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

Elbagory, Tarek M. A. A.

  • Google
  • 1
  • 4
  • 10

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2023Machine learning‐based prediction of mechanical and thermal properties of nickel/cobalt/ferrous and dried leaves fiber‐reinforced polymer hybrid composites10citations

Places of action

Chart of shared publication
Alarifi, Ibrahim M.
1 / 10 shared
Ali, Vakkar
1 / 1 shared
Sanjay, M. R.
1 / 4 shared
Mohit, H.
1 / 1 shared
Chart of publication period
2023

Co-Authors (by relevance)

  • Alarifi, Ibrahim M.
  • Ali, Vakkar
  • Sanjay, M. R.
  • Mohit, H.
OrganizationsLocationPeople

article

Machine learning‐based prediction of mechanical and thermal properties of nickel/cobalt/ferrous and dried leaves fiber‐reinforced polymer hybrid composites

  • Alarifi, Ibrahim M.
  • Ali, Vakkar
  • Elbagory, Tarek M. A. A.
  • Sanjay, M. R.
  • Mohit, H.
Abstract

<jats:title>Abstract</jats:title><jats:sec><jats:label /><jats:p>Dried leaves are the outstanding origin of cellulosic plant matter, and it is securing reputation as a renewable resource. Dried leaves fiber is suggested to possess the capability to substitute synthetic fibers in polymer laminates as a reinforcing component. The novelty of the present study reveals the effect of dried leaves fiber, cobalt, nickel, and ferrous reinforcement on the physical, mechanical, and thermal characteristics of epoxy, vinyl‐ester, and polyester polymers using artificial neural network (ANN) technique. These composites were fabricated using ultrasonication bath‐assisted wet layup method under ambient condition. The outcomes of this research exhibit that the dried leaves‐cobalt fillers reinforced in all three polymers possess higher mechanical and thermal stability characteristics when compared with other samples. The reason may be assigned to producing novel hydroxyl functional groups and strong interfacial bonding of fillers within the matrix as observed from Fourier‐transform infrared (FTIR) spectra and scanning electron microscope (SEM) micrographs, respectively. Moreover, as observed from the thermogravimetric analysis, the dried leaves‐ferrous filler‐reinforced polymer hybrid composites provided higher thermal stability. Statistical analysis was performed using the one‐way ANOVA technique and found that outcomes were significant statistically under the confidence level of 95%. Hence, this investigation not only emphasize the significance of investigating new polymer composites but also highlight the benefits of engaging advanced modeling to forecast the material characteristics precisely.</jats:p></jats:sec><jats:sec><jats:title>Highlights</jats:title><jats:p><jats:list list-type="bullet"> <jats:list-item><jats:p>Dried leaves and cobalt/nickel/ferrous are applied reinforcement to polymers.</jats:p></jats:list-item> <jats:list-item><jats:p>Composites fabricated using ultrasonication bath‐assisted wet layup technique.</jats:p></jats:list-item> <jats:list-item><jats:p>LM Algorithm‐based ANN selected for predicting the best composite.</jats:p></jats:list-item> <jats:list-item><jats:p>Higher mechanical and thermal stability with dried leaves‐cobalt filler.</jats:p></jats:list-item> <jats:list-item><jats:p>One‐way ANOVA proved statistically significant within the material properties.</jats:p></jats:list-item> </jats:list></jats:p></jats:sec>

Topics
  • impedance spectroscopy
  • polymer
  • nickel
  • scanning electron microscopy
  • composite
  • thermogravimetry
  • cobalt
  • interfacial
  • size-exclusion chromatography
  • ester
  • machine learning
  • ultrasonication