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<p>A Mg<sup>2+</sup> and Cl<sup>−</sup> selective filtration membrane was synthesized in the current study using Amoxicillin functionalized PVC/α-Fe<sub>2</sub>O<sub>3</sub>, and it was further examined using FTIR, UV-visible, HR-TEM, SEM, DSC, and TGA like tools. Phase inversion technology was used in the membrane’s fabrication. The creation of the nanocomposite was verified by the appearance of metal oxide stretching in the FT-IR spectrum at 430&#xa0;cm<sup>− 1</sup>. The microvoids on the membrane surface were partially sealed during the seawater filtering. Both conductivity and total dissolved solids were shown to decrease with longer filtering times, indicating that the membranes were properly desalinating the water. The maximum pure water flux value was determined as 428&#xa0;L/M<sup>2</sup>.h for nanocomposite membrane. Both membrane systems’ filtering effectiveness dropped after the sixth cycle. Various machine learning models like, LR, PNR, GBT, SVM and RF were used for the prediction of porosity of the membrane. The LR based machine learning model produced excellent % porosity prediction results. The acquired results are thoroughly examined and contrasted with the published literature.</p>

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Structural modification, characterization and porosity prediction by machine learning process of PVC-g-Amoxicillin/α-Fe2O3 nanocomposite membrane

  • 期刊论文

  • 发表日期：

    2026年4月13日

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  • R. Anbarasan,
  • K. Roobini,
  • M. F. Parveen,
  • R. Yoga,
  • S. Anusuya,
  • M. Akshaya

摘要

A Mg2+ and Cl− selective filtration membrane was synthesized in the current study using Amoxicillin functionalized PVC/α-Fe2O3, and it was further examined using FTIR, UV-visible, HR-TEM, SEM, DSC, and TGA like tools. Phase inversion technology was used in the membrane’s fabrication. The creation of the nanocomposite was verified by the appearance of metal oxide stretching in the FT-IR spectrum at 430 cm− 1. The microvoids on the membrane surface were partially sealed during the seawater filtering. Both conductivity and total dissolved solids were shown to decrease with longer filtering times, indicating that the membranes were properly desalinating the water. The maximum pure water flux value was determined as 428 L/M2.h for nanocomposite membrane. Both membrane systems’ filtering effectiveness dropped after the sixth cycle. Various machine learning models like, LR, PNR, GBT, SVM and RF were used for the prediction of porosity of the membrane. The LR based machine learning model produced excellent % porosity prediction results. The acquired results are thoroughly examined and contrasted with the published literature.