Deep multi-scale modelling of electrified metal oxide nanostructures

One promising solution toward a sustainable society and a green economy is to use metal oxide-based materials.

Metal oxides are a class of inorganic materials that have various energy and environmental applications such as heterogeneous catalyst, fuel cell, lithium-ion battery, supercapacitor, water treatment and antimicrobial application. Most metal oxides are synthesized as nanostructures which leads to unique properties and reduced economical costs.

The very properties that make the metal oxide nanostructures attractive and indispensable in modern science and technology also cause an issue for the environment and human safety. In both the functioning and the degradation of metal oxide nanostructures, aqueous interface plays a vital role.

The metal oxide-aqueous solution aqueous interface is electrified in working conditions due to acid-base chemistry and composed of protonic electric double layer.

Given the importance of metal oxide surfaces in practical applications, surprisingly little is known about the relation between atomic structure of protonic double layer and the interfacial reactivity.

This is largely due to the fact that our knowledge is mostly based on macroscopic observations such as current and concentration in electrochemistry and microscopic information of protonic double layer is difficult to be obtained in experiments.

Therefore, developing a novel deep-learning empowered multi-scale modelling framework and providing a revolutionizing understanding at microscopic level of the functioning and degradation of electrified metal oxide nanostructures are the aims of this proposal.

The outcome of this project will not only lead to the knowledge discovery about the impact of protonic electric double layer on porous metal oxide-based supercapacitors and on the degradation of metal oxide nanoparticles, but it will also propose useful design principles for synthesis and fabrication.