Modeling Adsorption of CO2 in Rutile Metallic Oxide Surfaces: Implications in CO2 Catalysis.

CO2 is the most abundant greenhouse gas, and for this reason, it is the main target for finding solutions to climatic change. A strategy of environmental remediation is the transformation of CO2 to an aggregated value product to generate a carbon-neutral cycle. CO2 reduction is a great challenge because of the large C=O dissociation energy, ~179 kcal/mol. Heterogeneous photocatalysis is a strategy to address this issue, where the adsorption process is the fundamental step. The focus of this work is the role of adsorption in CO2 reduction by means of modeling the CO2 adsorption in rutile metallic oxides (TiO2, GeO2, SnO2, IrO2 and PbO2) using Density Functional Theory (DFT) and periodic DFT methods. The comparison of adsorption on different metal oxides forming the same type of crystal structure allowed us to observe the influence of the metal in the adsorption process. In the same way, we performed a comparison of the adsorption capability between two different surface planes, (001) and (110). Two CO2 configurations were observed, linear and folded: the folded conformations were observed in TiO2, GeO2 and SnO2, while the linear conformations were present in IrO2 and PbO2. The largest adsorption efficiency was displayed by the (001) surface planes. The CO2 linear and folded configurations were related to the interaction of the oxygen on the metallic surface with the adsorbate carbon, and the linear conformations were associated with the physisorption and folded configurations with chemisorption. TiO2 was the material with the best performance for CO2 interactions during the adsorption.

Grammatical evolution-based design of SARS-CoV-2 main protease inhibitors.

A series of SARS-CoV-2 main protease (SARS-CoV-2-Mpro) inhibitors were modeled using evolutive grammar algorithms. We have generated an automated program that finds the best candidate to inhibit the main protease, Mpro, of SARS-CoV-2. The candidates were constructed based on a pharmacophore model of the above-mentioned target; relevant moieties of such molecules were modified using data-basis sets with similar chemical behavior to the reference moieties. Additionally, we used the SMILES language to translate 3D chemical structures to 1D words; then, an evolutive grammar algorithm was used to explore the chemical space and obtain new candidates, which were evaluated via the binding energy of molecular coupling assays as an evaluation function. Finally, sixteen molecules were obtained in 3 runs of our program, three of which show promising binding properties as SARS-CoV-2-Mpro inhibitors. One of them, TTO, maintained its relevant binding properties during 100 ns molecular dynamics experiments. For this reason, TTO is the best candidate to inhibit SARS-CoV-2-Mpro. The software we developed for this contribution is available at the following URL: https://github.com/masotelof/GEMolecularDesign.

Modeling Adsorption and Optical Properties for the Design of CO2 Photocatalytic Metal-Organic Frameworks.

Four Metal-Organic Frameworks (MOFs) were modeled (IRMOF-C-BF2, IRMOF-C-(2)-BF2, IRMOF-C'-BF2, and IRMOF-C-CH2BF2) based on IRMOF-1. A series of linkers, based on Frustrated Lewis Pairs and coumarin moieties, were attached to IRMOF-1 to obtain MOFs with photocatalytic properties. Four different linkers were used: (a) a BF2 attached to a coumarin moiety at position 3, (b) two BF2 attached to a coumarin moiety in positions 3 and 7, (c) a BF2 attached in the coumarin moiety at position 7, and (d) a CH2BF2 attached at position 3. An analysis of the adsorption properties of H2, CO2, H2O and possible CO2 photocatalytic capabilities was performed by means of computational modeling using Density Functional Theory (DFT), Time-Dependent Density Functional (TD-DFT) methods, and periodic quantum chemical wave function approach. The results show that the proposed linkers are good enough to improve the CO2 adsorption, to hold better bulk properties, and obtain satisfactory optical properties in comparison with IRMOF-1 by itself.