Interfacial electron-transfer engineering in electrostatically assembled ɑ-Fe2O3/MXene for sulfamethoxazole degradation and antibiotic-resistance risk mitigation

초록

The widespread occurrence of sulfamethoxazole (SMX) in aquatic environments raises increasing concerns regarding ecological toxicity and the dissemination of antibiotic resistance genes (ARGs). Here, we report an electrostatically self-assembled α-Fe2O3/MXene (HMe) heterostructure that couples interfacial electron-transfer engineering with peroxymonosulfate (PMS) activation for SMX degradation and mitigation of antibiotic-resistance-related residual risks. Density functional theory (DFT) calculations revealed interfacial electron redistribution from α-Fe2O3 toward MXene and a work-function difference of ∼2.2 eV, supporting interfacial electronic polarization and a built-in potential difference. The predicted work-function ordering was further supported by Kelvin probe force microscopy, while the upshifted Fe d-band center indicated a favorable electronic environment for PMS adsorption and activation. Under the selected conditions (0.2 g/L HMe and 1 mM PMS), the HMe/PMS system exhibited rapid SMX degradation (kobs = 0.121 min−1) at an initial concentration of 200 μg/L and maintained ∼96% removal in natural surface water and > 95% removal for seven of nine co-occurring antibiotics in municipal wastewater effluent. HMe maintained high SMX removal through approximately four consecutive cycles, although progressive deactivation became pronounced in the fifth cycle, while Fe and Ti leaching remained below 1%. Beyond parent-compound removal, treatment substantially reduced culturable SMX-tolerant bacteria and decreased the absolute abundances of the sulfonamide-resistance markers sul1 and sul2 by approximately 1.9–2.0 log. These findings demonstrate the potential of interfacial electron-transfer engineering for MXene-based PMS catalysts targeting antibiotic contamination and associated resistance-related risks in water treatment. © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

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