Microplastics, defined as plastic fragments smaller than 5 millimeters, have become one of the most pervasive environmental contaminants on the planet. They are found in marine sediments, freshwater systems, drinking water, and food products, and they have been detected in human lungs, livers, breast milk, placental tissue, and reproductive organs. Studies estimate that average daily dietary intake of microplastics can range from roughly 25 to 450 milligrams per capita depending on the region, and polystyrene, polyethylene, and polypropylene are consistently reported as the most frequently detected polymer types in aquatic matrices. Once ingested, these particles can carry co-contaminants such as organic pollutants and heavy metals into tissues, with reported links to disruptions in reproductive function and metabolic processes.

Despite the scale of the problem, quantifying microplastics remains surprisingly difficult. The standard analytical techniques, including Fourier-transform infrared spectroscopy, Raman micro-spectroscopy, and scanning electron microscopy, demand laborious sample preparation, costly instrumentation, and highly trained specialists. FTIR spectroscopy, for instance, cannot reliably identify particles smaller than about 20 micrometers. Moreover, most environmental studies rely on trawl nets with mesh sizes between 200 and 333 micrometers, which exclude the vast majority of smaller particles that are nonetheless ecologically significant. Recent surveys of European Atlantic coastal waters found that approximately 80 percent of microplastics fell between 10 and 300 micrometers, while other work shows that particles below 100 micrometers dominate stormwater and coastal systems. Zooplankton species can ingest particles between roughly 2 and 32 micrometers, overlapping with the sizes of their natural prey, which means the smallest fractions sit squarely within the base of the aquatic food web.

The zero-plastic prototype was designed specifically to address this analytical gap. Built on an open-source hardware platform using off-the-shelf components, the benchtop device integrates a fluidic microscope with high-resolution digital imaging and embedded artificial intelligence processing. The system employs two linked embedded computers: a Raspberry Pi 4 controls image acquisition through a Sony IMX477 12.3-megapixel camera, while an NVIDIA Jetson board provides GPU-accelerated image analysis, running tasks such as particle detection, segmentation, and classification in near real time. The optical train combines a 12-millimeter microscope objective with a 25-millimeter tube lens to achieve approximately 1000-fold magnification, yielding a spatial resolution of about 0.667 micrometers per pixel across a 4056 by 3040 pixel field of view. Water samples flow through a thin transparent glass microfluidic channel, 0.2 millimeters in depth, driven by a peristaltic pump that advances the fluid in precise 0.01-milliliter steps synchronized with white LED backlight illumination, ensuring that particles remain stationary during each exposure and that images stay sharp.

The entire assembled prototype is roughly the size of a small shoebox and costs a few hundred US dollars, a fraction of the price of commercial flow-imaging instruments such as FlowCam or the Imaging FlowCytobot, which offer similar capabilities but at much higher cost and complexity and in bulkier packages. The researchers note that the microfluidic channel slide remains the most expensive and fragile component of the system, and no suitable alternative was identified during development.

To validate the device under controlled conditions, the team developed a reproducible laboratory method for generating spherical polystyrene microplastic test particles using an emulsification and solvent evaporation process. Polystyrene is dissolved in chloroform at 40 degrees Celsius, added dropwise to an aqueous polyvinyl alcohol solution under high-shear mixing at 6,000 to 10,000 revolutions per minute, and then stirred magnetically to evaporate the solvent and form particles. Six different formulations were prepared, and their morphology and size distribution were confirmed by scanning electron microscopy using a JEOL JSM-6610LV microscope with gold-coated samples imaged at 500 and 1000 times magnification. Both methods confirmed that the synthesized particles were predominantly spherical and centered around 5 micrometers in diameter, consistent with expectations for mini-emulsion polymerization.

