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Regenerable Membrane Sensors for Ultrasensitive Nanoplastic Quantification Enabled by A Data-driven Raman Spectral Processing Algorithm
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Researchers developed Pre_seg, a data-driven Raman spectral processing algorithm paired with regenerable anodic aluminum oxide membrane sensors, enabling ultrasensitive quantification of nanoplastics in complex natural water systems by overcoming matrix interference and spectral noise limitations.
The detection of nanoplastics (NPs) in complex natural water systems is hindered by matrix interferences and limitations in current analytical techniques. This study presents Pre_seg, a Raman spectral processing algorithm integrated with regenerable anodic aluminum oxide (AAO) membrane sensors, for ultrasensitive, rapid, and quantitative NP detection at the single-particle level. The AAO membranes function as both filtration substrates and Raman sensors, reducing sample loss and contamination. Pre_seg incorporates statistically determined thresholds for signal-to-noise ratios (SNRs) and full width at half maximums (fwhms) across segmented spectral ranges, effectively minimizing noise and enhancing accuracy and sensitivity of NP detection. Pre_seg achieved 93.5% prediction accuracy of NPs and ≥90.4% rejection accuracy for non-NP entries. Mixed NPs were quantified at the lowest concentration of 0.5 μg L-1. The robustness of Pre_seg was validated in eutrophic and oligotrophic lake matrices following oxidation digestion pretreatment to mitigate organic interferences. Furthermore, the AAO membrane sensors demonstrated stability through multiple regeneration and reuse cycles. This innovative approach advances NP detection by enabling scalable, customizable, and environmentally relevant monitoring.
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Regenerable MembraneSensors for Ultrasensitive NanoplasticQuantification Enabled by A Data-driven Raman Spectral ProcessingAlgorithm
AI summary Read the abstract
Researchers developed Pre_seg, a data-driven Raman spectral processing algorithm integrated with regenerable anodic aluminum oxide membrane sensors, achieving ultrasensitive detection and quantification of nanoplastics in complex natural water matrices by addressing spectral interference challenges.
Integrating Metal–Phenolic Networks-Mediated Separation and Machine Learning-Aided Surface-Enhanced Raman Spectroscopy for Accurate Nanoplastics Quantification and Classification
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Researchers combined a metal-based separation technique with machine learning and surface-enhanced Raman spectroscopy to detect and classify nanoplastics in environmental samples. The method achieved high accuracy in identifying different types of nanoplastics at very low concentrations. This approach could make it significantly easier and more reliable to monitor nanoplastic contamination in real-world water and soil samples.
Metal-free AAO membranes function as both filters and Raman enhancers for the analysis of nanoplastics
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Scientists developed a simple, metal-free aluminum oxide membrane that works as both a filter and a signal-enhancing surface for detecting nanoplastics using Raman spectroscopy. The approach successfully identified plastic particles as small as 200 nanometers across six common polymer types, without complex sample preparation. This dual-function tool could offer a cost-effective and practical way to analyze nanoplastic contamination in environmental samples.
Engineered Approaches to Facile Identification of Tiny Microplastics in Polymeric and Ceramic Membrane Filtrations for Wastewater Treatment
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Researchers developed engineered approaches using Raman spectroscopy and automated image analysis to identify tiny microplastics retained in polymeric and ceramic membranes from wastewater treatment, enabling more precise assessment of membrane performance and microplastic removal efficiency.
Interfacial Regulation–Driven Dual-Enrichment SERS Coupled With Deep Learning Enable Ultrasensitive and In Situ Identification of Microplastics in Natural Waters
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Scientists have developed a new sensor technology that can detect tiny microplastic particles in real water samples—like rivers or tap water—at extremely low levels, without needing complex lab processing. By combining specially designed metal nanoparticles with AI-powered pattern recognition, the tool can also tell different types of plastic apart, even when they're mixed together. This matters because better detection tools are a key first step toward understanding how much plastic pollution is actually in our water supplies and food chain, which is important for assessing potential risks to human health.
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