Self-healing and mechanical performance of dynamic glycol chitosan hydrogel nanocomposites.

The application of functional self-healing and mechanically robust hydrogels in bioengineering, drug delivery, soft robotics, etc., is continuously growing. However, fabricating hydrogels that simultaneously possess good mechanical and self-healing properties remains a challenge. Developing robust hydrogel formulations for the encapsulation and release of hydrophobic substances is a major challenge especially in some pharmaceutical treatments where the many of drugs show incompatibility with the hydrophilic hydrogel matrices. Schiff base hydrogels have been developed using a benzaldehyde multifunctional amphiphilic polyacrylamide crosslinker in conjunction with glycol chitosan. The polymeric crosslinker was synthesized by a two-step reaction using aqueous Cu-RDRP to give an ABA telechelic copolymer of N,N-dimethyl acrylamide (DMAc) and N-hydroxyethyl acrylamide (HEAm) from a bifunctional PEG. The polymer was then modified by post functionalization leading to a multifunctional benzaldehyde crosslinker that was shown to be capable of self-assembly into aggregates in aqueous media serving as a possible candidate for the entrapment of hydrophobic substances. Aqueous solutions of the crosslinker spontaneously formed hydrogels when mixed with glycol chitosan due to the in situ formation of imine bonds. Hydrogels were characterized while additional comparisons were made with a commonly used bifunctional PEG crosslinker. The effect of introducing partially reduced graphene oxide (GO) nanosheets was also examined and led to enhancements in both mechanical properties (2.0 fold increase in modulus and 1.4 fold increase in strain) and self-healing efficiencies (>99% from 60% by rheology) relative to the pristine polymer hydrogels.

Automatic peak assignment and visualisation of copolymer mass spectrometry data using the 'genetic algorithm'.

Copolymer analysis is vitally important as the materials have a wide variety of applications due to their tunable properties. Processing mass spectrometry data for copolymer samples can be very complex due to the increase in the number of species when the polymer chains are formed by two or more monomeric units. In this paper, we describe the use of the genetic algorithm for automated peak assignment of copolymers synthesised by a variety of polymerisation methods. We find that in using this method we are able to easily assign copolymer spectra in a few minutes and visualise them into heat maps. These heat maps allow us to look qualitatively at the distribution of the chains, by showing how they alter with different polymerisation techniques, and by changing the initial copolymer composition. This methodology is simple to use and requires little user input, which makes it well suited for use by less expert users. The data outputted by the automatic assignment may also allow for more complex data processing in the future.