Optimization and Modeling of Sound-Absorption Properties of Natural Fibers for Acoustical Application
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Bibliographic record
Abstract
Kenaf fibers have long been utilized because of their remarkable properties, such as availability, durability, and strength. Recently, they have also been used as sound-absorbing composites for noise control purposes. This paper investigates the possibility of improving the tensile strength of kenaf fibers through optimizing the alkaline treatment process. It also considers the effects of such optimization on the acoustic absorption coefficients of the samples fabricated from these treated kenaf fibers as well as the applicability of the numerical model to predict the acoustic absorption. Having employed the response surface methodology (RSM) to optimize the alkaline treatment process and achieve optimal conditions for the kenaf fibers, the scanning electron microscopy (SEM) and tensile test were used to study and compare the morphological and tensile properties of raw fibers (nonoptimal) and the fibers treated in optimal conditions. Several cylindrical samples with constant thickness and density (30 mm and 200 kg/m3) were then made of fibers treated in optimal conditions. The sound absorption coefficient, porosity, and airflow resistivity of these samples were measured based using ISO 10534–2 (impedance tube system), SEM and ASTM C423-09A, respectively. The results demonstrated that the tensile strength of optimally treated fibers increased by 182.39%. The acoustic absorption coefficients of the samples fabricated from these fibers were also higher at all frequencies (low-, mid-, and high-frequency range) compared with samples made of untreated fibers in a way that the Sound Absorption Average value of the former increased by 17.97%. Moreover, it was found that inverted Dunn and Davern model via Nelder-Mead simplex method well follows the sound absorption pattern from the experimental results in the overall frequency range. The use of multifaceted improvement approaches for natural materials such as kenaf fibers increases the usability of these materials as sustainable and eco-friendly alternatives in the engineering process of manufacturing sound-absorbing materials.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it