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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Naji, M.
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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (21/21 displayed)

  • 2024Impact of rubber content on average properties and distributions of high impact polystyrene by means of multiphase coupled matrix-based Monte Carlocitations
  • 2024Surfactant-free peroxidase-mediated enzymatic polymerization of a biorenewable butyrolactone monomer via a green approach : synthesis of sustainable biobased latexes4citations
  • 2024Combining ternary phase diagrams and multiphase coupled matrix-based Monte Carlo to model phase dependent compositional and molar mass variations in high impact polystyrene synthesis7citations
  • 2024Exploring the influence of polybutadiene content on high-impact polystyrene properties : a multiphase coupled matrix-based Monte Carlo approachcitations
  • 2023Surfactant-Free Peroxidase-Mediated Enzymatic Polymerization of a Biorenewable Butyrolactone Monomer via a Green Approach: Synthesis of Sustainable Biobased Latexescitations
  • 2023Multi-angle evaluation of kinetic Monte-Carlo simulations as a tool to evaluate the distributed monomer composition in gradient copolymer synthesis2citations
  • 2023Bayesian tuned kinetic Monte Carlo modeling of polystyrene pyrolysis : unraveling the pathways to its monomer, dimers, and trimers formation29citations
  • 2023Bayesian tuned kinetic Monte Carlo modeling of polystyrene pyrolysis : unraveling the pathways to its monomer, dimers, and trimers formation29citations
  • 2023Playing with process conditions to increase the industrial sustainability of poly(lactic acid)-based materials14citations
  • 2023Comparing thermal degradation for fused filament fabrication (FFF) with chain or step-growth polymerscitations
  • 2022Identifying optimal synthesis protocols via the in silico characterization of (a)symmetric block and gradient copolymers with linear and branched chainscitations
  • 2022A unified kinetic Monte Carlo approach to evaluate (a)symmetric block and gradient copolymers with linear and branched chains illustrated for poly(2-oxazoline)s16citations
  • 2020Connecting polymer synthesis and chemical recycling on a chain-by-chain basis : a unified matrix-based kinetic Monte Carlo strategy62citations
  • 2020Progress in reaction mechanisms and reactor technologies for thermochemical recycling of poly(methyl methacrylate)93citations
  • 2019The relevance of multi‐injection and temperature profiles to design multi‐phase reactive processing of polyolefins8citations
  • 2017How penultimate monomer unit effects and initiator choice influence ICAR ATRP of n-butyl acrylate and methyl methacrylate36citations
  • 2015Model-based visualization and understanding of monomer sequence formation in the synthesis of gradient copoly(2-oxazoline)s on the basis of 2-methyl-2-oxazoline and 2-phenyl-2-oxazoline40citations
  • 2015Model-based design of the polymer microstructure : bridging the gap between polymer chemistry and engineeringcitations
  • 2015Model-based design of the polymer microstructure: bridging the gap between polymer chemistry and engineering102citations
  • 2014Fed-batch control and visualization of monomer sequences of individual ICAR ATRP gradient copolymer chains65citations
  • 2012Linear gradient quality of ATRP copolymers144citations

Places of action

Chart of shared publication
Luo, Zheng-Hong
3 / 3 shared
Zhou, Yin-Ning
3 / 3 shared
Figueira, Freddy L.
3 / 3 shared
Dhooge, Dagmar
9 / 25 shared
Alassmy, Yasser A.
1 / 5 shared
Es Sayed, Julien
1 / 6 shared
El Hariri El Nokab, Mustapha
1 / 2 shared
Abduljawad, Marwan M.
1 / 4 shared
Sebakhy, Khaled
2 / 4 shared
Elshewy, Ahmed
1 / 2 shared
Habib, Mohamed H.
1 / 2 shared
Reyes Isaacura, Pablo
1 / 3 shared
Edeleva, Mariya
3 / 17 shared
Wu, Yi-Yang
1 / 1 shared
Marien, Yoshi
7 / 9 shared
Habib, Mohamed
1 / 1 shared
Dhooge, Dagmar R.
12 / 33 shared
Sayed, Julien Es
1 / 4 shared
Hoogenboom, Richard
4 / 45 shared
Conka, Robert
3 / 3 shared
Dobbelaere, Maarten
2 / 2 shared
Van Geem, Kevin
4 / 19 shared
Eschenbacher, Andreas
2 / 7 shared
Dogu, Onur
2 / 2 shared
John Varghese, Robin
1 / 3 shared
Varghese, Robin
1 / 1 shared
De Smit, Kyann
3 / 4 shared
Cardon, Ludwig
1 / 42 shared
Trossaert, Lynn
1 / 2 shared
Amaral Ceretti, Daniel
1 / 7 shared
Ohnmacht, Hannelore
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Fiorillo, Chiara
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Sedlacek, Ondrej
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Dubois, Jean-Luc
1 / 1 shared
Moens, Eli
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Trigilio, Alessandro
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Duchateau, Jan
1 / 1 shared
Hernández Ortiz, Julio César
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Reyniers, Marie-Françoise
7 / 14 shared
Marin, Guy
6 / 29 shared
Schreurs, Fons
1 / 1 shared
Toloza Porras, Carolina
1 / 1 shared
Fierens, Stijn
1 / 1 shared
Verbraeken, Bart
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Derboven, Pieter
2 / 2 shared
Konkolewicz, Dominik
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Zhong, Mingjiang
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Matyjaszewski, Krzysztof
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Wang, Yu
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Co-Authors (by relevance)

