Modeling of the acoustic absorption of bi-modal polylactide foams
Bibliographic record
Abstract
In this study, highly porous bi-modal structures were designed and fabricated from polylactide (PLA) as the main structure and utilizing polyethylene glycol (PEG) to form micro pores by compression molding combined with particulate leaching technique. The pore size of the foam structure was controlled by salt particulates and higher interconnectivity was achieved by the co-continuous blending morphology of PLA matrix with water-soluble PEG. This fabrication method makes it possible to control pore geometry and interconnectivity closely and therefore is an ideal approach to study the relation between microstructure and acoustic properties of the foams. PLA is a bio-based thermoplastic polymer derived from renewable resources. Therefore, the resulting acoustic foams are benign and environmentally friendly. Fabricated foams were characterized based on cellular, acoustic, and mechanical properties. The acoustic performance of the foams was studied by measuring the normal incident absorption coefficient in accordance with the ASTM E1050 standard. An analytical model based on Johnson–Champoux–Allard model was used to numerically simulate the acoustic performance of foams under study. Numerical results predict the absorption behavior of PLA foams with high accuracy. Through this research, open porosities close to 90% were achieved, and the effect of water soluble polymer on cellular properties, acoustic and mechanical performance of polylactide foams was studied.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".