Nonideality in Silicone Network Formation via Solvent Swelling and ¹H Double-Quantum NMR

Abstract

The versatile cross-linking chemistry of poly­(dimethylsiloxane) (PDMS)-based materials affords a large research space in which polymers with widely varying elastomeric properties may be synthesized. Parameters such as chain length, cross-link density, cross-link functionality, filler content, and chain chemistry can all be modified to produce materials with specific physical and mechanical properties. Commercial polysiloxane-based, “silicone” elastomers are generally intractable, which makes the precise characterization of their networks problematic. We report here the application of equilibrium solvent uptake analysis and 1H double-quantum nuclear magnetic resonance (1H DQ NMR) spectroscopy to determine the network topology of end-linked PDMS networks with nonideal network topology. Despite their structural complexity, we can quantify both the classical and nonclassical contributions to network structure using 1H DQ NMR which are in reasonable agreement with solvent uptake data. These findings serve as the foundation for future investigations of even more complex commercial silicones using 1H DQ NMR.

Spectroscopic Signatures of MQ-Resins in Silicone Elastomers

“…Given the large variability of the measurement itself, the change in soluble fraction of the materials is not statistically significant. When compared to previous studies, our 0% MQ sample has nearly double the soluble fraction (4.8 versus 2.8%) of a model network sample made with a 32 kDa PDMS prepolymer that was demonstrated to have a very low defect fraction . The difference in soluble fraction between the two model network samples is likely due to a change in the cross-linker in these new formulations.…”

“…The actual MQ content of each sample along with solvent uptake and mechanical data taken on the samples are displayed in Table . Solvent uptake experiments were used to determine network structure parameters such as the soluble fraction of material (% ω sol ) and the equilibrium degree of swelling ( Q ) as well as to compare the network structure of our materials to those previously studied. , We also use the network properties determined from swelling and mechanical data as a standard metric for the network dynamics determined by NMR spectroscopy.…”

“…For example, polysiloxane mixing conditions such as the presence of excess cross-linker, moisture, and the implementation of an elevated-temperature postcuring step can lead to unwanted condensation reactions that produce silicone-resins within the network. As a result, many silicone networks can have unintended silicone-resin species present that can lead to nonideal behavior. − …”

“…A sequence based on the generation of a signal through repetitive CP contact times (so-called MultiCP) has shown to ameliorate some of these motional effects and has shown promise in enhancing the 13 C­{ 1 H} CP/MAS signal for partially mobile polyolefins . In addition, much effort has been recently devoted to the use of static 1 H NMR methods and double-quantum NMR (DQ NMR) methods, in particular, to quantify the segmental dynamics of silicone networks as a function of formulation, filler content, and aging as well as to gain insight into structure–property relationships in this ubiquitous family of elastomers. ,− …”

Age-aware constitutive materials model for a 3D printed polymeric foam

“…For the printed foam, we observe roughly the same activation barrier (Δ E ~ 83–86 kJ/mol) for the evolution of all four properties (i.e., CS , LR , μ 10 , and μ 11 ), which is consistent with a single dominant molecular/network level mechanism governing the aging of all such properties. However, to associate the obtained activation barrier with specific structure-relaxation or damage-formation modes in a complex macromolecular system such as a filled polymeric network is non-trivial, and warrants further analysis, e.g., analyzing microstructural changes in X-ray computer-tomography images 32–34 , attempting to assess changes in molar mass distributions and chain entanglements through methods such as mechanical 35 , dielectric spectroscopy 36 , solid state NMR 37 and solvent-swelling experiments 38,39 , as well as realistic multiscale simulations 40 .…”