# Fabrication and optimization of magnetic amino-functionalized polyacrylonitrile nanocomposites for enhanced copper removal from aqueous media

## TL;DR

A new magnetic nanocomposite was developed to efficiently remove copper ions from water, showing strong potential for water purification.

## Contribution

Development of a magnetic amino-functionalized polyacrylonitrile nanocomposite with optimized copper removal efficiency.

## Key findings

- The nanocomposite achieved a maximum copper removal efficiency of 78.29% at pH 5.5.
- The Langmuir isotherm model best described the adsorption behavior with a maximum adsorption capacity of 5.65 mg/g.
- Optimization using RSM and an ANN model improved copper removal to 51.51 mg/L with high correlation (R² = 0.996).

## Abstract

A magnetic amino polyacrylonitrile nanocomposite (MAPA) was prepared and applied as an adsorbent to eliminate Cu2+ ions from aqueous media through batch experiments. Its physicochemical properties were examined using most known characterization methods. The optimal removal efficiency was obtained at pH 5.5. Adsorption studies were performed under different experimental conditions, considering initial copper concentration, solution pH, and temperature. The maximum removal efficiency attained was 78.29%, while the corresponding maximum adsorption capacity (\documentclass[12pt]{minimal}
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\begin{document}$$\:{q}_{m}$$\end{document}) was calculated to be 5.65 mg/g. The equilibrium adsorption data were analyzed using different isotherm models, among which the Langmuir (LIM) exhibited the best correlation with the experimental results. Kinetic behavior was also assessed through various known models. Among these models, the pseudo-second-order (PSO) demonstrated the strongest correlation (R² = 1.0), indicating that it most precisely represents the adsorption behavior. In summary, the fabricated MAPA demonstrated strong potential for efficiently eliminating Cu2+ ions from aqueous solutions. A maximum removal percentage of 51.51 mg/L of Cu2+ ions and 5.12 g of MAPA could be attained by using a Response Surface Methodology (RSM) optimization of the adsorption parameters. The optimized BPNN of ANN model stopped after 4 epochs with the best validation of 5.474, with an overall R2 of 0.996.