ABSTRACT
The design of self-healing agents is a topic of important scientific interest for the development of high-performance materials for coating applications. Herein, two series of copolymers of 2-hydroxyethyl methacrylate (HEMA) with either the hydrophilic N,N-dimethylacrylamide (DMAM) or the epoxy group-bearing hydrophobic glycidyl methacrylate were synthesized and studied as potential self-healing agents of waterborne polyurethanes (WPU). The molar percentage of DMAM or GMA units in the P(HEMA-co-DMAMy) and P(HEMA-co-GMAy) copolymers varies from 0% up to 80%. WPU/polymer composites with a 10% w/w or 20% w/w copolymer content were prepared with the facile method of solution mixing. Thanks to the presence of P(HEMA-co-DMAMy) copolymers, WPU/P(HEMA-co-DMAMy) composite films exhibited surface hydrophilicity (water contact angle studies), and tendency for water uptake (water sorption kinetics studies). In contrast, the surfaces of the WPU/P(HEMA-co-GMAy) composites were less hydrophilic compared with the WPU/P(HEMA-co-DMAMy) ones. The room-temperature, water-mediated self-healing ability of these composites was investigated through addition of water drops on the damaged area. Both copolymer series exhibited healing abilities, with the hydrophilic P(HEMA-co-DMAMy) copolymers being more promising. This green healing procedure, in combination with the simple film fabrication process and simple healing triggering, makes these materials attractive for practical applications.
Subject(s)
Polymers , Polyurethanes , Polymers/chemistry , Temperature , Water/chemistryABSTRACT
Recent advances in the latest generation of MEMS (micro-electro-mechanical system) Fabry-Pérot interferometers (FPI) for near infrared (NIR) wavelengths has led to the development of ultra-fast and low cost NIR sensors with potential to be used by the process industry. One of these miniaturised sensors operating from 1350 to 1650 nm, was integrated into a software platform to monitor a multiphase solid-gas-liquid process, for the production of saturated polyester resins. Twelve batches were run in a 2 L reactor mimicking industrial conditions (24 h process, with temperatures ranging from 220 to 240 °C), using an immersion NIR transmission probe. Because of the multiphase nature of the reaction, strong interference produced by process disturbances such as temperature variations and the presence of solid particles and bubbles in the online spectra required robust pre-processing algorithms and a good long-term stability of the probe. These allowed partial least squares (PLS) regression models to be built for the key analytical parameters acid number and viscosity. In parallel, spectra were also used to build an end-point detection model based on principal component analysis (PCA) for multivariate statistical process control (MSPC). The novel MEMS-FPI sensor combined with robust chemometric analysis proved to be a suitable and affordable alternative for online process monitoring, contributing to sustainability in the process industry.
