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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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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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (5/5 displayed)

  • 2022Validation of Fourier Transform Infrared Spectroscopy for Serotyping of Streptococcus pneumoniae.20citations
  • 2022Fast Readout of Split-Ring Resonators Made Simple and Low-Cost for Application in HPLCcitations
  • 2021Towards a Highly Sensitive Piezoelectric Nano-Mass Detectio : A Model-Based Concept Studycitations
  • 2018Design and evaluation of split-ring resonators for aptamer-based biosensors25citations
  • 2017Differential Inductive Sensor for Continuous Non-Invasive Cell Growth Monitoring in Disposable Bioreactorscitations

Places of action

Chart of shared publication
Mauder, N.
1 / 1 shared
Passaris, Ioannis
1 / 1 shared
Kostrzewa, M.
1 / 2 shared
Burckhardt, I.
1 / 1 shared
Desmet, Stefanie
1 / 1 shared
Nm, Van Sorge
1 / 1 shared
Hc, Slotved
1 / 1 shared
Pj, Ceyssens
1 / 1 shared
Gehl, Adrian V.
1 / 1 shared
Sterr, Erk-Fietje
1 / 1 shared
Hitzemann, Moritz
2 / 3 shared
Dehning, Kirsten J.
1 / 1 shared
Glukhovkoy, Anatoly
1 / 1 shared
De Wall, Sascha
1 / 1 shared
Wurz, Marc Christopher
1 / 6 shared
Twiefel, Jens
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Sehlmeyer, Merle
1 / 1 shared
Ahrens, André
1 / 2 shared
Walter, Johanna-Gabriela
1 / 3 shared
Scheper, Thomas
2 / 8 shared
Kobelt, Tim
1 / 1 shared
Reinecke, Tobias
2 / 2 shared
Allers, Maria
1 / 1 shared
Nagraik, Tamanna
1 / 1 shared
Bakes, Kai
1 / 1 shared
Berger, Marc
1 / 1 shared
Solle, Dörte
1 / 1 shared
Chart of publication period
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2021
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Co-Authors (by relevance)

  • Mauder, N.
  • Passaris, Ioannis
  • Kostrzewa, M.
  • Burckhardt, I.
  • Desmet, Stefanie
  • Nm, Van Sorge
  • Hc, Slotved
  • Pj, Ceyssens
  • Gehl, Adrian V.
  • Sterr, Erk-Fietje
  • Hitzemann, Moritz
  • Dehning, Kirsten J.
  • Glukhovkoy, Anatoly
  • De Wall, Sascha
  • Wurz, Marc Christopher
  • Twiefel, Jens
  • Sehlmeyer, Merle
  • Ahrens, André
  • Walter, Johanna-Gabriela
  • Scheper, Thomas
  • Kobelt, Tim
  • Reinecke, Tobias
  • Allers, Maria
  • Nagraik, Tamanna
  • Bakes, Kai
  • Berger, Marc
  • Solle, Dörte
OrganizationsLocationPeople

article

Validation of Fourier Transform Infrared Spectroscopy for Serotyping of Streptococcus pneumoniae.

  • Mauder, N.
  • Passaris, Ioannis
  • Kostrzewa, M.
  • Burckhardt, I.
  • Desmet, Stefanie
  • Nm, Van Sorge
  • Hc, Slotved
  • Pj, Ceyssens
  • Zimmermann, Stefan
Abstract

Fourier transform infrared (FT-IR) spectroscopy (IR Biotyper; Bruker) allows highly discriminatory fingerprinting of closely related bacterial strains. In this study, FT-IR spectroscopy-based capsular typing of Streptococcus pneumoniae was validated as a rapid, cost-effective, and medium-throughput alternative to the classical phenotypic techniques. A training set of 233 strains was defined, comprising 34 different serotypes and including all 24 vaccine types (VTs) and 10 non-vaccine types (NVTs). The acquired spectra were used to (i) create a dendrogram where strains clustered together according to their serotypes and (ii) train an artificial neural network (ANN) model to predict unknown pneumococcal serotypes. During validation using 153 additional strains, we reached 98.0% accuracy for determining serotypes represented in the training set. Next, the performance of the IR Biotyper was assessed using 124 strains representing 59 non-training set serotypes. In this setting, 42 of 59 serotypes (71.1%) could be accurately categorized as being non-training set serotypes. Furthermore, it was observed that comparability of spectra was affected by the source of the Columbia medium used to grow the pneumococci and that this complicated the robustness and standardization potential of FT-IR spectroscopy. A rigorous laboratory workflow in combination with specific ANN models that account for environmental noise parameters can be applied to overcome this issue in the near future. The IR Biotyper has the potential to be used as a fast, cost-effective, and accurate phenotypic serotyping tool for S. pneumoniae.

Topics
  • Fourier transform infrared spectroscopy