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<p>Hydrogen peroxide (H₂O₂) oxidation has emerged as a promising and environmentally sustainable strategy for enhancing the surface chemistry and functional performance of biochar. This study systematically evaluated the effects of controlled H₂O₂ oxidation (1, 3, 10, 20 and 30%) on palm kernel shell biochar (OxyAChar). Among all treatments, 3% H₂O₂ produced the most favorable modification outcome, increasing methylene blue (MB) removal from 56.01 to 93.39% and improving water-holding capacity from 104.95 to 167.18% compared to pristine biochar. Equilibrium adsorption data fitted to Langmuir and Freundlich isotherm models showed that the Langmuir model provided the best fit (R<sup>2</sup> up to 0.9942), with OxyAChar-3 exhibiting the highest monolayer adsorption capacity (Q<sub>max</sub> = 0.5513&#xa0;mg&#xa0;g⁻<sup>1</sup>). BET adsorption–desorption isotherms further revealed that OxyAChar-3 possessed a well-developed pore network capable of strongly retaining MB molecules while enabling controlled desorption, highlighting its balanced sorption–release behavior. Mechanistically, the performance enhancement was attributed to H₂O₂-induced delignification and surface oxygenation, evidenced by enriched oxygen-containing functional groups, reductions in pH (–18.0%) and electrical conductivity (–37.0%), and notable increases in micropore (+ 28.8%) and mesopore (+35.4%) surface areas. In contrast, excessive oxidation disrupted pore architecture and reduced functional group density, resulting in diminished sorption performance. Comparative analysis with existing literature demonstrates that optimal H₂O₂ dosage is strongly dependent on feedstock characteristics and pyrolysis conditions. Overall, this study provides mechanistic insights and practical guidance for the optimization of OxyAChar and highlights opportunities for integrating artificial intelligence to predict optimal oxidation parameters and minimize experimental trial-and-error in biochar engineering.</p> Graphical abstract <p></p>

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Engineering biochar through surface oxygenation: a green approach for sustainable environmental applications

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月31日

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  • Ziwei Lee,
  • Rosazlin Abdullah,
  • Jamilah Syafawati Yaacob,
  • Noor Sharina Mohd Rosli

摘要

Hydrogen peroxide (H₂O₂) oxidation has emerged as a promising and environmentally sustainable strategy for enhancing the surface chemistry and functional performance of biochar. This study systematically evaluated the effects of controlled H₂O₂ oxidation (1, 3, 10, 20 and 30%) on palm kernel shell biochar (OxyAChar). Among all treatments, 3% H₂O₂ produced the most favorable modification outcome, increasing methylene blue (MB) removal from 56.01 to 93.39% and improving water-holding capacity from 104.95 to 167.18% compared to pristine biochar. Equilibrium adsorption data fitted to Langmuir and Freundlich isotherm models showed that the Langmuir model provided the best fit (R2 up to 0.9942), with OxyAChar-3 exhibiting the highest monolayer adsorption capacity (Qmax = 0.5513 mg g⁻1). BET adsorption–desorption isotherms further revealed that OxyAChar-3 possessed a well-developed pore network capable of strongly retaining MB molecules while enabling controlled desorption, highlighting its balanced sorption–release behavior. Mechanistically, the performance enhancement was attributed to H₂O₂-induced delignification and surface oxygenation, evidenced by enriched oxygen-containing functional groups, reductions in pH (–18.0%) and electrical conductivity (–37.0%), and notable increases in micropore (+ 28.8%) and mesopore (+35.4%) surface areas. In contrast, excessive oxidation disrupted pore architecture and reduced functional group density, resulting in diminished sorption performance. Comparative analysis with existing literature demonstrates that optimal H₂O₂ dosage is strongly dependent on feedstock characteristics and pyrolysis conditions. Overall, this study provides mechanistic insights and practical guidance for the optimization of OxyAChar and highlights opportunities for integrating artificial intelligence to predict optimal oxidation parameters and minimize experimental trial-and-error in biochar engineering.

Graphical abstract