Rayleigh wave propagation method for the characterization of a thin layer of biomaterials
Bibliographic record
Abstract
An experimental method based on Rayleigh wave propagation was developed for quantifying the frequency-dependent viscoelastic properties of a small volume of expensive biomaterials over a broad frequency range. Synthetic silicone rubber and gelatin materials were fabricated and tested to evaluate the proposed method. Planar harmonic Rayleigh waves at different frequencies, from 80 to 4000 Hz, were launched on the surface of a sample composed of a substrate with known material properties coated with a thin layer of the soft material to be characterized. A transfer function method was used to obtain the complex Rayleigh wavenumber. An inverse wave propagation problem was solved and a complex nonlinear dispersion equation was obtained. The complex shear and elastic moduli of the sample materials were then calculated through the numerical solution of the obtained dispersion equation using the measured wavenumbers. The results were in good agreement with those of a previous independent study. The proposed method was found to be reliable and cost effective for the measurement of viscoelastic properties of a thin layer of expensive biomaterials, such as phonosurgical biomaterials, over a wide frequency range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".