Optical analysis and machine learning techniques can now readily detect microplastics in marine and freshwater environments using inexpensive porous metal substrates.
Details of the method, developed by researchers at Nagoya University with collaborators at the National Institute for Materials Science s in Japan and others, are published in the journal Nature Communications.
The system uses a porous metal foam to capture microplastics from solution and detect them optically using a process called surface-enhanced Raman spectroscopy ."The SERS data obtained is highly complex," explains Dr. Joel Henzie of NIMS,"but it contains discernible patterns that can be interpreted using modern machine learning techniques."
The researchers hope their innovation will greatly assist society in evaluating the significance of microplastic pollution on public health and the health of all organisms in marine and freshwater environments. By creating inexpensive microplastic sensors and open-source algorithms to interpret data, they hope to enable the rapid detection of microplastics, even in resource-limited labs.
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