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Size, Shape, and Material matter: All-optical Mie void sensor for complex nanoplastic mixtures
Summary
Scientists have developed a low-cost sensor that can quickly detect and identify tiny plastic particles (smaller than 1/100th the width of a human hair) by trapping them in specially designed nanoscale wells that reveal their size, shape, and even plastic type through color changes. This matters because nanoplastics are increasingly found in our food, water, and bodies, and this faster, cheaper detection method could help researchers and health officials monitor plastic contamination more easily than current expensive, slow lab techniques allow.
The fragmentation of plastic debris and the direct release of nanoplastics have emerged as a pressing ecological concern. Once dispersed, these enduring particles infiltrate food webs, accumulate within organisms, and bind toxic co-contaminants, posing long-term risks to ecosystems and human health. Despite growing awareness, the detection and characterization of nanoplastics remain highly challenging due to their minute size. Moreover, obtaining additional critical information, such as the particle shape or material composition, further exacerbates these detection hurdles. Conventional analytical techniques capable of providing more detailed information often demand substantial experimental and lab-bound effort, costly instrumentation, and lengthy measurement times. Here, we introduce a novel photonic sensing platform based on nanoscale voids that enables the simultaneous material- and morphology-sensitive detection of particles below 500 nm. Arrays of voids embedded in a high-refractive-index material act in parallel as both sorting elements and direct color reporters. Spherical and elongated particles are selectively trapped in circular and elliptical voids, while polymer types such as PS, PMMA, and PET are distinguished via the specific color signatures arising from their refractive index contrasts. This approach offers a cheap and scalable route toward rapid optical identification of nanoplastics in complex environmental and biological settings. Its compatibility with quick, high-throughput analysis positions it as a promising tool for real-time monitoring and comparative studies of heterogeneous nanoplastic populations.