Complex Morphology of the Intermediate Phase in Block Copolymers and Semicrystalline Polymers As Revealed by ¹H NMR Spin Diffusion Experiments

Abstract

Nanostructured multiphase polymers exhibiting a mobile and a rigid phase also contain a phase of intermediate mobility that is usually assumed to be a continuous, uninterrupted interphase layer. This assumption is contrary to recent molecular-resolution micrographs and contradicts results from NMR spin diffusion experiments, all of which suggest a nontrivial interface structure. In this contribution, we reconsider our previous 1 H NMR spin diffusion data sets (Roos et al. Colloid. Polym. Sci. 2014, 292, 1825 and perform optimized 2D and 3D numerical spin diffusion calculations to characterize the basic intermediate-phase morphological pattern, thus overcoming previous inconsistencies in data fitting. For the diblock copolymer poly(butadiene)-poly(styrene), PS-b-PB, we demonstrate that the interphase region comprises nanometersize intermixed immobile, intermediate and mobile subregions. In contrast, for the semicrystalline polymer poly(ε-caprolactone), PCL, the spin diffusion data are best reproduced by an intermediate phase that is fully embedded within the rigid phase, which is attributed to an imperfect crystalline structure. For both samples, the new findings reveal a complex discontinuous, dynamically inhomogeneous structure of the intermediate phase.

Time Domain NMR in Polymer Science: From the Laboratory to the Industry

“…That was explained assuming a direct contact between rigid and mobile phase, with the interphase not forming a contiguous in-between layer, but an island-like distribution immersed completely inside the rigid phase. Further simulations favored this vision for semi-crystalline polymers, while block copolymers were better represented by a mixed-interphase model, where dynamic inhomogeneities are present on the nanometer scale [85].…”

Dynamic Heterogeneity of Filler‐Associated Interphases in Polymer Nanocomposites

“…Recently, we have shown that bidirectional 1 H NMR spin diffusion (SD) experiments [ 38–41 ] are highly sensitive to how regions of different mobility are connected to each other. [ 42,43 ] In these experiments, magnetization transfer from rigid to mobile domains are compared to magnetization flow in the opposite direction, from mobile to rigid domains. [ 39 ] For phase‐separated block‐copolymers comprising mobile and a glassy polymer phase (PS‐PB), the technique revealed asymmetric magnetization flow that could only be reproduced by considering intermixed immobile, intermediate, and mobile domains in the rigid‐mobile transition area.…”

“…We focus on a set of so far unpublished results obtained several years ago for poly(ethyl acrylate) rubber filled with well‐dispersed silica, [ 22,25 ] for which the T g gradient model was proven to apply. [ 9 ] We extend our previous simulation model for SD data developed for layered systems [ 43 ] to accommodate the spatial requirements in PNC with spherical filler particles. The global fits to the whole data set do not allow for a clear preference of the shell versus the dynamic‐heterogeneity model, but the relevant early stage of the SD process in our samples shows clear indications of the existence of lateral dynamic inhomogeneities in our samples.…”

Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials

“…PCL has a high-order structure of mobile, rigid, and interphase [ 28 , 33 ]. Evaluating the structure, motility, and proportion of multiple domains is important for material development including such as the optimization of physical properties.…”

“…Therefore, the application of signal deconvolution to measure solid-state NMR data is an important challenge to extract hidden information in the NMR spectra of macromolecular samples with multiple phases and components. Several methods for spectral separation [ 32 ], apodization, zero filling, linear prediction, fitting and numerical simulation [ 33 ], such as covariance analysis [ 34 ], SIMPSON [ 35 ], SPINEVOLUTION [ 36 ], dmfit [ 37 ], EASY-GOING deconvolution [ 38 ], INFOS [ 39 ], Fityk [ 40 ], ssNake [ 41 ], the noise reduction method based on principal component analysis [ 42 ], and the signal deconvolution method that combines short-time Fourier transform (STFT, a time–frequency analytical method), and probabilistic sparse matrix factorization (PSMF which is one of the non-negative matrix factorizations) [ 43 ] were developed as computational approaches to measured data.…”