Thermal conductivity of thin film-substrate systems from two-side scanning photothermal deflection measurements: Theoretical model and validation
Bibliographic record
Abstract
Photothermal deflection (PTD) has been frequently utilized to measure the thermal properties of thin solid films on a substrate. In the models commonly used to interpret PTD data, the substrate is assumed to be an ideal thermal insulator. This assumption poses important restrictions on the reliability of these thermal measurements and limits the possibility to use PTD for also measuring the specific heat of the samples. Simultaneous knowledge of specific heat and thermal diffusivity is necessary to determine the thermal conductivity of thin solid films. In this work, we calculated the phase and amplitude of the PTD signal at the two opposites sides (film-side and substrate-side) of a thin-film substrate system. We find that, on both sides, the phases of the PTD signal primarily depend on the thermal diffusivity of the thin film, while the amplitudes primarily depend on the specific heat. By using the phases and amplitudes at the two sides, we show that the accuracy of thermal conductivity measurements by PTD can be dramatically improved. We validate our theoretical model by measuring, in a scanning PTD apparatus, the thermal properties of gold thin films, which are in excellent agreement with, and improve on, existing data from the literature.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".