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<p>The uncontrolled release of phenolic-rich effluents from olive oil production represents a major environmental challenge, yet efficient removal of these hydrophilic micropollutants remains elusive. Developing cost-effective, high-performance processes for remediating olive oil mill wastewater is a critical priority. Among existing methods, adsorption is particularly attractive due to its simplicity, efficiency, and effectiveness in removing phenolic compounds. This work aims to advance adsorption-based solutions by designing novel functional materials for the selective removal of phenolic pollutants from olive oil extraction wastewater. To achieve this goal, we report the design and synthesis of six novel pyridinic poly(arylene ether) adsorbents (P1–P6). These materials were prepared through polycondensation reactions between specifically engineered pyridine–gem-diphenol monomers and a series of difluoro aromatic derivatives, enabling systematic modulation of polymer structure and functionality for enhanced adsorption performance. Structural analyses by NMR and MALDI-ToF confirmed the coexistence of linear and cyclic architectures, while thermal studies revealed high glass transition temperatures (up to 258&#xa0;°C) and remarkable stability (decomposition &gt; 390&#xa0;°C). Adsorption experiments against eleven representative aromatic pollutants demonstrated pronounced structure–property relationships: sulfone–pyridine rich polymers (P1, P2) efficiently captured polar phenolics such as hydroxytyrosol and tyrosol, whereas nonpolar backbones (P3, P4) favored hydrophobic analytes, and fluorinated morpholine derivatives (P5, P6) exhibited intermediate selectivity toward mid-polar phenolic acids. To extend experimental insights, we developed a deep-learning framework trained on adsorption data on P1 and P2, which achieved high predictive accuracy (RMSE = 6.96% for P1 and 7.66% for P2) and successfully extrapolated adsorption efficiencies for the eleven phenolic compounds. Together, these results establish functionalized pyridinic poly(arylene ether)s as a versatile and tunable platform for the selective removal of phenolic micropollutants, olive oil-by products, from water, and demonstrate how integrating polymer design with machine learning accelerates the discovery of next-generation sorbents for environmental remediation.</p>

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Engineering pyridinic poly(arylene ether)s for olive oil by-product adsorption: experiments coupled with AI

  • 期刊论文

  • 发表日期：

    2026年4月10日

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  • Eya Hadrich,
  • Taha Chabbah,
  • Saber Chatti,
  • Houyem Abderrazak,
  • Ibtissem Jlalia,
  • Régis Mercier,
  • Hamdi Ben Halima,
  • Nicole Jaffrezic-Renault,
  • Catherine Marestin,
  • Hatem Ben Romdhane

摘要

The uncontrolled release of phenolic-rich effluents from olive oil production represents a major environmental challenge, yet efficient removal of these hydrophilic micropollutants remains elusive. Developing cost-effective, high-performance processes for remediating olive oil mill wastewater is a critical priority. Among existing methods, adsorption is particularly attractive due to its simplicity, efficiency, and effectiveness in removing phenolic compounds. This work aims to advance adsorption-based solutions by designing novel functional materials for the selective removal of phenolic pollutants from olive oil extraction wastewater. To achieve this goal, we report the design and synthesis of six novel pyridinic poly(arylene ether) adsorbents (P1–P6). These materials were prepared through polycondensation reactions between specifically engineered pyridine–gem-diphenol monomers and a series of difluoro aromatic derivatives, enabling systematic modulation of polymer structure and functionality for enhanced adsorption performance. Structural analyses by NMR and MALDI-ToF confirmed the coexistence of linear and cyclic architectures, while thermal studies revealed high glass transition temperatures (up to 258 °C) and remarkable stability (decomposition > 390 °C). Adsorption experiments against eleven representative aromatic pollutants demonstrated pronounced structure–property relationships: sulfone–pyridine rich polymers (P1, P2) efficiently captured polar phenolics such as hydroxytyrosol and tyrosol, whereas nonpolar backbones (P3, P4) favored hydrophobic analytes, and fluorinated morpholine derivatives (P5, P6) exhibited intermediate selectivity toward mid-polar phenolic acids. To extend experimental insights, we developed a deep-learning framework trained on adsorption data on P1 and P2, which achieved high predictive accuracy (RMSE = 6.96% for P1 and 7.66% for P2) and successfully extrapolated adsorption efficiencies for the eleven phenolic compounds. Together, these results establish functionalized pyridinic poly(arylene ether)s as a versatile and tunable platform for the selective removal of phenolic micropollutants, olive oil-by products, from water, and demonstrate how integrating polymer design with machine learning accelerates the discovery of next-generation sorbents for environmental remediation.