Experimental Measurement of Multiple Thermal Properties by Error Minimization
Bibliographic record
Abstract
Common thin film thermometry techniques are usually based on transient heat diffusion within a sample and its surroundings and are therefore sensitive to the film’s thermal conductivity (k) and heat capacity (C). This presents a problem of under-constraint in the numerical fitting models when both k and C of a given film are unknown. A number of approaches and assumptions have been studied to eliminate this dual dependence or estimate C analytically. However, they often amount to little more than fitting parameters, experimental assumptions, and rough estimates for many composite and polymer films that are emerging in the microelectronics and MEMS industries. The effect that the uncertainty in one property has on the prediction of the other is discussed in the framework of the polymer film PVDF used in many microsensor and actuator applications. An error surface analysis is used to describe the link between assumption and prediction for thermoreflectance and temperature phase measurement techniques. A methodology is presented that combines the results of two thermal tests through an error minimization algorithm to solve for both k and C with no analytical assumptions or approximations. This approach is demonstrated with an experimental test case, validated with synthesized data, and generalized to any system variable and a multitude of thin film thermometry variable or thin film thermometry technique.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".