High-Internal-Phase Pickering Emulsions for Enhanced Sound-Absorbing Materials
Bibliographic record
Abstract
This work introduces a simple, efficient, and reliable approach for producing acoustic porous materials by using solid-stabilized emulsion templates. The technique allows for precise control of the pore size through straightforward emulsification processing conditions, highlighting its potential for developing multifunctional acoustic foams. The microstructure of the porous material was investigated using X-ray microtomography and open pore network modeling. The correlation between processing conditions, porous microstructure, and acoustic performance was determined. The findings reveal that the desired sound absorption performance can be achieved by adjusting the rotational speed during emulsification, which affects droplet size and ultimately results in targeted pore size, connectivity, and tortuosity. Notably, a near perfect sound absorption coefficient at 1100 Hz was achieved for samples with largest pores, highest porosity, and greatest connectivity. Furthermore, samples with medium porosity and pore size, but the highest tortuosity, exhibited maximum sound absorption below 500 Hz, despite a thickness of only 3 cm. This performance is particularly notable, as it is challenging to achieve with conventional acoustic foams, demonstrating the potential of this novel approach for developing high-performance acoustic materials over broad ranges of frequencies.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".