Redox properties of polyelectrolyte multilayer modified electrodes: a significant effect of the interactions between the polyelectrolyte layers in the films

Prediction of coating thickness for polyelectrolyte multilayers via machine learning

Layer-by-layer (LbL) deposition method of polyelectrolytes is a versatile way of developing functional nanoscale coatings. Even though the mechanisms of LbL film development are well-established, currently there are no predictive models that can link film components with their final properties. The current health crisis has shown the importance of accelerated development of biomedical solutions such as antiviral coatings, and the implementation of machine learning methodologies for coating development can enable achieving this. In this work, using literature data and newly generated experimental results, we first analyzed the relative impact of 23 coating parameters on the coating thickness. Next, a predictive model has been developed using aforementioned parameters and molecular descriptors of polymers from the DeepChem library. Model performance was limited because of insufficient number of data points in the training set, due to the scarce availability of data in the literature. Despite this limitation, we demonstrate, for the first time, utilization of machine learning for prediction of LbL coating properties. It can decrease the time necessary to obtain functional coating with desired properties, as well as decrease experimental costs and enable the fast first response to crisis situations (such as pandemics) where coatings can positively contribute. Besides coating thickness, which was selected as an output value in this study, machine learning approach can be potentially used to predict functional properties of multilayer coatings, e.g. biocompatibility, cell adhesive, antibacterial, antiviral or anti-inflammatory properties.

Bioelectrochemical Properties of Enzyme-Containing Multilayer Polyelectrolyte Microcapsules Modified with Multiwalled Carbon Nanotubes

This work investigated changes in the biochemical parameters of multilayer membrane structures, emerging at their modification with multiwalled carbon nanotubes (MWCNTs). The structures were represented by polyelectrolyte microcapsules (PMCs) containing glucose oxidase (GOx). PMCs were made using sodium polystyrene sulfonate (polyanion) and poly(allylamine hydrochloride) (polycation). Three compositions were considered: with MWCNTs incorporated between polyelectrolyte layers; with MWCNTs inserted into the hollow of the microcapsule; and with MWCNTs incorporated simultaneously into the hollow and between polyelectrolyte layers. The impedance spectra showed modifications using MWCNTs to cause a significant decrease in the PMC active resistance from 2560 to 25 kOhm. The cyclic current-voltage curves featured a current rise at modifications of multilayer MWCNT structures. A PMC-based composition was the basis of a receptor element of an amperometric biosensor. The sensitivity of glucose detection by the biosensor was 0.30 and 0.05 μA/mM for PMCs/MWCNTs/GOx and PMCs/GOx compositions, respectively. The biosensor was insensitive to the presence of ethanol or citric acid in the sample. Polyelectrolyte microcapsules based on a multilayer membrane incorporating the enzyme and MWCNTs can be efficient in developing biosensors and microbial fuel cells.