Emerging MOF-based antibiotic detection methods in water environments: Recent advances, challenges, and prospects

The misuse and improper disposal of antibiotics lead to the emergence and dissemination of antibiotic-resistant bacteria and antibiotic resistance genes, posing serious threats to water environmental safety and human health. Thus, developing efficient detection methods for residual antibiotics in water environments is crucial for pollution control and public health safety. Traditional methods for detecting antibiotics including chromatographic methods, microbiological methods, and immunoassays, suffering from issues such as complex procedures, the requirement for skilled operators, and high costs, greatly limiting their applicability in in-situ monitoring. Recently, rapid detection methods, including colorimetric, fluorescence, biosensors, and paper-based detection, have received widespread attention, overcoming the aforementioned limitations and being widely adopted. Metal-organic frameworks (MOFs) possess distinct advantages, such as enrichment adsorption, catalytic degradation, and self-generated fluorescence, making them highly promising in the field of rapid antibiotic detection. Herein, the detection principles and recent advances in rapid antibiotic detection methods based on MOFs are summarized, and the unique strengths and potential of MOFs in the field of rapid antibiotic detection are highlighted. Notably, recent progress and challenges in high-throughput computing (HTC) and machine learning (ML) for screening MOFs for specific applications is discussed, and strategies for their use in MOF-based rapid antibiotic detection methods are proposed. This comprehensive review may further guide the development and optimization of antibiotic detection methods utilizing MOFs and promote their practical applications for sensing environmental antibiotics. • Recent advances of current antibiotic detection methods are listed. • The application potential of MOFs in rapid antibiotic detection are clarified. • Challenges and future trends for MOF-based antibiotic detection are highlighted. • The potential of machine learning for MOF-based antibiotic detection is emphasized.