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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Van Steenberge, Paul
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 Carlo
- 2024Surfactant-free peroxidase-mediated enzymatic polymerization of a biorenewable butyrolactone monomer via a green approach : synthesis of sustainable biobased latexescitations
- 2024Combining ternary phase diagrams and multiphase coupled matrix-based Monte Carlo to model phase dependent compositional and molar mass variations in high impact polystyrene synthesiscitations
- 2024Exploring the influence of polybutadiene content on high-impact polystyrene properties : a multiphase coupled matrix-based Monte Carlo approach
- 2023Surfactant-Free Peroxidase-Mediated Enzymatic Polymerization of a Biorenewable Butyrolactone Monomer via a Green Approach: Synthesis of Sustainable Biobased Latexes
- 2023Multi-angle evaluation of kinetic Monte-Carlo simulations as a tool to evaluate the distributed monomer composition in gradient copolymer synthesiscitations
- 2023Bayesian tuned kinetic Monte Carlo modeling of polystyrene pyrolysis : unraveling the pathways to its monomer, dimers, and trimers formationcitations
- 2023Bayesian tuned kinetic Monte Carlo modeling of polystyrene pyrolysis : unraveling the pathways to its monomer, dimers, and trimers formationcitations
- 2023Playing with process conditions to increase the industrial sustainability of poly(lactic acid)-based materialscitations
- 2023Comparing thermal degradation for fused filament fabrication (FFF) with chain or step-growth polymers
- 2022Identifying optimal synthesis protocols via the in silico characterization of (a)symmetric block and gradient copolymers with linear and branched chains
- 2022A unified kinetic Monte Carlo approach to evaluate (a)symmetric block and gradient copolymers with linear and branched chains illustrated for poly(2-oxazoline)scitations
- 2020Connecting polymer synthesis and chemical recycling on a chain-by-chain basis : a unified matrix-based kinetic Monte Carlo strategycitations
- 2020Progress in reaction mechanisms and reactor technologies for thermochemical recycling of poly(methyl methacrylate)citations
- 2019The relevance of multi‐injection and temperature profiles to design multi‐phase reactive processing of polyolefinscitations
- 2017How penultimate monomer unit effects and initiator choice influence ICAR ATRP of n-butyl acrylate and methyl methacrylatecitations
- 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-oxazolinecitations
- 2015Model-based design of the polymer microstructure : bridging the gap between polymer chemistry and engineering
- 2015Model-based design of the polymer microstructure: bridging the gap between polymer chemistry and engineeringcitations
- 2014Fed-batch control and visualization of monomer sequences of individual ICAR ATRP gradient copolymer chainscitations
- 2012Linear gradient quality of ATRP copolymerscitations
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article
Multi-angle evaluation of kinetic Monte-Carlo simulations as a tool to evaluate the distributed monomer composition in gradient copolymer synthesis
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.