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High-Efficiency Oxidative-Adsorptive Desulfurization of Diesel Fuel Using a Hydrophobic Cellulose-Silica Hybrid Aerogel

Lookup NU author(s): Professor Lidija SillerORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

A novel cellulose-silica hybrid aerogel was synthesised via a sol-gel technique and investigated as a sustainable adsorbent for oxidative-adsorptive desulfurization (OADS) of dibenzothiophene (DBT) from diesel fuel. The material demonstrated excellent physicochemical properties such as a high surface area of 357.9 m²/g, mesoporous structure of 7.45 nm average pore size, and strong hydrophobicity interpreted by its high contact angle of 129.4° combined with tailored amphiphilic surface chemistry. Collectively, they enabled the selective and efficient uptake of polar oxidised sulfur species (DBTO₂). The synergistic effects of temperature (25-100 °C) and ultrasonic frequency (20-60 kHz) on the removal efficiency of sulfur were systematically evaluated. A maximum removal efficiency of 90.3% was achieved at 100 °C and 60 kHz in addition to an apparent adsorption capacity of 529.9 mg/g which surpasses previously reported materials under diesel fuel conditions. Kinetic analysis revealed that the pseudo-second-order model best described the adsorption process and indicated a chemisorption-dominant mechanism whereas the intraparticle diffusion model suggested a multistep adsorption process. Thermodynamic parameters confirmed the spontaneous (ΔG° < 0) and endothermic (ΔH° > 0) nature of the process. The hybrid aerogel preserved over 93% of its initial performance after five reuse cycles, which confirms its excellent structural resilience and regeneration ability. Additionally, a two-hidden-layer artificial neural network (ANN) trained by the Bayesian regularisation algorithm achieved excellent predictive accuracy (R² = 0.9995 for training, R² = 0.9769 for testing, MSE = 0.0905 for training, MSE = 2.4386 for testing), offering a valuable tool for process optimization. Overall, this study proposes a scalable, metal-free, and high-performance desulfurization platform, combining green material design with superior cavitation-enhanced oxidation and machine learning-based prediction.

Publication metadata

Author(s): Shihab MA, Hassan KT, Humadi JI, Saud AN, Madhkali N, Alzahrani SS, Siller L

Publication type: Article

Publication status: Published

Journal: Fuel

Year: 2026

Volume: 407 Part D

Pages: 137565

Print publication date: 01/03/2026

Online publication date: 19/11/2025

Acceptance date: 14/11/2025

Date deposited: 10/02/2026

ISSN (print): 0016-2361

ISSN (electronic): 1873-7153

Publisher: Elsevier

URL: https://doi.org/10.1016/j.fuel.2025.137565

DOI: 10.1016/j.fuel.2025.137565

ePrints DOI: 10.57711/dakx-sy42

Data Access Statement: Data will be made available on request.

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