EXPRESS: Visualization of Inter- and Intramolecular Interactions in Poly(3-hydroxybutyrate)/Poly(L-lactic acid) (PHB/PLLA) Blends During Isothermal Melt Crystallization Using Attenuated Reflection Fourier Transform infrared (ATR FT-IR) Spectroscopic Imaging

Inter- and intramolecular interactions in multicomponent polymer systems influence their physical and chemical properties significantly and thus have implications on their synthesis and processing. In the present study, chemical images were obtained by plotting the peak position of a spectral band from the datasets generated by in-situ ATR-FTIR spectroscopic imaging. This approach was successfully used to visualize changes in intra- and intermolecular interactions in Poly(3-hydroxybutyrate)/Poly(L-lactic acid) (PHB/PLLA) blends during the isothermal melt crystallization. The peak position of ν (C=O) band, which reflects the nature of the intermolecular interaction, shows that the intermolecular interactions between PHB and PLLA in the miscible state (1733 cm-1) changes to the inter- and intramolecular interaction (CH3∙∙∙O=C, 1720 cm-1) within PHB crystal during the isothermal melt crystallization. Compared with spectroscopic images obtained by plotting the distribution of absorbance of spectral bands, which reveals the spatial distribution of blend components, the approach of plotting the peak position of a spectral band reflects the spatial distribution of different intra- and intermolecular interactions. With the process of isothermal melt-crystallization, the disappearance of the intermolecular interaction between PHB and PLLA and the appearance of the inter- and intramolecular interactions within the PHB crystal were both visualized through the images based on the observation of the band position. This work shows the potential of using in-situ ATR-FTIR spectroscopic imaging to visualize different types of inter- or intramolecular interactions between polymer molecules or between polymer and other additives in various types of multicomponent polymer systems.  

Scattering of multicomponent polymer blends

The random phase approximation for polymer blends was developed by H. Benoît and described small angle scattering functions as well as mean field phase boundaries. It is a pure mean field theory that loses validity close to the real phase boundaries due to strong fluctuations. However, it gives a very clear roadmap about phase diagrams and scattering functions. A simplification of the random phase approximation is discussed that comes into effect when several polymers are mixed that involve a rather low number of chemically different repeat units. Then, the correlation functions of the same repeat unit pairs can be added up in a specific way such that the overall complexity for the calculations is reduced. The scattering functions and mean field phase boundaries are discussed within this concept. Graphical abstract

Numerical simulation, clustering, and prediction of multicomponent polymer precipitation

Multicomponent polymer systems are of interest in organic photovoltaic and drug delivery applications, among others where diverse morphologies influence performance. An improved understanding of morphology classification, driven by composition-informed prediction tools, will aid polymer engineering practice. We use a modified Cahn–Hilliard model to simulate polymer precipitation. Such physics-based models require high-performance computations that prevent rapid prototyping and iteration in engineering settings. To reduce the required computational costs, we apply machine learning (ML) techniques for clustering and consequent prediction of the simulated polymer-blend images in conjunction with simulations. Integrating ML and simulations in such a manner reduces the number of simulations needed to map out the morphology of polymer blends as a function of input parameters and also generates a data set which can be used by others to this end. We explore dimensionality reduction, via principal component analysis and autoencoder techniques, and analyze the resulting morphology clusters. Supervised ML using Gaussian process classification was subsequently used to predict morphology clusters according to species molar fraction and interaction parameter inputs. Manual pattern clustering yielded the best results, but ML techniques were able to predict the morphology of polymer blends with ≥90% accuracy.