Automated, High-Throughput Screening of Hybrid Elastin-like Polypeptide/Polysaccharide Multilayer Film Deposition.

The self-assembled layer-by-layer technique has attracted a great deal of attention as a method for engineering bio-functional surfaces under mild chemical conditions. The production of multilayer films, starting from newly designed building blocks, may be laborious, considering the inherent limitations for anticipating how minimal changes in the macromolecular composition may impact both film deposition and performance. This paper presents an automated, high-throughput approach to depositing polyelectrolyte multilayers (PEMs) in multiwell plates, enabling the screening of nearly 100 film formulations in the same process. This high-throughput layer-by-layer (HT-LbL) method runs in an affordable, fully commercial platform using Python-coded routines that can be easily adapted for the materials science lab settings. The HT-LbL system was validated by investigating the deposition of polysaccharide-based films in multiwell plates, probing the absorbance signal of ionically stained polyelectrolyte multilayers (PEMs) prepared in one single batch. The HT-LbL method was also used to investigate the deposition of PEMs with a small library of genetically engineered elastin-like polypeptides (ELPs) with different levels of ionizable and hydrophobic amino acid residues. The deposition of ELP/chitosan films was assessed based on the signal of fluorescently labeled species (chitosan or ELP-mCherry), demonstrating that both electrostatic and hydrophobic residues are essential for film buildup. The growth and surface properties of ELP-mCherry/chitosan films also seemed susceptible to the assembly pH, forming a higher film growth and a rougher and more hydrophobic surface for both polyelectrolytes deposited under a low ionization degree. Overall, this study illustrates the challenge of predicting the growth and properties of multilayer films and how the HT-LbL can accelerate the development of multilayer films that demand high levels of testing and optimization.