Advances in single-molecule junctions for the detection of hazardous pollutants

Abstract

With the continuous development of the global economy, environmental pollution has emerged as a significant challenge impeding sustainable economic development. Consequently, the advancement of highly sensitive pollutant detection technologies plays a critical role in analyzing and mitigating environmental issues. Single-molecule junctions (SMJs), by constructing electrode–molecule–electrode junctions, directly convert specific recognition events of individual pollutant molecules into measurable electrical conductance signals, fundamentally enabling ultra-high sensitivity capable of reaching the single-molecule detection limit, along with real-time and label-free detection capabilities. This work begins by outlining the core technological platforms, primarily including two categories of single-molecule junction fabrication strategies, namely, dynamic methods (e.g., scanning tunneling microscopy break junction and mechanically controllable break junction) and static methods (e.g., electromigration break junction and carbon-based molecular junction), and analyzes their respective characteristics. Subsequently, it focuses on reviewing the specific applications of this technology in detecting inorganic pollutants (heavy metal ions, inorganic anions, and acidity/alkalinity), organic pollutants (explosives, pesticides, and dye molecules), and microbial-related pollutants (biomacromolecules). These applications demonstrate its exceptional detection sensitivity, ranging from femtomole to attomole levels, and excellent selectivity, with some techniques already validated in real environmental samples and clinical specimens. This work also identifies challenges in practical applications, such as mass transport limitations, interfacial stability, interference from complex matrices, and data analysis issues. Finally, it outlines future development directions, including the development of parallel array sensing, intelligent responsive probes, integration of artificial intelligence for data analysis, and promoting technological standardization and interdisciplinary collaboration, thereby providing guidance and reference for the subsequent development of single-molecule detection sensors.