Development, characterization, and modeling of environmentally friendly open‐cell acoustic foams
Bibliographic record
Abstract
Abstract This study shows the development of new polymeric open‐cell foams from polypropylene (PP) and polylactide (PLA) resins with a focus on sound absorption properties and modeling of these foams. The objective is to develop new environmentally friendly foams to replace the existing non‐recyclable Polyurethane foams are currently used for sound insulation in industry. Through this research, open‐cell foams of about 90% porosity were fabricated from PP and PLA. These resins were selected since PP is a recyclable thermoplastic polymer, and PLA is a bio‐based thermoplastic polymer made from renewable resources. Polyurethane (PU) foam which is currently used for sound absorption and noise control in industry was compared to the fabricated PP and PLA foams. As the first attempt to fabricate environmentally friendly acoustic foams, the resulting foam structures show improved properties as compared to the existing materials. The average absorption of PP and PLA foams fabricated is in the range of 0.42–0.55 which is comparable or even higher than the average absorption of PU foam. To better understand the effect of structural and material properties on sound absorption and further improve the acoustic performance of bio‐based foams, an analytical model based on Johnson–Champoux–Allard model was used to numerically simulate the acoustic performance of foams under study. POLYM. ENG. SCI., 2013. © 2013 Society of Plastics Engineers
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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 | 0.001 |
| 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 | 0.001 | 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".