Miniemulsion polymerization reactions of methyl methacrylate (MMA) and vinyl acetate (VAc) for the synthesis of biocompatible polymeric nanoparticles were comparatively studied. 2,2'-Azo-bis(isobutyronitrile) was used as initiator, lecithin as surfactant, and Miglyol 812 or castor oil as costabilizers. Despite the propagation coefficient of VAc being ten times higher than that of MMA, the reaction rates of the VAc polymerizations were much lower. In VAc polymerizations a higher amount of low-molar-mass polymer was formed which may be attributed to transfer reactions to low-molar-mass species. Polymerizations with a high Miglyol 812/VAc ratio were slower and resulted in the formation of a higher amount of low-molar-mass polymer compared to polymerizations with a low Miglyol 812/VAc ratio or VAc miniemulsion polymerizations with hexadecane as costabilizer. In reactions with high Miglyol 812/VAc ratios the peak area of the GPC trace corresponding to the polymer was substantially higher than the theoretical polymer content.
Degradation pathway models constructed using network structural equation modeling (netSEM) are used to study degradation modes and pathways active in photovoltaic (PV) system variants in exposure conditions of high humidity and temperature. This data-driven modeling technique enables the exploration of simultaneous pairwise and multiple regression relationships between variables in which several degradation modes are active in specific variants and exposure conditions. Durable and degrading variants are identified from the netSEM degradation mechanisms and pathways, along with potential ways to mitigate these pathways. A combination of domain knowledge and netSEM modeling shows that corrosion is the primary cause of the power loss in these glass/backsheet PV minimodules. We show successful implementation of netSEM to elucidate the relationships between variables in PV systems and predict a specific service lifetime. The results from pairwise relationships and multiple regression show consistency. This work presents a greater opportunity to be expanded to other materials systems.
Herein, we have employed a supervised learning approach combined with Core-Modified Dissipative Particle Dynamics Simulations (CM-DPD) in order to develop and design a reliable physics-based computational model that will be used in studying confined flow of suspensions. CM-DPD was recently developed and has shown promising performance in capturing rheological behavior of colloidal suspensions; however, the model becomes problematic when the flow of the material is confined between two walls. Wall-penetration by the particles is an unphysical phenomenon that occurs in coarse-grained simulations such as Dissipative Particle Dynamics (DPD) that mostly rely on soft inter-particle interactions. Different solutions to this problem have been proposed in the literature; however, no reports have been given on how to deal with walls using CM-DPD. Due to complexity of interactions and system parameters, designing a realistic simulation model is not a trivial task. Therefore, in this work we have trained a Random Forest (RF) for predicting wall penetration as we vary input parameters such as interaction potentials, flow rate, volume fraction of colloidal particles, and confinement ratio. The RF predictions were compared against simulation tests, and a sufficiently high accuracy and low errors were obtained. This study shows the viability and potentiality of ML combined with DPD to perform parametric studies in complex fluids.
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