Microplastic (MP) contamination results in environmental degradation and human health impairments. The existing methods for MP separation from soil samples are either non-automated or semi-automated. This makes the extraction process operator dependent, time-consuming, and prone to contamination. The aim of this study was to develop a remotely controlled automated pre-treatment system for MP separation from soil samples. The proposed system integrates digestion, density separation, and overflow-based filtration in a single compact unit. An ESP32-based controller was used to automate the reagent flow rate, temperature, and processing time. The experiment was conducted under controlled laboratory conditions using 15 soil sub samples of 10 g each spiked with 1 g of a predefined amount of MP. The system was validated using low-density polyethylene (LDPE), polypropylene (PP) and polyvinyl chloride (PVC) with different size range. Each sub sample underwent digestion with 30% H 2 O 2 followed by density separation with ZnCl 2 . MP was finally separated by using the overflow-based filtration method and analyzed by Raman spectroscopy. Validation results showed recovery efficiency of 89 to 94% for LDPE, with a mean recovery efficiency of 91.8± 1.92% (mean ± SD), 95 to 99% for PP with a mean recovery efficiency of 97% ± 1.6% (mean ± SD) and 83 to 88% for PVC with a mean recovery efficiency of 85.2% ± 1.92% (mean ± SD). This proposed automated MP separation system for soil samples reduces manual intervention and process variability. This system also supports reliable environmental microplastic analysis and large-scale applications.
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Meenaakumari et al. (2026) studied this question.
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