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Do We Really Need Extra Signal-Enhancing Techniques for Nanoplastic Detection? The Mie-Resonance-Assisted Self-Nanolensing Effect Enables Routine Raman Analysis
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Researchers revealed that submicrometer dielectric nanoplastics exhibit inherent Mie resonance optical properties that enable their detection without additional signal-enhancing techniques. The finding challenged the assumption that extra amplification is always needed for nanoplastic detection, potentially simplifying analytical approaches.
Despite growing interest in nanoplastics (NPs) in the environment, reliable and harmonized accumulation of data has been hindered by analytical limitations. Although some techniques have been proposed to quantify or qualify NPs in the environment, they are still not standardized. Here, we revealed that the inherent optical properties of submicrometer dielectric nanoplastics lead to a previously unappreciated Mie-resonance-assisted self-nanolensing effect, significantly amplifying their Raman signals, sufficient to be detected by a conventional Raman spectrometer. Through experimental and theoretical validation, we demonstrate that the self-nanolensing effect amplifies Raman signals by up to 35-fold in isolated NPs, enabling reliable detection down to 125 nm in diameter. This effect is crucially demonstrated to be significant even for irregularly shaped nanoplastics, enough for environmental applications. This led to the development of an automated Raman-based quantification system for environmental NPs, which allows for rapid and efficient measurement in wastewater from recycling facilities. NP concentration of wastewater is estimated to be 1.6 × 109 particles per liter, marking the first report of NP distribution in the recycling process. Additionally, we analyzed the NP distribution in bottled water and obtained similar levels of data to those previously reported with stimulated Raman scattering (SRS). This study demonstrates the potential for routine NP quantification, facilitating the accumulation of distribution data in various environments, which is crucial for risk assessments and evidence-based environmental policy decisions.
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