Contactless Scanning Near‐Field Optical Dilatometry Imaging at the Nanoscale
Bibliographic record
Abstract
Abstract To date, there are very few experimental techniques, if any, that are suitable for the purpose of acquiring quantitative maps of the thermal expansivity of 2D materials and nanostructured thin films with nanoscale lateral resolution in spite of huge demand for nanoscale thermal management, for example in designing integrated circuitry for power electronics. Besides, contactless analytical tools for determining the thermal expansion coefficient (TEC) are highly desirable because probes in contact with the sample significantly perturb any thermal measurements. Here, ω‐2ω near‐field thermoreflectance imaging is presented as a novel, all‐optical, and contactless technique to map the TEC at the nanoscale with precision. Testing of this technique is performed on nanogranular films of gold and multilayer graphene (ML‐G) platelets. With ω‐2ω near‐field thermoreflectance, it is demonstrated that the TEC of Au is higher at the metal‐insulator interface, with an average of (17.12 ± 2.30) ×10−6 K−1 in agreement with macroscopic techniques. For ML‐G, the average TEC is (−5.77 ± 3.79) x10−6 K−1 and is assigned to in‐plane vibrational bending modes. A vibrational‐thermal transition from graphene to graphite is observed, where the TEC becomes positive as the ML thickness increases. The nanoscale method here reported demonstrates results in excellent agreement with its macroscopic counterparts, as well as superior capabilities to probe 2D materials and interfaces.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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; 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".