Broadband superabsorption of waterborne acoustic waves by bubble metascreens
Bibliographic record
Abstract
Absorption of acoustic or mechanical waves is an important challenge for various applications such as noise insulation, stealth coating, seismic event mitigation and ultrasonic testing. In order to absorb sound, one needs to introduce a medium with sufficient dissipation but without significant reflection of the incoming wave. Ideally, the absorber should also be thin and light, a goal that may be realized through the use of a thin 2-D metamaterial, or metalayer. The conventional way of achieving strong absorption in a thin metamaterial is to exploit low-frequency resonant inclusions. However, most resonant structures have an intrinsically narrowband response, making it difficult to attain broadband absorption in a deeply subwavelength-thick meta-layer, and making it necessary to devise larger structures to increase the bandwidth. For waterborne acoustic waves, an exception to this common situation can be achieved through the fabrication of bubble metascreens, which consist of a single layer of bubble inclusions embedded in a soft solid. In this presentation, we re-visit the optimization of such bubble metascreens and show that, despite being resonance-based, near-perfect absorption is possible over a very wide frequency range even when the metalayer is ultrathin.
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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 | 0.001 |
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".