Metal oxide-water interfaces: the key for tailoring the molecular adsorption in aqueous environment

Adsorption of organic molecules on solid surfaces is fundamental for biosensor, molecular sorting and to predict the toxicity of nanomaterials. Titanium dioxide (TiO2) is widely used as nano-material because of its bio and organic compatibility. Its surface atoms react with water and alter its interfacial structure and thus the adsorption behaviour.

This mechanism can be exploited in order to engineer and drive the adsorption process. Atomistic insight is fundamental to interpret experimental data and understand the organic-chemical-solid affinity. Biological systems are fully hydrated, which makes it difficult to separate interface and bulk signals in experiments.

Computer power and computational methods have evolved to the point where solid-liquid interfaces can be modelled in explicit solvents accounting for the electronic degrees of freedom of the system.

We propose here to use machine learning-based-ab-initio molecular dynamics simulations for the amino acid adsorption on TiO2-water surfaces. Binding free energies are calculated from accelerated sampling and by tailored experimental set ups. NMR chemical shifts for the adsorbing configurations will be also extracted from machine learning and experiments.

The generated data and methodology are expected to push the boundaries of ab-initio-based calculations and the atomistic information will firmly underpin the development of nano-bio models for solid-liquid interfaces.