Bayesian determination of the effect of a deep eutectic solvent on the structure of lipid monolayers

TL;DR

This study uses Bayesian modeling to analyze phospholipid monolayers at air-ionic solvent interfaces, revealing their structural similarities to water-based monolayers and advancing understanding of biomolecules in non-aqueous environments.

Contribution

First demonstration of self-assembled phospholipid monolayers in a deep eutectic solvent using Bayesian analysis of reflectometry data.

Findings

Monolayers behave similarly to those on water.

Bayesian modeling determines molecular volumes without water constraints.

Enhanced understanding of parameter correlations in monolayer structure.

Abstract

In this work, we present the first example of the self-assembly of phospholipid monolayers at the interface between air and an ionic solvent. Deep eutectic solvents are a novel class of environmentally friendly non-aqueous room temperature liquids with tunable properties, that have wide ranging potential applications and are capable of promoting the self-assembly of surfactant molecules. We use a chemically-consistent Bayesian modelling of X-ray and neutron reflectometry measurements to show that these monolayers broadly behave as they do on water. This method allows for the monolayer structure to be determined, alongside the molecular volumes of the individual monolayer components without the need for water-specific constraints to be introduced. Furthermore, using this method we are able to better understand the correlations present between parameters in the analytical model. This example of a non-aqueous phospholipid monolayer has important implications for the potential uses of these solvents and for our understanding of how biomolecules behave in the absence of water.