Issue 18, 2026, Issue in Progress

Biosurfactant-driven desorption and remediation of heavy oil contaminated soils underpinned by molecular simulations and microbial dynamics

Abstract

This study integrates molecular dynamics simulations and bench-scale experiments to investigate the adsorption and desorption behaviors of heavy oil on five mineral substrates: SiO2, kaolinite, muscovite, and Ca2+-/Na+-montmorillonite. Adsorption followed Langmuir isotherms, with montmorillonite exhibiting the highest capacities (0.061–0.062 molecules per Å2 for aromatics in simulations; 0.086–0.091 g g−1 in bench-scale tests) and SiO2 the lowest (0.027 pcs per Å2; 0.013 g g−1). Among four biosurfactants evaluated—rhamnolipid, sophorolipid, trehalose lipid, and mannosylerythritol lipid–sophorolipid consistently achieved the greatest desorption efficiency, removing up to 99.63% of adsorbed oil from Na+-montmorillonite and 96.04% from field-contaminated soil. 16S rRNA and metagenomic sequencing revealed an increased abundance of hydrocarbon-degrading bacteria within the soil microbial community, highlighting a synergistic effect between biosurfactant-induced desorption and biodegradation. These findings underscore the critical roles of mineralogical properties, oil fraction characteristics, and biosurfactant selection in soil washing treatment. This work presents a viable and eco-friendly strategy for remediating crude oil-contaminated soils, with important implications for optimizing large-scale environmental restoration efforts.

Graphical abstract: Biosurfactant-driven desorption and remediation of heavy oil contaminated soils underpinned by molecular simulations and microbial dynamics

Supplementary files

Article information

Article type
Paper
Submitted
08 Dec 2025
Accepted
16 Mar 2026
First published
25 Mar 2026
This article is Open Access
Creative Commons BY license

RSC Adv., 2026,16, 16316-16328

Biosurfactant-driven desorption and remediation of heavy oil contaminated soils underpinned by molecular simulations and microbial dynamics

Q. Xiu, H. He, Z. Liu, X. Ou, Y. Meng, K. Zhao, Q. Yang, X. Zhang, Y. Hou, S. Yao, P. Gao and W. Xia, RSC Adv., 2026, 16, 16316 DOI: 10.1039/D5RA09479H

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