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

Nagataki, Shigehiro

  • Google
  • 1
  • 6
  • 24

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2021From Supernova to Supernova Remnant: Comparison of Thermonuclear Explosion Models24citations

Places of action

Chart of shared publication
Warren, Donald
1 / 1 shared
Röpke, Friedrich
1 / 2 shared
Ono, Masaomi
1 / 1 shared
Lach, Florian
1 / 1 shared
Iwasaki, Hiroyoshi
1 / 1 shared
Seitenzahl, Ivo
1 / 1 shared
Chart of publication period
2021

Co-Authors (by relevance)

  • Warren, Donald
  • Röpke, Friedrich
  • Ono, Masaomi
  • Lach, Florian
  • Iwasaki, Hiroyoshi
  • Seitenzahl, Ivo
OrganizationsLocationPeople

article

From Supernova to Supernova Remnant: Comparison of Thermonuclear Explosion Models

  • Nagataki, Shigehiro
  • Warren, Donald
  • Röpke, Friedrich
  • Ono, Masaomi
  • Lach, Florian
  • Iwasaki, Hiroyoshi
  • Seitenzahl, Ivo
Abstract

<jats:title>Abstract</jats:title><jats:p>Progress in the three-dimensional modeling of supernovae (SNe) prompts us to revisit the supernova remnant (SNR) phase. We continue our study of the imprint of a thermonuclear explosion on the SNR it produces, which we started with a delayed detonation model of a Chandrasekhar-mass white dwarf. Here we compare two different types of explosion models, each with two variants: two delayed detonation models (N100ddt, N5ddt) and two pure deflagration models (N100def, N5def), where the N number parameterizes the ignition. The output of each SN simulation is used as input to an SNR simulation carried on until 500 yr after the explosion. While all SNR models become more spherical over time and overall display the theoretical structure expected for a young SNR, clear differences are visible among the models, depending on the geometry of the ignition and on the presence or not of detonation fronts. Compared to N100 models, N5 models have a strong dipole component and produce asymmetric remnants. N5def produces a regular-looking, but offset remnant, while N5ddt produces a two-sided remnant. Pure deflagration models exhibit specific traits: a central overdensity, because of the incomplete explosion, and a network of seam lines across the surface, boundaries between burning cells. Signatures from the SN dominate the morphology of the SNR up to 100–300 yr after the explosion, depending on the model, and are still measurable at 500 yr, which may provide a way of testing explosion models.</jats:p>

Topics
  • impedance spectroscopy
  • morphology
  • surface
  • phase
  • simulation