Fen Bilimleri Enstitüsü

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Browsing Fen Bilimleri Enstitüsü by Author "0000-0002-6909-723X"

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    OPTICAL SCATTERING BASED RANDOM FOREST ASSISTED PARTICLE DETECTION AND CLASSIFICATION
    (Abdullah Gül Üniversitesi / Fen Bilimleri Enstitüsü, 2023) Genç, Sinan; 0000-0002-6909-723X; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
    Microplastics, tiny plastic particles with sizes smaller than 5 mm., are often found in oceans, rivers, lakes, and atmosphere due to plastic pollution. Microplastics releasing toxic chemicals threaten the environment and harm the aquatic life and humans. Especially, the accumulation of microplastics can have detrimental effects on the food chain as a result of larger organisms consuming smaller organisms. Detecting the microplastics is crucial but also challenging. Over the years, researchers have developed different detection methods. One of the standard methods is using spectroscopy tools such as Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy. These techniques can identify the chemical composition of microplastics, which can help determine their sources and potential impacts. Another method is the use of microscopy, which allows for the visualization and counting of microplastics in samples. However, these techniques require costly infrastructure, and these instruments being large in size significantly limits the mobility. As a remedy to the cost and mobility problems, in this thesis, we propose and demonstrate a low-cost, portable system to detect size, concentration, and refractive index of microplastics. Our system comprises of low-cost and low-weight components which are utilized for recording the scattering patterns of microplastics in aqueous media. We demonstrate successful predictions of the size and refractive index of microparticles at a given wavelength using a Random Forest Algorithm which relates the measured scattering pattern with the Mie theory. We further employ the refractive index information at various wavelengths for determining the material type of microplastics. We believe that our proposed system enabling an easy, fast, low-cost, and on-site detection of microplastics will be a beneficial tool for the fight against microplastics in the environment.