Normalized photoacoustic techniques for thermal diffusivity measurements of buried layers in multilayered systems
Bibliographic record
Abstract
The one-dimensional heat diffusion problem for a three-layer system is solved assuming the surface absorption model. The analytical solution is shown to be suitable for the implementation of normalized depth-profilometric photoacoustic methodologies involving the open photoacoustic-cell configuration for thermal diffusivity measurements in buried underlayers within a three-layer stack. Our normalization procedures eliminate the frequency-dependent instrumental electronic contribution (transfer function) and some thermophysically nonrelevant proportionality factors in the theoretical equations, thus making the depth-profilometric analysis feasible. The measurement methodology is achieved by normalizing the theoretical photoacoustic signal from the three layers with the corresponding signal from the uppermost two layers, involving linear fits to measure the thermal diffusivity of the third underlayer. Three different multilayered materials were examined using the proposed methodologies. High reproducibility of the thermal diffusivity measurements and good agreement with values reported in literature were found. Besides the foregoing procedures, a lumped photoacoustic model was developed, which yields the effective thermal-diffusivity value of the multilayer stack.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".