D materials. Further on, we investigate the thermal properties of a graphene thin film decorated with copper particles and demonstrate that contactless near-field scanning thermoreflectance imaging can map the electron-phonon coupling in graphene-based nanocomposites.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".