Magneto-Fluorescent Microrobots with Selective Detection Intelligence for High-Energy Explosives and Antibiotics in Aqueous Environments

Abstract

Fluorescence-based sensing is a straightforward and powerful technique with high sensitivity for the detection of a wide range of chemical and biological analytes. Integrating the high sensing capabilities of fluorescent probes with wireless navigation systems can enable the extension of their operational range, even in challenging scenarios with limited accessibility or involving hazardous substances. This study presents the development of molecularly engineered magneto-fluorescent microrobots based on the push-pull quinonoids by incorporating magnetic nanoparticles using a reprecipitation approach with the aim of detecting high-energy explosives and antibiotics in aqueous environments. The magnetic components in the microrobots offer remotely controlled navigability toward the intended target areas under the guidance of external magnetic fields. Upon interactions with either explosives (picric acid) or antibiotics (tetracycline), the microrobots' intrinsic fluorescence switches to a "fluorescence off" state, enabling material-based intelligence for sensing applications. The molecular-level interactions that underlie "on-off" fluorescence state switching upon engagement with target molecules are elucidated through extensive spectroscopy, microscopy, and X-ray diffraction analyses. The microrobots' selectivity toward target molecules is achieved by designing microrobots with amine functionalities capable of intermolecular hydrogen bonding with the acidic hydroxyl group of picric acid, leading to the formation of water-soluble charge transfer picrate complexes through proton transfer. Similarly, proton transfer interactions play a key role in tetracycline detection. The selective fluorescence switching performance of microrobots in fluidic channel experiments illustrates their selective sensing intelligence for target molecules in an externally controlled manner, highlighting their promising characteristics for sensing applications in real-world scenarios.Fluorescence-based sensing is a straightforward and powerful technique with high sensitivity for the detection of a wide range of chemical and biological analytes. Integrating the high sensing capabilities of fluorescent probes with wireless navigation systems can enable the extension of their operational range, even in challenging scenarios with limited accessibility or involving hazardous substances. This study presents the development of molecularly engineered magneto-fluorescent microrobots based on the push-pull quinonoids by incorporating magnetic nanoparticles using a reprecipitation approach with the aim of detecting high-energy explosives and antibiotics in aqueous environments. The magnetic components in the microrobots offer remotely controlled navigability toward the intended target areas under the guidance of external magnetic fields. Upon interactions with either explosives (picric acid) or antibiotics (tetracycline), the microrobots' intrinsic fluorescence switches to a "fluorescence off" state, enabling material-based intelligence for sensing applications. The molecular-level interactions that underlie "on-off" fluorescence state switching upon engagement with target molecules are elucidated through extensive spectroscopy, microscopy, and X-ray diffraction analyses. The microrobots' selectivity toward target molecules is achieved by designing microrobots with amine functionalities capable of intermolecular hydrogen bonding with the acidic hydroxyl group of picric acid, leading to the formation of water-soluble charge transfer picrate complexes through proton transfer. Similarly, proton transfer interactions play a key role in tetracycline detection. The selective fluorescence switching performance of microrobots in fluidic channel experiments illustrates their selective sensing intelligence for target molecules in an externally controlled manner, highlighting their promising characteristics for sensing applications in real-world scenarios.