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Matrix-assisted laser desorption ionization (MALDI) is a powerful analytical technique used in mass spectrometry. It enables the identification and characterization of various biomolecules, including proteins, peptides, nucleic acids, and carbohydrates. MALDI is an ionization technique, widely employed in biological and medical research, as well as in fields like pharmacology and biochemistry.The analyte of interest, a biomolecule or a mixture of biomolecules, is mixed with a suitable matrix...
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Spatially resolved polymer classification using laser induced breakdown spectroscopy (LIBS) and multivariate

Lukas Brunnbauer1, Silvia Larisegger2, Hans Lohninger1

  • 1TU Wien, Institute of Chemical Technologies and Analytics, Getreidemarkt 9/164-IAC, 1060, Vienna, Austria.

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|January 2, 2020
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Summary
This summary is machine-generated.

Laser induced breakdown spectroscopy (LIBS) offers spatially resolved classification of synthetic polymers, analyzing both 2D structures and depth profiles. This powerful technique provides elemental composition alongside polymer identification for comprehensive material characterization.

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Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Synthetic polymers are crucial in modern applications, necessitating advanced characterization methods.
  • Conventional techniques like FT-IR and Raman lack depth profiling capabilities for complex polymer structures.
  • Emerging multi-polymer materials require novel analytical approaches for spatial and depth analysis.

Purpose of the Study:

  • To introduce Laser Induced Breakdown Spectroscopy (LIBS) as a novel method for spatially resolved synthetic polymer classification.
  • To demonstrate LIBS's capability in analyzing both 2D structured and multilayer polymer systems.
  • To showcase LIBS's potential for depth profiling analysis in synthetic polymers.

Main Methods:

  • Analysis of 2D structured and multilayer synthetic polymer samples using LIBS.
  • Classification of five polymers: acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), polyethylene (PE), polyacrylate (PAK), and polyvinylchloride (PVC).
  • Application of multivariate statistical approaches, including principal component analysis (PCA) and k-means clustering, for classification.

Main Results:

  • Successful spatial classification of five different synthetic polymers (ABS, PLA, PE, PAK, PVC) in structured samples.
  • Validation of 2D sample classification by comparing with elemental contamination distribution.
  • Validation of multilayer system classification by comparison with microscopic cross-sections.
  • Demonstration of LIBS's ability for direct depth profile analysis.

Conclusions:

  • LIBS is a powerful and versatile tool for spatially resolved synthetic polymer classification, including depth profiling.
  • LIBS provides simultaneous elemental analysis, enhancing the information obtained from a single measurement.
  • This technique addresses the need for advanced characterization of complex, multi-component polymer materials.