Determining thermo‐mechanical properties of polydimethylsiloxanes from their strain‐induced spectral fingerprints
Bibliographic record
Abstract
ABSTRACT Adaptive properties and complex shapes of modern day soft matter components create a challenge for materials applications where mechanical properties of intricate fabricated components cannot be determined from conventional invasive and destructive mechanical tests. In particular, challenges arising from variable mechanical properties of polydimethylsiloxanes (PDMSs) constantly attract wide‐scale attention in the fields of material sciences, biological systems, and microfluidics. Herein, a noninvasive and nondestructive strain‐induced infrared spectroscopic method (S‐FTIR) is developed. S‐FTIR is a method that maps thermo‐mechanical response of PDMS to its strain‐induced spectral fingerprint. From the results of this study, strong correlations of up to 95% between spectral fingerprint of PDMS and its corresponding nonlinear thermo‐mechanical response is seen. Given the nature of these results, it is expected that S‐FTIR will provide an interesting new analytical approach to understand soft materials and allow for the characterization of micro and nanoscale devices composed of these polymeric materials as well. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2019, 57, 359–367
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".