首页|Johns Hopkins University Reports Findings in Nanoplastics (Integrating Metal-Phe nolic Networks-Mediated Separation and Machine Learning-Aided Surface-Enhanced R aman Spectroscopy for Accurate Nanoplastics Quantification and Classification)
Johns Hopkins University Reports Findings in Nanoplastics (Integrating Metal-Phe nolic Networks-Mediated Separation and Machine Learning-Aided Surface-Enhanced R aman Spectroscopy for Accurate Nanoplastics Quantification and Classification)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Nanotechnology - Nanoplastics is the subject of a report. According to news reporting from Baltimore, Maryland, b y NewsRx journalists, research stated, "Increasing accumulation of nanoplastics across ecosystems poses a significant threat to both terrestrial and aquatic lif e. Surfaceenhanced Raman scattering (SERS) is an emerging technique used for na noplastics detection." The news correspondents obtained a quote from the research from Johns Hopkins Un iversity, "However, the identification and classification of nanoplastics using SERS faces chAllenges regarding sensitivity and accuracy as nanoplastics are spa rsely dispersed in the environment. Metal-phenolic networks (MPNs) have the pote ntial to rapidly concentrate and separate various types and sizes of nanoplastic s. SERS combined with machine learning may improve prediction accuracy. Herein, we report the integration of MPNs-mediated separation with machine learning-aide d SERS methods for the accurate classification and high-precision quantification of nanoplastics, which is tailored to include the complete region of characteri stic peaks across diverse nanoplastics in contrast to the traditional manual ana lysis of SERS spectra on a singular characteristic peak. Our customized machine learning system (e.g., outlier detection, classification, quantification) Allows for the identification of detectable nanoplastics (accuracy 81.84% ), accurate classification (accuracy > 97%) , and sensitive quantification of various types of nanoplastics (polystyrene (PS ), poly(methyl methacrylate) (PMMA), polyethylene (PE), and poly(lactic acid) (P LA)) down to ultralow concentrations (0.1 ppm) as well as accurate classificatio n (accuracy > 92%) of nanoplastic mixtures at a subppm level."
BaltimoreMarylandUnited StatesNort h and Central AmericaCyborgsEmerging TechnologiesMachine LearningNanopla sticsNanotechnology
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