Engineering topics
Ongari, Daniele
Publications and source records attributed to Ongari, Daniele.
Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening
Not Available
Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution
Finding the best material for a specific application is the ultimate goal of materials discovery. However, there is also the reverse problem: when experimental groups discover a new material, they would like to know all the possible applications this material would be promising for. Computational modeling can aim to fulfill this expectation, thanks to the sustained growth of computing power and the collective engagement of the scientific community in developing more efficient and accurate workflows for predicting materials' performances. We discuss the impact that reproducibility and automation of the modeling protocols have on the field of gas adsorption in nanoporous crystals. We envision a platform that combines these tools and enables effective matching between promising materials and industrial applications.
In Silico Discovery of Covalent Organic Frameworks for Carbon Capture
We screen a database of more than 69 000 hypothetical covalent organic frameworks (COFs) for carbon capture using parasitic energy as a metric. To compute CO 2 –framework interactions in molecular simulations, we develop a genetic algorithm to tune the charge equilibration method and derive accurate framework partial charges. Nearly 400 COFs are identified with parasitic energy lower than that of an amine scrubbing process using monoethanolamine; more than 70 are better performers than the best experimental COFs and several perform similarly to Mg-MOF-74. We analyze the effect of pore topology on carbon capture performance to guide the development of improved carbon capture materials.
Metal Organic Frameworks for Xenon Storage Applications
The demand for cheap and convenient xenon storage continues to rise due to its wide spectrum of applications. It is expected that solid-state adsorbents can provide significant advantages over the current isolated stainless-steel tank-based storage technologies. In this context, we investigated metal organic frameworks for use as adsorbents for xenon. Initially, three representative MOFs were synthesized and characterized in terms of Xe storage. The results were used to validate a computational modeling approach, which was later extended to a larger set of materials. The collected results allowed us to rationalize the key parameters (pore volume, surface area, void fraction etc.), which are important for good performance and selection of the best materials for xenon storage