AI Article Synopsis

  • * Various analytical techniques like Raman spectroscopy and SEM were used to synthesize and optimize GPF, which was then tested for its sound absorption capabilities compared to traditional melamine foam.
  • * Results showed that a specific GO-PEI ratio of 1:3 in GPF led to significant improvements in sound absorption, with a peak coefficient of 0.97, and the Johnson-Champoux-Allard (JCA) model offered a better understanding of

Article Abstract

High-frequency noise exceeding 1 kHz has emerged as a pressing public health issue in industrial and occupational settings. In response to this challenge, the present study explores the development of a graphene oxide-polyethyleneimine (GO-PEI) foam (GPF) featuring a hierarchically porous structure. The synthesis and optimization of GPF were carried out using a range of analytical techniques, including Raman spectroscopy, scanning electron microscopy (SEM), Braunauer-Emmett-Teller (BET) analysis, X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FT-IR). To evaluate its acoustic properties, GPF was subjected to sound absorption tests over the 1000-6400 Hz frequency range, where it was benchmarked against conventional melamine foam. The findings demonstrated that GPF with a GO-to-PEI composition ratio of 1:3 exhibited enhanced sound absorption performance, with improvements ranging from 15.0% to 118%, and achieved a peak absorption coefficient of 0.97. Additionally, we applied the Johnson-Champoux-Allard (JCA) model to further characterize the foam's acoustic behavior, capturing key parameters such as porosity, flow resistivity, and viscous/thermal losses. The JCA model exhibited a superior fit to the experimental data compared to traditional models, providing a more accurate prediction of the foam's complex microstructure and sound absorption properties. These findings underscore GPF's promise as an efficient solution for mitigating high-frequency noise in industrial and environmental applications.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548673PMC
http://dx.doi.org/10.3390/polym16212983DOI Listing

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