A PDMS-based broadband acoustic impedance matched material for\n underwater applications
Bibliographic record
Abstract
Having a material that is matched in acoustic impedance with the surrounding\nmedium is a considerable asset for many underwater acoustic applications. In\nthis work, impedance matching is achieved by dispersing small, deeply\nsubwavelength sized particles in a soft matrix, and the appropriate\nconcentration is determined with the help of Coherent Potential Approximation\nand Waterman & Truell models. We show experimentally the validity of the models\nusing mixtures of Polydimethylsiloxane (PDMS) and TiO2 particles. The optimized\ncomposite material has the same longitudinal acoustic impedance as water and\ntherefore the acoustic reflection coefficient is essentially zero over a wide\nrange of frequencies (0.5 to 6 MHz). PDMS-based materials can be cured in a\nmold to achieve desired sample shape, which makes them very easy to handle and\nto use. Various applications can be envisioned, such the use of\nimpedance-matched PDMS in the design and fabrication of acoustically\ntransparent cells for samples, perfectly matched layers for ultrasonic\nexperiments, or superabsorbing metamaterials for water-borne acoustic waves.\n
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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) | 0.002 | 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".