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A Portable and Intelligent Dual-Modal Sensing System for Microplastics Detection
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Researchers built a portable dual-sensor system combining fiber optic fluorescence detection and impedance spectroscopy on a custom microfluidic chip, then trained a convolutional neural network on the combined signals to identify microplastics of varying sizes and concentrations with 100% accuracy alongside other dissolved substances.
The global pollution of microplastics (MPs) and their potential threats to human health necessitate the urgent development of efficient and portable detection technologies. This paper proposes a dual-modal sensing system for MPs identification. The system integrates fiber optic fluorescence (FOF) detection technology and direct current impedance spectroscopy (DCIS) detection technologies. Trace-level solution detection is achieved by using the design of a custom-designed microfluidic chip. FOF detection technology expands the fluorescence collection area with a multi-fiber structure. It improves detection sensitivity by precisely etching the tip of the fiber arrays. DCIS detection technology effectively captures and aggregates different substances within a microfluidic chip by generating a uniform electric field between two gold-plated copper wire electrodes. This improves the system's ability to detect different solutions, including MPs, Fe3+, NaCl solutions and water. Simultaneously, the convolutional neural network (CNN) model is used to intelligently analyze the fluorescence and impedance data. This enables the system to accurately detect MPs of multi-size and low-concentrations and different solutions. Additionally, this study demonstrated the high-precision identification of MPs, Fe3+, and NaCl solutions, achieving an accuracy of 100%. In this study, a new strategy for MPs detection with convenient and high accuracy is introduced.
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