We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Laser speckle imaging in discrimination of zooplanktons from supermicroplastics
Summary
Researchers developed a laser speckle imaging technique to distinguish tiny microplastic particles (under 350 µm) from zooplankton in water samples in real time. This new method could significantly improve the accuracy and speed of monitoring small microplastics in ocean environments.
The presence of supermicroplastic (SMP) of less than 350 µm is on the rise in oceans, and it is often difficult to discriminate the SMPs from zooplanktons in real-time. In this study, we propose a laser speckle method in differentiating SMPs from brine shrimps, a generally used model zooplankton, and at the same time in distinguishing varied sized SMPs too. The pilot system consisting of a laser diode source of wavelength 635 nm mounted in a submersible glass sphere that has the potential of surveying in deep-sea environments illuminated a cuvette containing either polystyrene microspheres alone or mixed with brine shrimp to produce laser speckles. Such speckles recorded by a CMOS camera at 15 frames/sec for 20sec were analyzed to calculate a parameter of maximum difference between recorded frames. The results clearly differentiated the microplastics and the microplastics from the brine shrimps, thus demonstrating the possible use of the laser speckle method as a potential tool in discriminating planktons from SMPs and has implications for reaching deep-sea environments.
Sign in to start a discussion.
More Papers Like This
Raman Tweezers for Small Microplastics and Nanoplastics Identification in Seawater
Researchers used Raman tweezers - optical tweezers combined with Raman spectroscopy - to capture and chemically identify individual small microplastic and nanoplastic particles in seawater samples in situ. This novel technique could enable real-time identification of the smallest plastic particles in marine environments, filling a critical gap in nano- and micro-plastic detection.
Remote 3D Imaging and Classification of Pelagic Microorganisms with A Short‐Range Multispectral Confocal LiDAR
Researchers developed a new underwater laser-based imaging system capable of identifying and classifying tiny marine organisms in three dimensions from a distance. The device uses multiple light wavelengths to capture detailed images of plankton as small as fractions of a millimeter without requiring physical sample collection. This technology could enable continuous, non-invasive monitoring of plankton communities, which are critical indicators of ocean health.
Smart polarization and spectroscopic holography for real-time microplastics identification
Researchers developed a new optical imaging system called SPLASH that simultaneously captures polarization, holographic, and texture data from tiny particles — without needing a traditional spectrometer — and used machine learning to identify different types of microplastics with high accuracy. This approach could enable faster, more practical real-time monitoring of microplastic pollution in water.
Sorting microplastics from other materials in water samples by ultra-high-definition imaging
Researchers used a commercial particle analyzer with ultra-high-definition imaging to sort and identify microplastic particles in water samples. The device successfully distinguished between different plastic types based on how light scatters through or off their surfaces, and could separate microplastics from air bubbles and other non-plastic particles. The study demonstrates a relatively fast and accessible method for characterizing microplastic contamination in water.
Fast microplastics identification with stimulated Raman scattering microscopy
Stimulated Raman scattering microscopy was applied to rapidly identify and image microplastic particles in complex environmental samples at speeds dramatically faster than conventional Raman spectroscopy. The technique has potential to enable high-throughput microplastic analysis that could make large-scale environmental monitoring more feasible.