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Synthesis of Unsaturated Polyester Resins from Various Bio-Derived Platform Molecules.

ABSTRACT: Utilisation of bio-derived platform molecules in polymer synthesis has advantages which are, broadly, twofold; to digress from crude oil dependence of the polymer industry and secondly to reduce the environmental impact of the polymer synthesis through the inherent functionality of the bio-derived platform molecules. Bulk polymerisation of bio-derived unsaturated di-acids has been employed to produce unsaturated polyester (UPEs) which have been analysed by GPC, TGA, DSC and NMR spectroscopy, advancing on the analysis previously reported. UPEs from the diesters of itaconic, succinic, and fumaric acids were successfully synthesised with various diols and polyols to afford resins of MN 480-477,000 and Tg of -30.1 to -16.6 °C with solubilities differing based on starting monomers. This range of properties allows for many applications and importantly due to the surviving Michael acceptor moieties, solubility and cross-linking can be specifically tailored, post polymerisation, to the desired function. An improved synthesis of itaconate and succinate co-polymers, via the initial formation of an itaconate bis-diol, is also demonstrated for the first time, resulting in significantly improved itaconate incorporation.

SUBMITTER: Farmer TJ

PROVIDER: S-EPMC4519879 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Synthesis of Unsaturated Polyester Resins from Various Bio-Derived Platform Molecules.

Farmer Thomas J TJ Castle Rachael L RL Clark James H JH Macquarrie Duncan J DJ

International journal of molecular sciences 20150702 7

Utilisation of bio-derived platform molecules in polymer synthesis has advantages which are, broadly, twofold; to digress from crude oil dependence of the polymer industry and secondly to reduce the environmental impact of the polymer synthesis through the inherent functionality of the bio-derived platform molecules. Bulk polymerisation of bio-derived unsaturated di-acids has been employed to produce unsaturated polyester (UPEs) which have been analysed by GPC, TGA, DSC and NMR spectroscopy, adv ...[more]

PMID: 26147423

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Project description:Unsaturated polyester resins are widely used for the preparation of composite materials and fulfill the majority of practical requirements for industrial and domestic applications at low cost. These resins consist of a highly viscous polyester oligomer and a reactive diluent, which allows its process ability and its crosslinking. The viscosity of the initial polyester and the reactive diluent mixture is critical for practical applications. So far, these viscosities were determined by trial and error which implies a time-consuming succession of manipulations, to achieve the targeted viscosities. In this work, we developed a strategy for predicting the viscosities of unsaturated polyesters formulation based on neural networks. In a first step 15 unsaturated polyesters have been synthesized through high-temperature polycondensation using usual monomers. Experimental Hansen solubility parameters (HSP) were determined from solubility experiment with HSPiP software and glass transition temperatures (T g ) were measured by Differential Scanning Calorimetry (DSC). Quantitative Structure-Property Relationship (QSPR) coupled to multiple linear regressions have been used to get a prediction of Hansen solubility parameters δd, δ p , and δ h from structural composition. A second QSPR regression has been done on glass transition temperature (prediction vs. experimental coefficient of determination R 2 = 0.93) of these unsaturated polyesters. These unsaturated polyesters were next diluted in several solvents with different natures (ethers, esters, alcohol, aromatics for example) at different concentrations. Viscosities at room temperature of these polyesters in solution were finally measured in order to create a database of 220 entries with 7 descriptors (polyester molecular weight, T g , dispersity index Ð, polyester-solvent HSP RED, molar volume of the solvent, δ h of the solvent, concentration of polyester in solvent). The QSPR method for predicting the viscosity from these 6 descriptors proved to be ineffective (R 2 = 0.56) as viscosities exhibit non-linear phenomena. A Neural Network with an optimized number of 12 hidden neurons has been trained with 179 entries to predict the viscosity. A correlation between experimental and predicted viscosities based on 41 testing instances gave a correlation coefficient R 2 of 0.88 and a predicted vs. measured slope of 0.98. Thanks to Neural Networks, new developments with eco-friendly reactive diluents can be accelerated.

Dataset Information

Synthesis of Unsaturated Polyester Resins from Various Bio-Derived Platform Molecules.

Publications

Synthesis of Unsaturated Polyester Resins from Various Bio-Derived Platform Molecules.

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