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<p>The removal of hydrophobic emerging pollutants from water is a major environmental challenge. In this study, a fungal graphene oxide bio-adsorbent was synthesized, characterized, and evaluated for the removal of Benzo[a]pyrene (BaP) using a packed-bed column system. The structural and surface properties were investigated using XRD, SEM, FTIR, and BET analyses, revealing a highly porous, irregular morphology with hexagonal block-like structures, a high specific surface area of 670.15 m<sup>2</sup> g⁻<sup>1</sup>, a total pore volume of 0.3276 cm<sup>3</sup> g⁻<sup>1</sup>, and an average pore diameter of 1.956 nm. Elemental analysis (EDX) confirmed a carbon-rich (38 wt%) and oxygen-rich (42 wt%) skeleton. The notable presence of potassium (6 wt%), chlorine (1 wt%), and calcium (1 wt%) provides direct evidence of the fungal biomass and confirms the successful crosslinking of chitosan with CaCl₂, thereby verifying the successful formation of the composite. Process optimization using a Box–Behnken design identified pH, biocomposite dosage, contact time, and initial BaP concentration as significant variables. Under optimal conditions (pH 7.02, dosage 0.75 g L⁻<sup>1</sup>, BaP concentration 35 mg L⁻<sup>1</sup>, contact time 16h), the system achieved a BaP removal efficiency of 96.5%). Adsorption equilibrium followed both the Langmuir&#xa0;and the&#xa0;Freundlich&#xa0;isotherms,&#xa0;with a maximum Langmuir adsorption capacity of 38.9 ± 0.82 mg g⁻<sup>1</sup>, while the pseudo-second-order model best described the adsorption kinetics. The dynamic column behavior was accurately described by the Thomas and Yoon–Nelson models, confirming the stable breakthrough performance. An artificial neural network model was developed based on the experimental design to enhance the predictive capability, and it demonstrated excellent agreement between the predicted and experimental results. Reusability studies showed that the system retained 84.0 ± 1.8% of removal efficiency after four consecutive cycles, confirming good operational stability. Moreover, GC–MS analysis confirmed the enzymatic degradation of BaP into less toxic intermediates, demonstrating true bioremediation rather than mere physical adsorption. Overall, the fungal@GO packed-bed system exhibits high adsorption efficiency, strong modeling reliability, and promising potential for sustainable treatment of BaP-contaminated wastewater.</p>

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Sustainable Bioremediation of Benzo[a]pyrene Using a Trichoderma Graphene Oxide Bio-Composite in a Hybrid Packed Bed System

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  • 发表日期：

    2026年6月16日

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  • Mohammed T. M. H. Hamad

摘要

The removal of hydrophobic emerging pollutants from water is a major environmental challenge. In this study, a fungal graphene oxide bio-adsorbent was synthesized, characterized, and evaluated for the removal of Benzo[a]pyrene (BaP) using a packed-bed column system. The structural and surface properties were investigated using XRD, SEM, FTIR, and BET analyses, revealing a highly porous, irregular morphology with hexagonal block-like structures, a high specific surface area of 670.15 m2 g⁻1, a total pore volume of 0.3276 cm3 g⁻1, and an average pore diameter of 1.956 nm. Elemental analysis (EDX) confirmed a carbon-rich (38 wt%) and oxygen-rich (42 wt%) skeleton. The notable presence of potassium (6 wt%), chlorine (1 wt%), and calcium (1 wt%) provides direct evidence of the fungal biomass and confirms the successful crosslinking of chitosan with CaCl₂, thereby verifying the successful formation of the composite. Process optimization using a Box–Behnken design identified pH, biocomposite dosage, contact time, and initial BaP concentration as significant variables. Under optimal conditions (pH 7.02, dosage 0.75 g L⁻1, BaP concentration 35 mg L⁻1, contact time 16h), the system achieved a BaP removal efficiency of 96.5%). Adsorption equilibrium followed both the Langmuir and the Freundlich isotherms, with a maximum Langmuir adsorption capacity of 38.9 ± 0.82 mg g⁻1, while the pseudo-second-order model best described the adsorption kinetics. The dynamic column behavior was accurately described by the Thomas and Yoon–Nelson models, confirming the stable breakthrough performance. An artificial neural network model was developed based on the experimental design to enhance the predictive capability, and it demonstrated excellent agreement between the predicted and experimental results. Reusability studies showed that the system retained 84.0 ± 1.8% of removal efficiency after four consecutive cycles, confirming good operational stability. Moreover, GC–MS analysis confirmed the enzymatic degradation of BaP into less toxic intermediates, demonstrating true bioremediation rather than mere physical adsorption. Overall, the fungal@GO packed-bed system exhibits high adsorption efficiency, strong modeling reliability, and promising potential for sustainable treatment of BaP-contaminated wastewater.