Browse by author
Lookup NU author(s): Professor Lidija SillerORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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.
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.
Altmetrics provided by Altmetric
