Rayleigh wave propagation method for the characterization of viscoelastic properties of biomaterials
Bibliographic record
Abstract
The frequency-dependent viscoelastic properties of injectable biomaterials used for vocal fold augmentation and repair must be characterized to ensure the integrity with the vibrating tissue throughout the frequency range of vocalization. Experimental methods for quantifying the frequency-dependent viscoelastic properties of biomaterials over a broad frequency range (i.e., up to 4 kHz) using Rayleigh wave propagations were investigated. Appropriate models for Rayleigh wave propagations in single and layered media were developed. Different silicone rubber samples were made and tested to evaluate the proposed methods. Rayleigh waves at different frequencies were launched on the surface of different samples; i.e., single layer samples and samples composed of a substrate with known material properties coated with a thin layer of the soft material that is to be characterized. The input vibrations of the actuator and the motion of the sample surface were measured using an accelerometer and a laser Doppler vibrometer, respectively. A transfer function method was used to obtain the complex Rayleigh wavenumbers. Finally, the complex shear and elastic moduli and the loss factor of samples were calculated through the proper modelling using the measured wavenumbers. The results were compared and shown to be in good agreement with those obtained from other measurement methods.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".