The imaging comparison produced encouraging results. The zero-plastic system detected particles down to approximately 3 micrometers, and for spherical polystyrene beads above that threshold its measured size distributions agreed closely with SEM measurements. In one representative sample, the device processed 0.3 milliliters of fluid across 30 high-resolution images and detected roughly 24,000 particle instances, whereas SEM analysis of 11 fields of view identified 337 particles. Mean particle diameters were measured at 6.85 micrometers by the prototype and 5.77 micrometers by SEM, with minimum detectable sizes of 2.71 and 2.11 micrometers respectively. A two-sample Kolmogorov–Smirnov test revealed detectable differences between the full distributions, driven primarily by the optical system’s inability to resolve particles smaller than 3 micrometers, an inherent limitation of diffraction and sensor resolution at this magnification. When particles below 3 micrometers were excluded, cumulative differences between the two methods fell below 5 percent with no statistically significant difference.

The image analysis pipeline itself relies on a data-centric AI approach implemented in Python using NumPy and Scikit-Image. Raw images from the prototype undergo preprocessing in which a background base image is computed from the pixel-wise median of five frames, capturing static artifacts such as lens dust, which is then subtracted to improve contrast. Segmentation applies a global grayscale threshold to create binary masks, removes connected regions below a minimum area, and retains only particles with an eccentricity of 0.55 or less, effectively filtering out clusters, debris, and elongated objects. An additional solidity filter discards regions where less than 80 percent of the area is covered by the convex hull, further reducing false positives. Accepted particles are sized by computing the equivalent circular diameter from the convex hull area, scaled by the known pixel resolution and reported in micrometers. Interestingly, the optical system detected a population of particles in the 15 to 20 micrometer range that SEM did not, which the authors attribute to differences in aggregation behavior between dried samples deposited on a substrate and particles suspended in water reflecting their hydrodynamic diameter.

The researchers are candid about the prototype’s limitations. It cannot detect particles below 3 micrometers, including nanoplastics, and it has not yet been tested with the heterogeneous mixture of shapes, polymer types, and organic materials found in real environmental waters. The current processing pipeline is tailored for spherical particles and would require further development to classify fragments, fibers, and films, or to distinguish plastics from non-plastic debris that may resemble them optically. Sample preparation, including pre-filtration to prevent channel clogging, is still required, which currently prevents autonomous in-field operation. Unlike impedance-based or microwave-based sensing methods, however, the imaging approach provides direct visual confirmation of particles along with size and shape information, a significant advantage for validation and interpretation.

The system has also been designed with a larger technological vision in mind. The zero-plastic architecture is intended to function as a node within a distributed planetary digital twin infrastructure, in which multiple sensing units stream processed, time-stamped data, such as particle counts and size distributions, to a shared platform using standard publisher-subscriber protocols like MQTT. In the current implementation, processed results are uploaded after each acquisition run and viewable through a web-based dashboard, though the digital twin integration remains at an early proof-of-concept stage and no field trials have been conducted. Each future deployment unit could act as a local observation point feeding a shared environmental model, enabling large-scale, geographically distributed monitoring that no single high-end laboratory instrument could achieve.

The team has released its full dataset, titled “Microplastic Dataset: Supporting microplastic monitoring based on cost-effective open hardware solutions,” on Zenodo in accordance with FAIR data principles, where it has already been downloaded more than 300 times. Future work will focus on extending validation to non-spherical particles, testing performance with real-world water samples containing sediments and biological material, benchmarking against commercial particle-sizing instruments, and eventually adding spectral identification of polymer types, a capability the researchers describe as a mid-term objective requiring changes to the optical configuration.

At its current technology readiness level, the zero-plastic prototype is positioned not as a field-ready monitor but as an intermediate validation tool that bridges high-resolution laboratory methods such as SEM and higher-throughput, lower-resolution monitoring approaches. Even so, the work represents a meaningful step toward democratizing microplastic analysis. By demonstrating that a few hundred dollars of off-the-shelf hardware, combined with embedded AI and careful optical engineering, can reproduce the size measurements of a scanning electron microscope for environmentally relevant particle sizes, the Brazilian team has opened a credible pathway toward affordable, distributed, and continuous microplastic monitoring, precisely the kind of scalable capability that the global effort to understand and manage plastic pollution has been missing.