Ultrasonic Setup for Testing Hydrogels: Preliminary Experiments on Collagen Gels
Bibliographic record
Abstract
The assessment of mechanical properties of highly hydrated natural materials remains a challenge because, in general, their mechanical evaluation implies invasive and finally destructive methods. Acoustic-based tests may represent the appropriate tools to investigate the mechanical properties of such materials, particularly collagen gels, whose acoustic properties are poorly understood. The objective of this work is to develop two experimental setups for the assessment of acoustic properties of such a hydrogels. In the first one, a typical pulse echo reflectometer was implemented. The acoustic parameters were measured at controlled temperature in an especially designed chamber. In the second one, the previous configuration was combined with a setup for compressive tests, allowing to interrogate simultaneously both the acoustic and mechanical properties of the sample under test. The frequency of the acoustic transducer was 10MHz. The acoustic and mechanical properties of collagen gels prepared according to different experimental conditions (pH and collagen concentration) were evaluated. The first set of experiment was useful to accomplish estimation of the speed of sound, attenuation and acoustic impedance. The second one allowed us to monitor the speed of sound during the evolution of the compression test. This approach could be a potential tool to study the changes in hydrogels mass density and bulk compressibility.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".