The Competition of Termination and Shielding to Evaluate the Success of Surface-Initiated Reversible Deactivation Radical Polymerization

Abstract

One of the challenges for brush synthesis for advanced bioinspired applications using surface-initiated reversible deactivation radical polymerization (SI-RDRP) is the understanding of the relevance of confinement on the reaction probabilities and specifically the role of termination reactions. The present work puts forward a new matrix-based kinetic Monte Carlo platform with an implicit reaction scheme capable of evaluating the growth pattern of individual free and tethered chains in three-dimensional format during SI-RDRP. For illustration purposes, emphasis is on normal SI-atom transfer radical polymerization, introducing concepts such as the apparent livingness and the molecular height distribution (MHD). The former is determined based on the combination of the disturbing impact of termination (related to conventional livingness) and shielding of deactivated species (additional correction due to hindrance), and the latter allows structure-property relationships to be identified, starting at the molecular level in view of future brush characterization. It is shown that under well-defined SI-RDRP conditions the contribution of (shorter) hindered dormant chains is relevant and more pronounced for higher average initiator coverages, despite the fraction of dead chains being less. A dominance of surface-solution termination is also put forward, considering two extreme diffusion modes, i.e., translational and segmental. With the translational mode termination is largely suppressed and the living limit is mimicked, whereas with the segmental mode termination occurs more and the termination front moves upward alongside the polymer layer growth. In any case, bimodalities are established for the tethered chains both on the level of the chain length distribution and the MHD.

Differences and similarities between mono-, bi- or tetrafunctional initiated cationic ring-opening polymerization of 2-oxazolines

“…In the next stage, these trained models can be used to explore different sets of conditions providing accurate predictive results with a strong kinetic foundation. This has been illustrated in previous contributions from our group 30,[38][39][40]44,[46][47][48][49][50][51] and other research groups. [52][53][54][55][56][57] Previous work also included the design of CROP witnessing the growth of linear to slightly branched homoand copolymer chains, while striving for an optimum between the reaction time and the control over molecular properties.…”

Going Beyond the Carothers, Flory and Stockmayer Equation by Including Cyclization Reactions and Mobility Constraints

“…However, conventional lattice-based models are treating time dependencies in a simplified manner. They are preferably extended considering kinetic rules such as varying reaction probabilities, as recently done in the field of polymer brush synthesis [ 85 , 86 ].…”

Conformational Variations for Surface-Initiated Reversible Deactivation Radical Polymerization: From Flat to Curved Nanoparticle Surfaces

“…Mostly the monomer unit connections and movements are based on the so-called three-dimensional (3D) bond fluctuation model (BFM) and the dynamic lattice liquid model (DLL). 57−66 38,39 (a) SI-RDRP lab-scale polymerization setup, in which the spherical nanoparticles are dispersed in a deoxygenated reaction mixture. (b) Selection of spherical nanoparticles for the modeling properly reflecting the kinetics at the surface level and in the solution that makes up the vicinity of nanoparticles (region included in the dotted circle).…”

“…modeling has been interconnected with BFM for SIP with flat surfaces to allow also in the stochastic approach a detailed understanding of the different probabilities of reaction, and thus time dependencies. 38,39 These kMC simulations have illustrated how (discrete) confinement effects at the surface level result in complex polymer layers. These layers are composed of hindered ("buried") shorter chains and longer (dormant/active) chains that grow well beyond the average layer height, leading to bimodal (number) chain length distributions.…”