Evaluation of electro-spun polymeric nanofibers for sound absorption applications
Bibliographic record
Abstract
The electrospinning process was used to manufacture nanofiber mats of polymeric materials that have low thickness and improved sound absorption characteristics. The processed nanofiber composites consisted of a number of formulated combinations of polymeric materials, i.e., Polyvinyl Alcohol (PVA), Polystyrene (PS), and Polyvinyl Chloride (PVC). These materials were further modified by addition of fillers like Carbon nanotubes (CNTs), Graphene (GN), Wollastonite (WS) and Fiberglass (FG) for the purpose of betterment of their soundproofing characteristics. This paper focuses on an experimental study for evaluating the sound absorption coefficient of these composites by implementing the standard test method of sound absorption using an impedance tube. The effects of back cavity, air gaps, layering and graded thickness of different combination of the produced nanofiber mats were also investigated in this study. The experimental results revealed that these nanofiber mats demonstrate good sound absorption potential in low and medium frequency ranges (Up to 2000Hz). These cost-effective nanofiber mats have the potential of being used as sound barriers, to reduce noise transmission and keep it contained and/or as sound-dampening and sound-deadening materials in applications where space and volume savings are critical, especially in the electronic and aerospace industries.
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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 | 0.000 |
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".