High performance bio-based thermosets from dimethacrylated epoxidized sucrose soyate (DMESS)

Machine learning analysis of a large set of homopolymers to predict glass transition temperatures

Glass transition temperature of polymers, Tg, is an important thermophysical property, which sometimes can be difficult to measure experimentally. In this regard, data-driven machine learning approaches are important alternatives to assess Tg values, in a high-throughput way. In this study, a large dataset of more than 900 polymers with reported glass transition temperature (Tg) was assembled from various public sources in order to develop a predictive model depicting the structure-property relationships. The collected dataset was curated, explored via cluster analysis, and then split into training and test sets for validation purposes and then polymer structures characterized by molecular descriptors. To find the models, several machine learning techniques, including multiple linear regression (MLR), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), gaussian processes for regression (GPR), and multi-layer perceptron (MLP) were explored. As result, a model with the subset of 15 descriptors accurately predicting the glass transition temperatures was developed. The electronic effect indices were determined to be important properties that positively contribute to the Tg values. The SVM-based model showed the best performance with determination coefficients (R2) of 0.813 and 0.770, for training and test sets, respectively. Also, the SVM model showed the lowest estimation error, RMSE = 0.062. In addition, the developed structure-property model was implemented as a web app to be used as an online computational tool to design and evaluate new homopolymers with desired glass transition profiles.

A Preliminary Environmental Assessment of Epoxidized Sucrose Soyate (ESS)-Based Biocomposite

Biocomposites can be both environmentally and economically beneficial: during their life cycle they generally use and generate less petroleum-based carbon, and when produced from the byproduct of another industry or recycled back to the manufacturing process, they will bring additional economic benefits through contributing to a circular economy. Here we investigate and compare the environmental performance of a biocomposite composed of a soybean oil-based resin (epoxidized sucrose soyate) and flax-based reinforcement using life cycle assessment (LCA) methodology. We evaluate the main environmental impacts that are generated during the production of the bio-based resin used in the biocomposite, as well as the biocomposite itself. We compare the life cycle impacts of the proposed biocomposite to a functionally similar petroleum-based resin and flax fiber reinforced composite, to identify tradeoffs between the environmental performance of the two products. We demonstrate that the bio-based resin (epoxidized sucrose soyate) compared to a conventional (bisphenol A-based) resin shows lower negative environmental impacts in most studied categories. When comparing the biocomposite to the fossil fuel derived composite, it is demonstrated that using epoxidized sucrose soyate versus a bisphenol A (BPA)-based epoxy resin can improve the environmental performance of the composite in most categories except eutrophication and ozone layer depletion. For future designs, considering an alternative cross-linker to facilitate the bond between the bio-based resin and the flax fiber, may help improve the overall environmental performance of the biocomposite. An uncertainty analysis was also performed to evaluate the effect of variation in LCA model inputs on the environmental results for both the biocomposite and composite. The findings show a better overall carbon footprint for the biocomposite compared to the BPA-based composite at almost all times, demonstrating a good potential for marketability especially in the presence of incentives or regulations that address reducing the carbon intensity of products. This analysis allowed us to pinpoint hotspots in the biocomposite's supply chain and recommend future modifications to improve the product's sustainability.

Preparation of Nanoscale Semi-IPNs with an Interconnected Microporous Structure via Cationic Polymerization of Bio-Based Tung Oil in a Homogeneous Solution of Poly(ε-caprolactone)

Nanoscale semi-interpenetrating polymer networks of bio-based poly(ε-caprolactone) (PCL) and polymerized tung oil have been prepared via in situ cationic polymerization and compatibilization in a homogeneous solution. This novel blending technique produced a nanoscale morphology of poly(ε-caprolactone) with average particle sizes as small as 100 nm dispersed in a cross-linked tung oil matrix for 20 and 30 wt % PCL blend compositions. In addition, the exothermic cationic polymerization of tung oil in the presence of the PCL homogeneous solution created a microporous morphology with open three-dimensional interconnected cluster structures. The porous morphology was found to be composition-dependent (the pore size and interconnectivity decreased with increasing PCL content in the blend). The values of the cross-link density and storage modulus in the glassy state for fully cured samples increased significantly and reached a maximum for the 20 wt % PCL blend. This simple, versatile, low-cost strategy for preparing nanoscale and interconnected three-dimensional cluster structures with a microporous morphology and desired properties should be widely applicable for new polymer systems.

New AB type monomers from lignocellulosic biomass

A series of renewable novel bicyclic AB type polyester precursors have been prepared in good overall yield from lignocellulosic biomass. These advancements take full advantage of the differing oxidation states of functional groups in 5-(hydroxymethyl)furfural by chemoselective preparation of furanic hydroxy esters and applying benzyne-Diels–Alder cycloaddition/aromatization strategies.