A Mechanism-Based Reaction-Diffusion Model for Accelerated Discovery of Thermoset Resins Frontally Polymerized by Olefin Metathesis.

Frontal ring-opening metathesis polymerization (FROMP) involves a self-perpetuating exothermic reaction, which enables the rapid and energy-efficient manufacturing of thermoset polymers and composites. Current state-of-the-art reaction-diffusion FROMP models rely on a phenomenological description of the olefin metathesis kinetics, limiting their ability to model the governing thermo-chemical FROMP processes. Furthermore, the existing models are unable to predict the variations in FROMP kinetics with changes in the resin composition and as a result are of limited utility toward accelerated discovery of new resin formulations. In this work, we formulate a chemically meaningful model grounded in the established mechanism of ring-opening metathesis polymerization (ROMP). Our study aims to validate the hypothesis that the ROMP mechanism, applicable to monomer-initiator solutions below 100 °C, remains valid under the nonideal conditions encountered in FROMP, including ambient to >200 °C temperatures, sharp temperature gradients, and neat monomer environments. Through extensive simulations, we demonstrate that our mechanism-based model accurately predicts the FROMP behavior across various resin compositions, including polymerization front velocities and thermal characteristics (e.g., Tmax). Additionally, we introduce a semi-inverse workflow that predicts FROMP behavior from a single experimental data point. Notably, the physiochemical parameters utilized in our model can be obtained through DFT calculations and minimal experiments, highlighting the model's potential for rapid screening of new FROMP chemistries in pursuit of thermoset polymers with superior thermo-chemo-mechanical properties.

Adaptive Data-Driven Deep-Learning Surrogate Model for Frontal Polymerization in Dicyclopentadiene.

Frontal polymerization (FP) is a self-sustaining curing process that enables rapid and energy-efficient manufacturing of thermoset polymers and composites. Computational methods conventionally used to simulate the FP process are time-consuming, and repeating simulations are required for sensitivity analysis, uncertainty quantification, or optimization of the manufacturing process. In this work, we develop an adaptive surrogate deep-learning model for FP of dicyclopentadiene (DCPD), which predicts the evolution of temperature and degree of cure orders of magnitude faster than the finite-element method (FEM). The adaptive algorithm provides a strategy to select training samples efficiently and save computational costs by reducing the redundancy of FEM-based training samples. The adaptive algorithm calculates the residual error of the FP governing equations using automatic differentiation of the deep neural network. A probability density function expressed in terms of the residual error is used to select training samples from the Sobol sequence space. The temperature and degree of cure evolution of each training sample are obtained by a 2D FEM simulation. The adaptive method is more efficient and has a better prediction accuracy than the random sampling method. With the well-trained surrogate neural network, the FP characteristics (front speed, shape, and temperature) can be extracted quickly from the predicted temperature and degree-of-cure fields.

Anisotropic frontal polymerization in a model resin-copper composite.

This work investigates experimentally and numerically frontal polymerization in a thermally anisotropic system with parallel copper strips embedded in 1,6-hexanediol diacrylate resin. Both experiments and multiphysics finite element analyses reveal that the front propagation in the thermally anisotropic system is orientation-dependent, leading to variations in the front shape and the front velocity due to the different front-metal strip interaction mechanisms along and across the metal strips. The parameters entering the cure kinetics model used in this work are chosen to capture the key characteristics of the polymerization front, i.e., the front temperature and velocity. Numerical parametric analyses demonstrate that the front velocity in the directions parallel and perpendicular to the metal strips increases as the system size decreases and approaches the analytical prediction for homogenized systems. A two-dimensional homogenized model for anisotropic frontal polymerization in the metal-resin system is proposed.

Manipulating Frontal Polymerization and Instabilities with Phase-Changing Microparticles.

Recently presented as a rapid and eco-friendly manufacturing method for thermoset polymers and composites, frontal polymerization (FP) experiences thermo-chemical instabilities under certain conditions, leading to visible patterns and spatially dependent material properties. Through numerical analyses and experiments, we demonstrate how the front velocity, temperature, and instability in the frontal polymerization of cyclooctadiene are affected by the presence of poly(caprolactone) microparticles homogeneously mixed with the resin. The phase transformation associated with the melting of the microparticles absorbs some of the exothermic reaction energy generated by the FP, reduces the amplitude and order of the thermal instabilities, and suppresses the front velocity and temperatures. Experimental measurements validate predictions of the dependence of the front velocity and temperature on the microparticle volume fraction provided by the proposed homogenized reaction-diffusion model.

Spontaneous Renal Artery Dissection in COVID-19 Pneumonia: Potential Danger of Cytokine Storm.

The coronavirus disease (COVID-19) pandemic has rapidly spread across the globe since its first detection in March 2020. Its widespread manifestations and vascular complications are increasingly being reported even in young and middle-aged patients. Hyperinflammation is a continuum of host's exaggerated inflammatory response representing cytokine dysregulation/storm which produces coagulopathy and vascular endothelial dysfunction, apart from a prothrombotic state. Cytokine storm or direct viral invasion of the vascular endothelial cells through surface angiotensin-converting enzyme 2 receptors may result in endothelial dysfunction which can potentially result in dissection. Only a few case reports have been published in the literature describing vascular dissection without any inciting factors in COVID-19 patients. Herein, we present the first case report of bilateral renal artery dissection in a 41-year-old male patient who recently recovered from COVID-19 and was managed successfully in stages after many medical hurdles.

ChemNet: A Deep Neural Network for Advanced Composites Manufacturing.

Among advanced manufacturing techniques for fiber-reinforced polymer-matrix composites (FRPCs) which are critical for aerospace, marine, automotive, and energy industries, frontal polymerization (FP) has been recently proposed to save orders of magnitude of time and energy. However, the cure kinetics of the matrix phase, usually a thermosetting polymer, brings difficulty to the design and control of the process. Here, we develop a deep learning model, ChemNet, to solve an inverse problem for predicting and optimizing the cure kinetics parameters of the thermosetting FRPCs for a desired fabrication strategy. ChemNet consists of a fully connected FeedForward 9 layer deep neural network trained on one million examples, and predicts activation energy and reaction enthalpy given the front characteristics such as speed and maximum temperature. ChemNet provides highly accurate predictions measured by the mean squared error (MSE) and by the maximum absolute error (MAE) metrics. ChemNet's performance on the "hidden" test data set had an MSE of 5.58 × 10-6 and a MAE of 1 × 10-3.