Laser Diode-Induced in Situ Solid-State Synthesis of Graphitized Magnetic Nanostructures as Novel Nanozyme Catalysts

Abstract

Laser-induced graphene (LIG) is a revolutionizing nanomaterial as a tunable, 3D-networked, highly conductive, which makes it a perfect platform for hosting functional nanostructures and enhancing catalytic activity. In this work, a one-step Near-UV laser-assisted graphitization of magnetic nanomaterials —Fe₃O₄-LIG, Ag-Fe₃O₄-LIG, and Cu-Fe₃O₄-LIG— has been developed that forms a universal four-in-one nanozyme catalytic platform with peroxidase (POD), oxidase (OXD), catalase (CAT), and laccase (LAC) activities. The laser-driven process enables simultaneous graphitization, metal oxidation, and nanostructuration, offering an inexpensive, green, and scalable option for multi-step synthesis protocols. The nanozymes thus obtained were well characterized by scanning/transmission electron microscopy (SEM/TEM) for morphology, X-ray diffraction (XRD) for crystallinity, and Raman spectroscopy for graphitic character. An exhaustive dual characterization via optical as well as electrochemical methods was employed to assess and monitor the nanozyme material's catalytic activities. Out of all synthesised materials, Ag-Fe₃O₄-LIG exhibited the highest oxidase and peroxidase mimicking activity, whereas Cu-Fe₃O₄-LIG exhibited enhanced catalase and laccase-like activity. To investigate the laccase activity further, Cu-Fe₃O₄-LIG was employed with phenolic compounds like dopamine, adrenaline, and L-dopa, which oxidized the substrates into aminochromes. As a proof of concept, taking advantage of the electroactivity of adrenochrome, adrenaline was sensed using a 3D porous LIG sensor. The sensor exhibited great sensitivity in the cathodic region with a linear range of 0.5 to 10 μM and a detection limit of 0.23 μM. This work creates a groundbreaking nanozyme platform with broad implications in biosensing, demonstrating the strength of laser-assisted soft synthesis for future generations of enzyme-mimicking materials.