témavezető: Nagy Tibor
helyszín (magyar oldal): Department of Applied Chemistry, E516/A, E517/A, E518, E105 helyszín rövidítés: E518
A kutatási téma leírása:
The diversity of copolymers is reflected in a wide range of where these special materials can be applied. The variety of copolymers is originated from the infinite possible combination of their composition and structure. However, to understand the property-structure relationship, detailed characterization is required. Since a copolymer system consists of various repeating units, average polymer quantities are not enough to obtain a clear insight into the correlation between the polymer quantities and their applicability, thus their distribution in the copolymer chain is essential. One of the most interesting polymer classes is the smart polymers, which can react to changes in their environment. Polymers of acrylamide derivatives have different solubility in water, which is sensitive to temperature. Thus thermoresponsive materials can be synthesized based on these monomers. In order to tune the properties, block copolymers are preferable since the size, composition and selection of the proper monomer combinations result in high variability.
The reversible addition-fragmentation chain transfer (RAFT) polymerization is a controlled polymerization technique that is preferable for the synthesis of block copolymers due to the large variety of suitable monomers. Copolymers with narrow distributions can be prepared, even with short reaction time.
The aim of the research is the development of thermoresponsive block copolymers with sequenced block polymerization and exploring the properties-structure relationship. In order to achieve such goal, numerous copolymers will be synthesized by applying and optimizing the RAFT polymerization technique. Furthermore, several analytical techniques such as GPC, MALDI-TOF-MS, ESI-TOF-MS, NMR, and HPLC-MS will be used for the detailed characterization and DLS, UV-VIS spectrometry to analyze the thermoresponsive properties. To understand the complex behaviors neural network-based machine learning will be applied.
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