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<p>This study reports the development and optimization of hybrid bio-nanopolyphenol coatings derived from <i>Piper betle</i> (PB) and <i>Terminalia catappa</i> (TC) extracts for functional finishing of cotton fabrics, targeting multifunctional UV protection and antibacterial performance through an integrated Artificial Neural Network (ANN) and Response Surface Methodology (RSM) framework. Polyphenol extraction and nanoencapsulation were optimized using a Box–Behnken experimental design supported by ANN-assisted trend analysis. Under optimal extraction conditions (solvent-to-material ratio 1:10, 50&#xa0;°C, 37.5&#xa0;min), a total polyphenol content of approximately 190&#xa0;mg GAE g<sup>−</sup><sup>1</sup> (based on dried extract) and DPPH radical scavenging activity exceeding 85% were achieved. The nanoencapsulation process yielded stable nanoparticles with sizes of 130–140&#xa0;nm, encapsulation efficiency above 90%, and controlled release behavior with half-release times (t<sub>50</sub>) exceeding 20&#xa0;h. The optimized bio-nanopolyphenol coating (PB:TC = 50:50) exhibited uniform surface coverage and strong interfacial interactions with cotton cellulose, as confirmed by XPS and XRD analyses, resulting in excellent textile-level UV protection with UPF values of approximately 58–62. Antibacterial performance evaluated quantitatively using the AATCC 100 method demonstrated high bacterial reduction against <i>Escherichia coli (E. coli)</i> and <i>Staphylococcus aureus</i> (<i>S. aureus</i>), while complementary agar diffusion tests (AATCC 147) confirmed clear inhibition zones around coated fabrics. The functional properties were largely retained after 20 laundering cycles, indicating good coating durability. ANN predictive performance was evaluated using error-based metrics (RMSE and MAE) rather than R<sup>2</sup> to ensure robust interpretation under limited experimental data conditions. The ANN model demonstrated satisfactory trend-level predictive capability for key nanoformulation and textile performance parameters, enabling effective integration with RSM and Pareto-based multi-objective optimization. Overall, the results demonstrate that synergistic interactions among phenolic compounds, including eugenol, quercetin, catechin, and tannic acid, enhance coating adhesion, structural stability, and controlled release, providing a durable bio-based coating platform with effective UV-protective and antibacterial performance suitable for sustainable textile&#xa0;applications.</p>

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Bio-Nanopolyphenol Coatings from Piper betle and Terminalia catappa on Cotton Cellulose: AI–RSM integrated optimization for enhanced UV and antibacterial performance

  • 期刊论文

  • 发表日期：

    2026年4月11日

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  • Tuan Anh Nguyen,
  • Trong Tuan Nguyen,
  • Huu Trung Dang

摘要

This study reports the development and optimization of hybrid bio-nanopolyphenol coatings derived from Piper betle (PB) and Terminalia catappa (TC) extracts for functional finishing of cotton fabrics, targeting multifunctional UV protection and antibacterial performance through an integrated Artificial Neural Network (ANN) and Response Surface Methodology (RSM) framework. Polyphenol extraction and nanoencapsulation were optimized using a Box–Behnken experimental design supported by ANN-assisted trend analysis. Under optimal extraction conditions (solvent-to-material ratio 1:10, 50 °C, 37.5 min), a total polyphenol content of approximately 190 mg GAE g−1 (based on dried extract) and DPPH radical scavenging activity exceeding 85% were achieved. The nanoencapsulation process yielded stable nanoparticles with sizes of 130–140 nm, encapsulation efficiency above 90%, and controlled release behavior with half-release times (t50) exceeding 20 h. The optimized bio-nanopolyphenol coating (PB:TC = 50:50) exhibited uniform surface coverage and strong interfacial interactions with cotton cellulose, as confirmed by XPS and XRD analyses, resulting in excellent textile-level UV protection with UPF values of approximately 58–62. Antibacterial performance evaluated quantitatively using the AATCC 100 method demonstrated high bacterial reduction against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), while complementary agar diffusion tests (AATCC 147) confirmed clear inhibition zones around coated fabrics. The functional properties were largely retained after 20 laundering cycles, indicating good coating durability. ANN predictive performance was evaluated using error-based metrics (RMSE and MAE) rather than R2 to ensure robust interpretation under limited experimental data conditions. The ANN model demonstrated satisfactory trend-level predictive capability for key nanoformulation and textile performance parameters, enabling effective integration with RSM and Pareto-based multi-objective optimization. Overall, the results demonstrate that synergistic interactions among phenolic compounds, including eugenol, quercetin, catechin, and tannic acid, enhance coating adhesion, structural stability, and controlled release, providing a durable bio-based coating platform with effective UV-protective and antibacterial performance suitable for sustainable textile applications.