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Related Concept Videos

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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Related Experiment Video

Updated: Jun 24, 2025

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis
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Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis

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Optimization of sample preparation, fluorescence- and Raman techniques for environmental microplastics.

Merel C Konings1, Liron Zada1, Robert W Schmidt1

  • 1LaserLaB, Vrije Universiteit Amsterdam, the Netherlands.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|June 4, 2024
PubMed
Summary
This summary is machine-generated.

This study optimized methods for detecting environmental microplastics (MPs) using microspectroscopic imaging. A new density separation system achieved 95% recovery, and advanced Raman microscopy identified all plastic types, including carbon black-pigmented ones.

Keywords:
Fluorescence microscopyFluorescence stainingMicroplasticsRamanStimulated Raman Scattering

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Materials Science

Background:

  • Environmental microplastics (MPs) pose a significant threat, necessitating accurate detection and characterization methods.
  • Current microspectroscopic techniques like Raman scattering and fluorescence have limitations in speed and identifying colored MPs.

Purpose of the Study:

  • To optimize sample preparation for microplastic analysis.
  • To evaluate and improve microspectroscopic techniques for environmental microplastic detection and identification.
  • To develop a comprehensive methodology for microplastic analysis in environmental samples.

Main Methods:

  • Development of a new density separation apparatus ('MESSY' system) and optimization of Nile Red staining.
  • Utilized spontaneous Raman spectroscopy, Stimulated Raman Scattering (SRS), and fluorescence imaging.
  • Constructed a Deep-UV Raman microscope for enhanced identification of colored microplastics.

Main Results:

  • The 'MESSY' system demonstrated a high recovery rate of 95% ± 5.5%.
  • Nile Red staining provided coarse categorization of MPs, though fluorescent additives could cause misclassification.
  • Raman spectroscopy, especially with a red laser and Deep-UV microscopy, successfully identified various colored MPs, including those with carbon black.

Conclusions:

  • Optimized sample preparation and advanced spectroscopic methods significantly improve microplastic detection and identification.
  • Deep-UV Raman microscopy offers a powerful solution for analyzing challenging samples, such as colored microplastics.
  • The developed methodologies contribute to more effective environmental microplastic monitoring and research.