A breakthrough method developed by researchers at Nagoya University, in collaboration with the National Institute for Materials Sciences in Japan and other partners, promises to revolutionize the detection of microplastics in marine and freshwater environments. Published in the prestigious journal Nature Communications, the method combines optical analysis with machine learning techniques, offering a cost-effective and efficient solution to a pressing environmental challenge.
Detecting and identifying microplastics in water samples is crucial for environmental monitoring, yet it has long been hindered by the structural similarities between microplastics and natural organic compounds. Traditional detection methods often rely on complex separation techniques, which are not only time-consuming but also costly.
Dr. Olga Guselnikova of the National Institute for Materials Science (NIMS) highlights the novelty of their approach, stating, “Our new method can simultaneously separate and measure the abundance of six key types of microplastics—polystyrene, polyethylene, polymethylmethacrylate, polytetrafluoroethylene, nylon, and polyethylene terephthalate.”
The system utilizes a porous metal foam to capture microplastics from solution, coupled with surface-enhanced Raman spectroscopy (SERS) for optical detection. Dr. Joel Henzie of NIMS elaborates on the complexity of SERS data, noting, “The SERS data obtained is highly complex, but it contains discernible patterns that can be interpreted using modern machine learning techniques.”
To tackle this complexity, the team developed a neural network computer algorithm named SpecATNet. This algorithm learns to interpret optical measurements’ patterns, enabling rapid and accurate identification of target microplastics.
Professor Yusuke Yamauchi of Nagoya University underscores the method’s potential for environmental monitoring, emphasizing its applicability to samples obtained directly from the environment without pretreatment. He states, “Our procedure holds immense potential for monitoring microplastics in samples obtained directly from the environment, with no pretreatment required, while being unaffected by possible contaminants that could interfere with other methods.”
The researchers aim to democratize microplastic detection by creating inexpensive sensors and open-source algorithms. Currently, their system offers cost savings of 90% to 95% compared to commercially available alternatives. They aspire to further reduce costs and simplify replication, making the method accessible even in resource-limited settings.
Looking ahead, the researchers envision expanding SpecATNet’s capabilities to detect a broader range of microplastics and accept different spectroscopic data types beyond SERS. Their ultimate goal is to empower society in evaluating the impact of microplastic pollution on public health and aquatic ecosystems, paving the way for effective mitigation strategies.