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<p>The urgent need for efficient, cost-effective, and regenerable materials for CO<sub>2</sub> capture is key to addressing climate change. A novel hydrogel composed of polyacrylamide, alpha-olefin sulfonate, and chromium (III) acetate was synthesized and investigated for its CO<sub>2</sub> adsorption performance for multiple cycles. The inclusion of AOS enhanced gel’s surface activity and porosity, while Cr(III) acetate served as cross-linker, providing structural stability and active CO<sub>2</sub> binding sites. Regeneration was achieved by mild heating at 50–60&#xa0;°C, and the material was reused for 5 cycles, retaining a CO<sub>2</sub> adsorption capacity of 0.60 mmol g⁻¹. Hydrogel maintained excellent mechanical integrity and adsorption capacity, highlighting its suitability for sustainable CO<sub>2</sub> capture applications. To further enhance performance, an artificial neural network (ANN) based optimization framework was employed to identify optimal gel compositions. The predictive capabilities of Levenberg-Marquardt (LM), particle swarm optimization (PSO), and genetic algorithm (GA) assisted ANN models were compared. Among these, the ANN-PSO model exhibited superior predictive accuracy, achieving a correlation coefficient of 89.73%, outperforming ANN-GA (84.16%) and ANN-LM (75.77%). The strong agreement between experimental and predicted results highlights the effectiveness of ANN-PSO as a powerful tool for optimizing hydrogel-based CO<sub>2</sub> capture systems.</p>

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Optimization-Driven Synergistic CO2 Capture Using a Polyacrylamide-Based Hydrogel with Enhanced Stability and Regenerability

  • 期刊论文

  • 发表日期：

    2026年6月3日

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  • Uma Sankar Behera,
  • Ganpati S Nayak,
  • Dharmendra Kumar Bal,
  • Hun-Soo Byun

摘要

The urgent need for efficient, cost-effective, and regenerable materials for CO2 capture is key to addressing climate change. A novel hydrogel composed of polyacrylamide, alpha-olefin sulfonate, and chromium (III) acetate was synthesized and investigated for its CO2 adsorption performance for multiple cycles. The inclusion of AOS enhanced gel’s surface activity and porosity, while Cr(III) acetate served as cross-linker, providing structural stability and active CO2 binding sites. Regeneration was achieved by mild heating at 50–60 °C, and the material was reused for 5 cycles, retaining a CO2 adsorption capacity of 0.60 mmol g⁻¹. Hydrogel maintained excellent mechanical integrity and adsorption capacity, highlighting its suitability for sustainable CO2 capture applications. To further enhance performance, an artificial neural network (ANN) based optimization framework was employed to identify optimal gel compositions. The predictive capabilities of Levenberg-Marquardt (LM), particle swarm optimization (PSO), and genetic algorithm (GA) assisted ANN models were compared. Among these, the ANN-PSO model exhibited superior predictive accuracy, achieving a correlation coefficient of 89.73%, outperforming ANN-GA (84.16%) and ANN-LM (75.77%). The strong agreement between experimental and predicted results highlights the effectiveness of ANN-PSO as a powerful tool for optimizing hydrogel-based CO2 capture systems.