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Multivariate curve resolution applied to in situ X-ray absorption spectroscopy data: an efficient tool for data

Alexey Voronov1, Atsushi Urakawa2, Wouter van Beek3

  • 1Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), NO-7491 Trondheim, Norway.

Analytica Chimica Acta
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PubMed
Summary

Multivariate Curve Resolution (MCR) efficiently processes large in situ spectroscopy datasets. This blind-source separation method extracts component spectra and concentration profiles without prior knowledge, ideal for complex catalytic reactions.

Keywords:
In situModulationMultivariate curve resolutionOperandoX-ray absorption spectroscopy

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Area of Science:

  • Spectroscopy
  • Catalysis
  • Data Analysis

Background:

  • Large datasets from in situ/operando experiments pose analysis challenges.
  • Conventional scan-by-scan methods are slow and may rely on assumptions.
  • Automated methods like least squares fitting require reference spectra.

Purpose of the Study:

  • To apply Multivariate Curve Resolution (MCR) for efficient processing of large spectroscopic datasets.
  • To demonstrate MCR as a blind-source separation technique for complex in situ experiments.
  • To analyze data from a periodic concentration perturbation study.

Main Methods:

  • Application of Multivariate Curve Resolution (MCR) algorithm.
  • Analysis of in situ X-ray absorption spectroscopy data.
  • Processing data from a reversible reduction-oxidation reaction.

Main Results:

  • MCR successfully extracted component spectra and concentration profiles.
  • The method operated in a highly automated manner without reference spectra.
  • Signal-to-noise ratio was enhanced by exploiting experimental periodicity.

Conclusions:

  • MCR offers an efficient and automated approach for analyzing large in situ/operando datasets.
  • The technique provides valuable insights into complex catalytic systems.
  • MCR overcomes limitations of traditional data analysis methods.