A commercially viable solution process to control long-chain branching in polyethylene.

In polyolefins, long-chain branching is introduced through an energy-intensive, high-pressure radical process to form low-density polyethylene (LDPE). In the current work, we demonstrated a ladder-like polyethylene architecture through solution polymerization of ethylene and less than 1 mole % of α,ω-dienes, using a dual-chain catalyst. The ladder-branching mechanism requires catalysts with two growing polymer chains on the same metal center, thus enchaining the diene without the requirement of a steady-state concentration of pendant vinyl groups. Molecular weight distributions lacking a high-molecular weight tail, distinctive Mark-Houwink signatures, nuclear magnetic resonance characterization, and shear and extensional rheology consistent with highly branched polyethylene architectures are described. This approach represents an industrially viable solution-polymerization process capable of producing controlled long-chain branched polyethylene with rheological properties comparable to those of LDPE or its blends with linear low-density polyethylene (LLDPE).

Experiments and Modeling of Flow-Enhanced Nucleation in LLDPE.

A computational and experimental framework for quantifying flow-enhanced nucleation (FEN) in polymers is presented and demonstrated for an industrial-grade linear low-density polyethylene (LLDPE). Experimentally, kinetic measurements of isothermal crystallization were performed by using fast-scanning calorimetry (FSC) for melts that were presheared at various strain rates. The effect of shear on the average conformation tensor of the melt was modeled with the discrete slip-link model (DSM). The conformation tensor was then related to the acceleration in nucleation kinetics by using an expression previously validated with nonequilibrium molecular dynamics (NEMD). The expression is based on the nematic order tensor of Kuhn segments, which can be obtained from the conformation tensor of entanglement strands. The single adjustable parameter of the model was determined by fitting to the experimental FSC data. This expression accurately describes FEN for the LLDPE, representing a significant advancement toward the development of a fully integrated processing model for crystallizable polymers.

Design of Nonideal Eutectic Mixtures Based on Correlations with Molecular Properties.

In this work, a statistical analysis was performed to reveal how the molecular properties are correlated with the nonideal behavior observed in eutectic mixtures. From this, a statistical model, combined with theory and experimental results, was developed to predict the nonideal behavior of a specific set of eutectic mixtures, consisting of quaternary ammonium bromides with dicarboxylic acids and polyols. The combination of this analysis and this model can be considered as a first step toward the a priori design of eutectic mixtures. The analysis performed is based on principal components. The descriptors used for this are molecular properties of the constituents of these mixtures. The molecular properties are a combination of experimental, theoretical, and computed properties. The analysis reveals that there are strong correlations between the nonideality of the mixtures and a measure of the acidity of the hydrogen bond donating protons, the displacement of the bromide anion, and the bulkiness of the quaternary ammonium salt. Our analysis highlights the design rules of deep eutectic systems (DES), enabling control over the extent of the liquid window. Our model enables prediction of the eutectic temperature for a range of related mixtures.

Rheology of Crystallizing LLDPE.

Polymer crystallization occurs in many plastic manufacturing processes, from injection molding to film blowing. Linear low-density polyethylene (LLDPE) is one of the most commonly processed polymers, wherein the type and extent of short-chain branching (SCB) may be varied to influence crystallization. In this work, we report simultaneous measurements of the rheology and Raman spectra, using a Rheo-Raman microscope, for two industrial-grade LLDPEs undergoing crystallization. These polymers are characterized by broad polydispersity, SCB and the presence of polymer chain entanglements. The rheological behavior of these entangled LLDPE melts is modeled as a function of crystallinity using a slip-link model. The partially crystallized melt is represented by a blend of linear chains with either free or crosslinked ends, wherein the crosslinks represent attachment to growing crystallites, and a modulus shift factor that increases with degree of crystallinity. In contrast to our previous application of the slip-link model to isotactic polypropylene (iPP), in which the introduction of only bridging segments with crosslinks at both ends was sufficient to describe the available data, for these LLDPEs we find it necessary to introduce dangling segments, with crosslinks at only one end. The model captures quantitatively the evolution of viscosity and elasticity with crystallization over the whole range of frequencies in the linear regime for two LLDPE grades.

Linking models of polymerization and dynamics to predict branched polymer structure and flow.

We present a predictive scheme connecting the topological structure of highly branched entangled polymers, with industrial-level complexity, to the emergent viscoelasticity of the polymer melt. The scheme is able to calculate the linear and nonlinear viscoelasticity of a stochastically branched "high-pressure free radical" polymer melt as a function of the chemical kinetics of its formation. The method combines numerical simulation of polymerization with the tube/entanglement physics of polymer dynamics extended to fully nonlinear response. We compare calculations for a series of low-density polyethylenes with experiments on structural and viscoelastic properties. The method provides a window onto the molecular processes responsible for the optimized rheology of these melts, connecting fundamental science to process in complex flow, and opens up the in silico design of new materials.