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<p>The environmental and economic issues of conventional silica mining necessitate sustainable alternatives. This study investigates silica nanoparticle extraction from sugarcane bagasse and groundnut shell using an eco-friendly extraction-precipitation method. Comprehensive characterization via SEM, EDX, FTIR, and XRD confirmed amorphous silica synthesis with particle sizes of 25–49&#xa0;nm and high surface area. Functionalization with trichlorodocecylsilane produced hydrophobic nanocoatings exhibiting water contact angles exceeding 140°, demonstrating excellent protective properties. Response Surface Methodology (RSM) employing central composite design optimized extraction parameters, identifying optimal conditions at 1.68&#xa0;M NaOH, 77.5&#xa0;°C, and 92&#xa0;min, yielding 89% silica with 96% purity (R<sup>2</sup> &gt; 0.95). A supervised machine learning model based on linear regression successfully predicted silica yield with R<sup>2</sup> = 0.97 and MAE = 1.26%, validating the integration of data-driven approaches with experimental frameworks. This integrated methodology combining sustainable extraction, statistical optimization, and predictive modeling establishes a robust framework for bio-derived nanomaterial synthesis, aligning with reducing environmental impact.</p>

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Sustainable Extraction and ML-Based Yield Prediction of Silica Nanoparticles from Sugarcane Bagasse and Groundnut Shell for Functional Surface Coatings

  • 期刊论文

  • 发表日期：

    2025年11月14日

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  • Shalini Kannan,
  • Thavasilingam K,
  • Muruga Boopathi M,
  • Karthi Marimuthu

摘要

The environmental and economic issues of conventional silica mining necessitate sustainable alternatives. This study investigates silica nanoparticle extraction from sugarcane bagasse and groundnut shell using an eco-friendly extraction-precipitation method. Comprehensive characterization via SEM, EDX, FTIR, and XRD confirmed amorphous silica synthesis with particle sizes of 25–49 nm and high surface area. Functionalization with trichlorodocecylsilane produced hydrophobic nanocoatings exhibiting water contact angles exceeding 140°, demonstrating excellent protective properties. Response Surface Methodology (RSM) employing central composite design optimized extraction parameters, identifying optimal conditions at 1.68 M NaOH, 77.5 °C, and 92 min, yielding 89% silica with 96% purity (R2 > 0.95). A supervised machine learning model based on linear regression successfully predicted silica yield with R2 = 0.97 and MAE = 1.26%, validating the integration of data-driven approaches with experimental frameworks. This integrated methodology combining sustainable extraction, statistical optimization, and predictive modeling establishes a robust framework for bio-derived nanomaterial synthesis, aligning with reducing environmental impact.