Materials Map

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

  • About
  • Privacy Policy
  • Legal Notice
  • Contact

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.

×

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.

To Graph

1.080 Topics available

To Map

977 Locations available

693.932 PEOPLE
693.932 People People

693.932 People

Show results for 693.932 people that are selected by your search filters.

←

Page 1 of 27758

→
←

Page 1 of 0

→
PeopleLocationsStatistics
Naji, M.
  • 2
  • 13
  • 3
  • 2025
Motta, Antonella
  • 8
  • 52
  • 159
  • 2025
Aletan, Dirar
  • 1
  • 1
  • 0
  • 2025
Mohamed, Tarek
  • 1
  • 7
  • 2
  • 2025
Ertürk, Emre
  • 2
  • 3
  • 0
  • 2025
Taccardi, Nicola
  • 9
  • 81
  • 75
  • 2025
Kononenko, Denys
  • 1
  • 8
  • 2
  • 2025
Petrov, R. H.Madrid
  • 46
  • 125
  • 1k
  • 2025
Alshaaer, MazenBrussels
  • 17
  • 31
  • 172
  • 2025
Bih, L.
  • 15
  • 44
  • 145
  • 2025
Casati, R.
  • 31
  • 86
  • 661
  • 2025
Muller, Hermance
  • 1
  • 11
  • 0
  • 2025
Kočí, JanPrague
  • 28
  • 34
  • 209
  • 2025
Šuljagić, Marija
  • 10
  • 33
  • 43
  • 2025
Kalteremidou, Kalliopi-ArtemiBrussels
  • 14
  • 22
  • 158
  • 2025
Azam, Siraj
  • 1
  • 3
  • 2
  • 2025
Ospanova, Alyiya
  • 1
  • 6
  • 0
  • 2025
Blanpain, Bart
  • 568
  • 653
  • 13k
  • 2025
Ali, M. A.
  • 7
  • 75
  • 187
  • 2025
Popa, V.
  • 5
  • 12
  • 45
  • 2025
Rančić, M.
  • 2
  • 13
  • 0
  • 2025
Ollier, Nadège
  • 28
  • 75
  • 239
  • 2025
Azevedo, Nuno Monteiro
  • 4
  • 8
  • 25
  • 2025
Landes, Michael
  • 1
  • 9
  • 2
  • 2025
Rignanese, Gian-Marco
  • 15
  • 98
  • 805
  • 2025

Zahiri, Zohreh

  • Google
  • 2
  • 5
  • 9

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (2/2 displayed)

  • 2022Characterization of corrosion products on carbon steel using hyperspectral imaging in Short-Wave Infrared (SWIR)8citations
  • 2022Corrosion monitoring on zinc electroplated steel using shortwave infrared hyperspectral imaging1citations

Places of action

Chart of shared publication
Wielant, Jan
1 / 3 shared
Scheunders, Paul
2 / 2 shared
Lamberti, Alfredo
1 / 11 shared
Kerf, Thomas De
1 / 2 shared
Vanlanduit, Steve
1 / 12 shared
Chart of publication period
2022

Co-Authors (by relevance)

  • Wielant, Jan
  • Scheunders, Paul
  • Lamberti, Alfredo
  • Kerf, Thomas De
  • Vanlanduit, Steve
OrganizationsLocationPeople

document

Corrosion monitoring on zinc electroplated steel using shortwave infrared hyperspectral imaging

  • Kerf, Thomas De
  • Scheunders, Paul
  • Vanlanduit, Steve
  • Zahiri, Zohreh
Abstract

In this study, we investigate the use of hyperspectral imaging (HSI) to inspect the formation of corrosion products on galvanised carbon steel samples. Ten samples were subjected to an accelerated corrosion test with different exposure times. The analysis is performed in a two-step procedure: First, the different corrosion minerals are identified by microscopic Fourier transform infrared spectroscopy (FTIR) at specific locations on the samples. The following corrosion minerals are identified: ZnO (zincite/zinc oxide) Zn 5 (OH) 8 Cl 2 . H 2 O(simonkolleite), ZnCO 3 ,(smithsonite), Zn 5 (CO 3 ) 2 (OH) 6 ,(marionite/hydrozincite). Second, the identified corrosion minerals are correlated with the HSI spectra for these specific locations. This correlation provides us with the spectra in the SWIR region and allows us to construct a classification map for the different corrosion minerals. The results show that we are able to identify the different minerals using HSI camera. This proposed methodology allows us to speed up the inspection process, compared to FTIR, while still accurately distinguishing between the different corrosion minerals

Topics
  • impedance spectroscopy
  • mineral
  • Carbon
  • corrosion
  • zinc
  • steel
  • Fourier transform infrared spectroscopy