Reusable, magnetic laser-induced graphene for efficient removal of organic pollutants from water
Source abstract
Abstract
The hybridization of graphene with magnetic nanoparticles has endowed graphene with increasing interest as the adsorbent for wastewater treatment. However, its fabrication often involves a multi-stepped chemical synthesis process. In this work, we demonstrate a facile, one-step, and solvent-free approach to fabricate Fe3O4 nanoparticle-anchored Laser-Induced Graphene (Fe3O4@LIG) as an efficient adsorbent by direct laser irradiation on a ferric acetylacetonate containing polybenzoxazine film. Raman and X-ray diffraction analysis confirm the graphene component in the adsorbent, and the morphology characterizations show that Fe3O4 nanoparticles are distributed uniformly on LIG with hierarchical meso- and macro-porous structures. Adsorption experiments indicate that Fe3O4@LIG can adsorb methylene blue (MB) from aqueous solutions in a fast and effective manner, with a maximum adsorption capacity up to 350.9 mg/g. The adsorption kinetics and isotherms are also investigated, which are well-described by the pseudo-second-order model and Langmuir model, respectively. Additionally, Fe3O4@LIG is also demonstrated with the efficient removal of a variety of organic solvents from water. The favorable adsorption behavior of Fe3O4@LIG is attributed to its unique porous structure and the molecular interactions with adsorbates. On the other hand, Fe3O4@LIG has high magnetic property, and therefore, it could be easily recovered from water and well regenerated for repeated use. With the efficient adsorption of organic pollutants, magnetic separability, and good recyclability, it is believed that the easy-fabricated Fe3O4@LIG has great potential applications in wastewater treatment. Graphical abstract
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Graphene and Nanomaterials Applications · Graphene research and applications · Carbon and Quantum Dots Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (85)
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