Leveraging large language models (LLMs) to enhance student inquiry in a nanochemistry teaching laboratory: a Fenton-like oxidation using recyclable ferrite nanoparticle (NP) catalysts

Abstract

This laboratory module presents a synergistic integration of nanomaterials chemistry and computational literacy by utilizing Large Language Model (LLM) agents as pedagogical partners in the study of transition metal ferrite nanoparticles (NPs). Students investigate the tunable magnetic and electronic properties of spinel-structured MFe₂O₄ (M = Mn, Co, Ni) synthesized using co-precipitation and stabilized with Tween-20. Rather than following a static protocol, students engage in an inquiry-based workflow where LLMs facilitate the optimization of experimental parameters and the interpretation of complex characterization data (spectroscopy, powder X-ray diffraction, electron microscopy, and dynamic light scattering). This partnership extends to the application of these NPs in Fenton-like oxidative catalysis for pollutant degradation, emphasizing both catalytic efficiency and material recyclability. By bridging benchtop experimentation with LLM-guided analysis, the module aligns with UN Sustainable Development Goals 4, 6, and 10, fostering independent scientific inquiry and the digital fluency required for modern, sustainable research. This framework empowers undergraduates to take ownership of their learning, transforming a robust nanomaterials synthesis project into an authentic, AI-augmented research experience.