  • Luo, Zheng-Hong
  • Zhou, Yin-Ning
  • Figueira, Freddy L.
  • Dhooge, Dagmar
  • Alassmy, Yasser A.
  • Es Sayed, Julien
  • El Hariri El Nokab, Mustapha
  • Abduljawad, Marwan M.
  • Sebakhy, Khaled
  • Elshewy, Ahmed
  • Habib, Mohamed H.
  • Reyes Isaacura, Pablo
  • Edeleva, Mariya
  • Wu, Yi-Yang
  • Marien, Yoshi
  • Habib, Mohamed
  • Dhooge, Dagmar R.
  • Sayed, Julien Es
  • Hoogenboom, Richard
  • Conka, Robert
  • Dobbelaere, Maarten
  • Van Geem, Kevin
  • Eschenbacher, Andreas
  • Dogu, Onur
  • John Varghese, Robin
  • Varghese, Robin
  • De Smit, Kyann
  • Cardon, Ludwig
  • Trossaert, Lynn
  • Amaral Ceretti, Daniel
  • Ohnmacht, Hannelore
  • Fiorillo, Chiara
  • Sedlacek, Ondrej
  • Dubois, Jean-Luc
  • Moens, Eli
  • Trigilio, Alessandro
  • Duchateau, Jan
  • Hernández Ortiz, Julio César
  • Reyniers, Marie-Françoise
  • Marin, Guy
  • Schreurs, Fons
  • Toloza Porras, Carolina
  • Fierens, Stijn
  • Verbraeken, Bart
  • Derboven, Pieter
  • Konkolewicz, Dominik
  • Zhong, Mingjiang
  • Matyjaszewski, Krzysztof
  • Wang, Yu
OrganizationsLocationPeople

article

Multi-angle evaluation of kinetic Monte-Carlo simulations as a tool to evaluate the distributed monomer composition in gradient copolymer synthesis

  • Van Steenberge, Paul
  • Dhooge, Dagmar R.
  • Marien, Yoshi
  • Hoogenboom, Richard
  • Conka, Robert
Abstract

Variations of the comonomer structure and synthesis conditions allow a wide range of comonomer sequences for polymer chains, with copolymer precision control mechanisms (e.g. anionic polymerization, cationic ring opening polymerization (CROP) and reversible deactivation radical polymerization (RDRP)) aiming at well-defined structures, such as gradient, block, and block–gradient–block copolymers. A main challenge remains a generic quality tool for evaluation of a synthesized polymer at a given overall monomer conversion or reaction time, for which recent research has pointed out that matrix-based kinetic Monte Carlo (kMC) simulations are crucial as they provide information on monomer sequences of individual chains. Via post-processing of these individual chains, a structural deviation (SD) distribution can be derived, which represent the number fraction of chains with a given deviation versus an ideally composed chain of a selected compositional target. Historically the average structural deviation (〈SD〉) is the main input for such kMC-based quality control labeling. The present work showcases that a multiangle evaluation is much more recommended, including besides 〈SD〉 calculation, the additional calculation of the SD variance and skewness as well derived characteristics for the segment (SEG) distribution. It is shown that copolymers codefined by non-gradient compositional distributions such as alternating, random, block and homopolymeric chain can have very similar 〈SD〉 = 〈GD〉 (G for gradient) values but still be distinguished by examining the skewness of the GD peak and the SEG distributions. Copolymers with a distinct A/B to B/A transitions show (high) positive GD skewness (3,GD), while values near 0 or negative values indicate no dominant A/B to B/A transition characteristics as the case for alternating, random or homopolymeric copolymers. The average SEG values show the increasing trend: alternating, random, gradient, block, and homopolymer. It is first highlighted that only certain combinations of the kinetic parameters under CROP conditions in the absence of side reactions deliver a certain control over gradient copolymer structure. Due to side reactions the gradient quality significantly decreases, especially due to chain transfer to monomer. Moreover, for the more non-gradient structures also extra SD-based evaluations can be performed using cumulative probability distribution functions to define specific gradient/block proportions.

Topics
  • impedance spectroscopy
  • phase
  • simulation
  • glass
  • glass
  • random
  • copolymer
  • homopolymer
  • block copolymer
  • gradient copolymer