Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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1.080 Topics available

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693.932 PEOPLE
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University of Amsterdam

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (3/3 displayed)

  • 2024A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry2citations
  • 2022Automated Feature Mining for Two-Dimensional Liquid Chromatography Applied to Polymers Enabled by Mass Remainder Analysis14citations
  • 2019Self Adjusting Algorithm for the Nontargeted Feature Detection of High Resolution Mass Spectrometry Coupled with Liquid Chromatography Profile Data30citations

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Seijo, Marianne
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Praetorius, Antonia
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Boudguiyer, Youssef
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Sadia, Mohammad
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Helmus, Rick
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Molenaar, S. R. A.
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Desport, J. S.
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Peters, R. A. H.
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Pirok, B. W. J.
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Put, B. Van De
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Reid, Malcolm J.
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Kevin Thomas, V.
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Co-Authors (by relevance)

  • Seijo, Marianne
  • Praetorius, Antonia
  • Boudguiyer, Youssef
  • Sadia, Mohammad
  • Helmus, Rick
  • Molenaar, S. R. A.
  • Desport, J. S.
  • Peters, R. A. H.
  • Pirok, B. W. J.
  • Put, B. Van De
  • Reid, Malcolm J.
  • Kevin Thomas, V.
OrganizationsLocationPeople

article

Automated Feature Mining for Two-Dimensional Liquid Chromatography Applied to Polymers Enabled by Mass Remainder Analysis

  • Molenaar, S. R. A.
  • Desport, J. S.
  • Peters, R. A. H.
  • Pirok, B. W. J.
  • Put, B. Van De
  • Samanipour, Saer
Abstract

A fast algorithm for automated feature mining of synthetic (industrial) homopolymers or perfectly alternating copolymers was developed. Comprehensive two-dimensional liquid chromatography-mass spectrometry data (LC × LC-MS) was utilized, undergoing four distinct parts within the algorithm. Initially, the data is reduced by selecting regions of interest within the data. Then, all regions of interest are clustered on the time and mass-to-charge domain to obtain isotopic distributions. Afterward, single-value clusters and background signals are removed from the data structure. In the second part of the algorithm, the isotopic distributions are employed to define the charge state of the polymeric units and the charge-state reduced masses of the units are calculated. In the third part, the mass of the repeating unit (i.e., the monomer) is automatically selected by comparing all mass differences within the data structure. Using the mass of the repeating unit, mass remainder analysis can be performed on the data. This results in groups sharing the same end-group compositions. Lastly, combining information from the clustering step in the first part and the mass remainder analysis results in the creation of compositional series, which are mapped on the chromatogram. Series with similar chromatographic behavior are separated in the mass-remainder domain, whereas series with an overlapping mass remainder are separated in the chromatographic domain. These series were extracted within a calculation time of 3 min. The false positives were then assessed within a reasonable time. The algorithm is verified with LC × LC-MS data of an industrial hexahydrophthalic anhydride-derivatized propylene glycol-terephthalic acid copolyester. Afterward, a chemical structure proposal has been made for each compositional series found within the data.

Topics
  • impedance spectroscopy
  • cluster
  • two-dimensional
  • copolymer
  • homopolymer
  • spectrometry
  • clustering
  • liquid chromatography
  • liquid chromatography-mass spectrometry
  • alternating copolymer
  • end-group composition