Bibliographic record
Abstract
Bubbly metamaterials are created by exploiting the low-frequency Minnaert resonance of bubbles, and can radically modify acoustic wave behaviour. In this presentation, I will first review the properties of bubble metascreens, which consist of a single layer of bubbles in a soft solid, and can be very efficient absorbers of waterborne acoustic waves. Two different metalayer configurations will be considered that allow almost perfect compensation of the radiative scattering losses, so that virtually all of the acoustic energy can be absorbed. I will show how optimization of bubble metascreens’ performance is facilitated by a relatively simple analytical model, and that, despite being resonance-based, near-perfect absorption is possible over a very wide frequency range even when the metalayer is ultrathin. In the second part of the talk, I will describe three-dimensional structures with pair-wise spatial correlations between the bubbles. Such structures exhibit doubly negative behaviour even though the low-frequency resonance of a single bubble is purely monopolar. Furthermore, this doubly negative behaviour can occur when the bubble pairs are arranged in either random or periodic configurations. Predictions for both types of structure will be presented and the influence of dissipation on doubly negative behaviour discussed. For the 3D crystalline metamaterial case, the focusing and imaging capabilities of a flat metalens will be demonstrated, showing the imaging of a point source by a slab with a relative refractive index n = -1, evidence of super-resolved focusing, and the imaging of an extended object.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".