https://hdl.handle.net/10012/21643

Microplastics (MPs), plastic particles smaller than 5~mm, are emerging as a significant environmental threat due to their widespread presence in ecosystems and potential health impacts. They originate from both primary sources, such as microbeads in personal care products, and secondary sources, like the degradation of larger plastics. MPs can accumulate in aquatic life, pose risks to food chains, and carry toxic pollutants. Despite their environmental significance, detecting MPs in natural settings is challenging due to complex particle characteristics and the limitations of current detection methods.

Several well-established methods have been developed for detecting and monitoring MPs in aqueous samples. Fourier-transform infrared and Raman spectroscopy are among the most widely used techniques due to their unique ability to identify chemical compositions at the molecular level. However, these methods generally require bulky, expensive equipment and skilled personnel. Additionally, they are offline techniques that involve time-consuming and labor-intensive sampling processes. As a result, there is growing demand for affordable and user-friendly MP sensing techniques suitable for on-site applications.

Electrical sensing methods-including resonance microwave spectroscopy, dielectric spectroscopy, high-frequency impedance spectroscopy, and electrical impedance spectroscopy-offer unique advantages for on-site detection of MPs due to their compact detection systems and scalability for multi-location testing. Each method interacts differently with the electrical properties of the material, offering diverse capabilities for MP detection.

Although most electrical sensing methods share similar working principles, resonance microwave spectroscopy stands out as a promising solution due to its broader frequency range (typically 0.1-100 GHz), enabling more versatile and precise detection of various particle types. Microwave sensing differentiates materials based on their permittivity, making it highly sensitive for detecting MPs, which typically exhibit permittivity values (∼2-3.5) distinct from their natural surrounding materials, such as water (∼80), wet sediments (∼10-30), and blood (∼50-60). Furthermore, microwave sensors can be integrated with planar technologies, such as printed circuit boards (PCBs) and microstrip antennas, to create compact, lightweight, durable, and cost-effective systems, offering a practical solution for continuous measurements in real-world applications.

This thesis presents the development of microwave-microfluidic sensors for detecting and characterizing MPs in aqueous environmental samples, offering a scalable and cost-effective solution for real-time monitoring. It starts with an exploratory study capable of only concentration monitoring and progresses to an enhanced sensing platform capable of monitoring both size and concentration. Then, continuous flow is added to the sensing platform to enable single-particle monitoring, which leads to MP size, type, and concentration characterization. In the final stage, the application of the microwave-microfluidic sensor extends from environmental to biomedical contexts.

The thesis begins by exploring the integration of microwave sensing with microfluidic platforms for detecting MPs in water. Experimental investigations were conducted using polyethylene microspheres of two different sizes (20 μm and 70 μm). The results indicate that the resonance frequency shift depends on particle size, concentration, and temperature. While experimental trends largely align with numerical simulations, the observed shifts were less pronounced than predicted, and the detection limits were higher than MP concentrations typically encountered in freshwater environments. These findings highlight the need for improved sensitivity and expanded applicability.

Building on this, a sensitivity-enhanced microwave sensing platform was introduced using coupled planar microwave resonators to characterize both the size and concentration of MPs in real time. The design incorporates an interdigital capacitor (IDC) structure with a traditional split-ring resonator (SRR) to enhance sensitivity. A disposable sample holder enables multiplex testing without cross-contamination, making the system field-deployable. The sensor was optimized through simulation and validated experimentally with MPs of three sizes (20 μm, 70 μm, and 275 μm) at various concentrations (100k, 1000k, and 10,000k particles/L). The results confirmed the sensor's ability to monitor particle size and concentration accurately. However, since experiments were conducted with fixed sample volumes in the microliter scale, continuous flow integration was needed to improve statistical robustness.

The next project introduces an innovative AI-powered microwave-microfluidic platform that enables comprehensive analysis of MPs. The system analyzes particle size, concentration, and type using a K-nearest neighbors (KNN) algorithm trained on raw sensor data. Environmental samples are prefiltered into specific size ranges, and single-particle detection enables precise quantification of MP concentration. This approach offers a scalable solution for real-time monitoring of MP contamination across a wide size range (20-300 μm).

The final project demonstrates the potential of the microwave-microfluidic sensor in biomedical diagnostics. The platform was adapted to detect biomarkers in biological samples, focusing on monitoring amylase levels in postoperative peritoneal drainage fluid as an indicator of anastomotic leakage. This work highlights the broader utility of the sensor system for cost-effective, non-invasive real-time monitoring in both environmental and clinical settings.

https://hdl.handle.net/10012/21643