Oxynitride perovskites have shown great potential as catalysts to split water in oxygen and hydrogen using solar light. It has also been demonstrated that by engineering strain in these materials, it is possible way to reduce the overpotentials needed to split water, and, in particular, for the Oxygen Evolution Reaction (OER), which is the bottleneck in the water splitting reactions. Recently, new oxynitrides, such as InSnO2N, have been suggested as a catalyst for water splitting, which combines optimal light harvesting properties with ferroelectricity, which can help in increasing the charge separation and the photo-voltage obtainable from these material. In this project, the student will identify optimal strain conditions to apply to the photocatalyst for reducing the overpotentials for OER. The student will use Density Functional Theory (DFT) to estimate bulk and surface properties.
One-dimensional inorganic nanotubes hold promise for technological applications due to their distinct physical/chemical properties, but so far advancements have been hampered by difficulties in producing single-wall nanotubes with a well-defined radius. Recently, we have investigated the formation mechanism of 135 different inorganic nanotubes formed by the intrinsic self-rolling driving force found in asymmetric 2D Janus sheets in the 1T and 2H prototypes. In this project, the student will screen the literature for more 2D Janus prototypes and apply an autonomous workflows, in the framework of DFT, to estimate the most stable radii of the nanotubes obtained by rolling up the 2D Janus sheet.
We have recently shown that nanometric inorganic nanotubes can be formed by rolling up 2D Janus monolayers. The broken symmetry between the top and bottom side of the 2D layer generates a planar strain, which drives the rolling of the 2D sheet into a 1D nanotube. In some cases, the stain is not enough to generate a single-wall 1D tube. On the other side, the stability can be enhanced by creating a multi-walled tube or a scroll. In this project, the student will study, using DFT, the stability and electronic properties of the bi-wall nanotubes and beyond, addressing the question of how properties change as a function of the number of walls in 1D nanotubes and how they correlate with the 2D counterpart.
Carbon and inorganic nanotubes can be used as solid lubricants. Their two main limitations are that their coefficients of friction are higher than the ones of liquid lubricants and that, once damaged, the dolid lubricant need to be replaces, which can be impossible in some devices. In this project, the student will investigate the class of Janus thin films to identify nanostructures with low friction coefficients and where the 2D sheet (which can be formed by damaging the thin film) can self-roll into a nanotube, thus increasing the lubricant properties of the interface.
Usign a similar approach as we have applied to study the formation of 1D nanotubes from 2D Janus sheets, the student will investigate the formation of 0D inorganic fullerenes from 2D Janus layers and compare them with the 1D counterpart.
Multivalent batteries such as magnesium and calcium batteries constitutes an example of promising, alternative non-Li energy storage systems. One of the key challenges for a broader use of these technology is the limited capacity and voltage associated with the current state-of-the-art materials. In this project, the student will use density functional theory (DFT) and a recent implemented workflow to identify possible new cathode materials for multivalent batteries. The student will calculate relevant properties such as the theoretical battery open circuit voltage and ion diffusion properties. If the cathode performs better than the state-of-the-art materials, the student will proceed with the more accurate calculations. Possible candidate materials will be investigated experimentally by some of our experimental partners.
A metamaterial is a material engineered to show properties that are not found in its naturally occurring form. Metamaterials are made from assembling multiple elements, or building blocks, arranged in repeating patterns, generally at smaller scales than the phenomena they influence. Metamaterials thus derive their properties not only from the properties of the base materials (atoms and bonds), as for conventional compounds, but from their newly designed, well defined structures. A conventional material, for example, is compressed under pressure. Despite the large potential, metamaterials have not been designed yet to improve electrochemical devices. In this project, the student will design building block based on carbon for metamaterials using quantum mechanical calculations in the framework of Density Functional Theory. The goal is to identify building blocks that have potential to be used as anode materials in Li-ion batteries to improve the storage capacity and charge properties of graphite. Properties like Li adsorption energy, Li coverage, and diffusion barriers will be descriptors to identify promising structures. Once that the building blocks have been identified, we will proceed to build a complex metamaterial using artificial intelligence tools.
Perovskite materials have shown a manifold of exciting properties and have been used in multiple applications, from electronics to solar cells, from catalysis to batteries. Antiperovskites have a similar crystal structure, but the position of the cations and anions is reverted. This has the effect of enhancing their functionalities paying off a reduction in the overall stability. Despite the numerous potential applications, a library of antiperovskites, together with a fundamental understanding of their physico-chemical properties, is still missing. Beyond perovskites, other structures, such as spinel, can sustain the inversion of the charge. In this project, the student will discovery simple rules to design stable antiperovskite materials by calculating their properties using Density Functional Theory (DFT).
A solid oxide fuel cell (SOFC) is an electrochemical conversion device that produces electricity directly from oxidizing a fuel, such as water from oxygen and hydrogen molecules. Several materials have been proposed as SOFC cathodes, like, for example, LaSrCoO3 and its doped structures. Very recently it has been shown that the perovskite material Zr0.4Ce0.4Y0.1Yb0.1O3 (BZCYYb4411) has significantly improved the current state-of-the-art of cathode materials because of its high stability and catalytic properties. The main hypothesis here is that the high-entropy of this and similar materials plays a key role to stabilize the structure and in creating the electrochemical conditions for the good catalytic properties. In this project, the student will study the structural and catalytic properties of some of the most promising high-entropy SOFC materials using Density Functional Theory (DFT) and Artificial Intelligence (AI) in the framework of the Cluster Expansion Method. AI is required to study the exact crystal structure of the materials and how the various elements interact with each other. In addition to explain the properties of these materials, the goal of the project is to identify trends and descriptors for designing novel, improved high-entropy oxides. Experimental and industrial collaborators will synthesize the discovered materials.