A review of current approaches in modeling and simulation of the resin transfer molding (RTM) process is presented. The processing technology of RTM is discussed and some available experimental techniques to monitor the process cycle are presented. A master model is proposed for the entire process cycle consisting of mold filling and curing stages. This master model contains the fundamental and constitutive sub-models for both stages. The key elements of the master model discussed in this study are: flow, heat and mass balance equations for fundamental sub-models, permeability. cure kinetics, resin viscosity and void formation for constitutive sub-models. At the end, numerical methods widely used to simulate the filling process are presented and published simulation results of mold filling and process cycle are reviewed.

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The chain dynamics in supramolecular polymer networks is determined by the interplay of the kinetics of transient interchain association and relaxation of the network chains themselves. This interplay can be addressed by studying model supramolecular polymer networks in which the number of associative side groups and the molar mass of the covalently jointed backbone polymers are both varied systematically. To realize this idea, we use precursor chains with three different molar masses, which comes along with different extents of entanglement in the melt state. For each molar mass, the precursor polymers are functionalized with three different relative contents of associative side groups, giving rise to transient network formation in the melt state. We evaluate the chain dynamics in these transient networks by probing the diffusivity of fluorescently labeled tracer chains by fluorescence recovery after photobleaching (FRAP). In these studies, we find that the presence of entanglements markedly outweighs the influence of transient associative interactions.

At nanoscale length scales, the properties of particles change rapidly with the slightest change in dimension. The use of a microfluidic platform enables precise control of sub-100 nm organic nanoparticles (NPs) based on polybenzimidazole. Using hydrodynamic flow focusing, we can control the size and shape of the NPs, which in turn controls a number of particle material properties. The anhydrous proton-conducting nature of the prepared NPs allowed us to make a high-performance ion exchange membrane for fuel cell applications, and microfluidic tuning of the NPs allowed us subsequently to tune the fuel cell performance.

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