DFT modeling of CO2 interaction with various aqueous amine structures.

In this study, the interaction of CO2 with aqueous monoethanolamine (MEA), 2-amino-2-methyl-1-propanol (AMP), ethylenediamine (EDA), guanidine, and tetramethylguanidine (TMG) was studied by using density functional theory (DFT) modeling. MEA was chosen as the benchmark system, and the reaction pathways of carbamate as well as bicarbonate formations were analyzed thoroughly in order to evaluate the performances of the amines involved in this study in terms of thermodynamics and reaction kinetics. Among the materials considered, AMP was shown to be the most promising one due to its decreased product stability.

Broken symmetry of an adsorbed molecular switch determined by scanning tunneling spectroscopy.

An asymmetric turn: Scanning tunneling spectroscopy has been used to analyze the structure of tris[4-(phenylazo)phenyl)]amine on a Au(111) surface. A degenerate marker state serves as a sensitive probe for the structure of the adsorbed molecules.

Structure and activity relationship for CO and O2 adsorption over gold nanoparticles using density functional theory and artificial neural networks.

In this work, the structure and activity relationship for CO and O(2) adsorption over Au(2) to Au(10) clusters was investigated using density functional theory (DFT) and artificial neural networks as a part of ongoing studies in the literature to understand CO oxidation over gold nanoparticles. The optimum structures for the anionic, neutral, and cationic clusters were determined first using DFT. The structural properties such as binding energy, highest occupied molecular orbital-lowest unoccupied molecular orbital gap, ionization potential, and electron affinity as well as the adsorption energies of CO and O(2) were calculated using the same method at various values of user defined descriptors such as the size and charge of the cluster, the presence or absence of unpaired electron, and the coordination number of the adsorption site. Then, artificial neural network models were constructed to establish the relationship between these descriptors and the structural properties, as well as between the structural properties and the adsorption energies. It was concluded that the neural network models can successfully predict the adsorption energies calculated using DFT. The statistically determined relative significances of user defined descriptors and the structural properties on the adsorption energies were also found to be in good agreement with the literature indicating that this approach may be used for the other catalytic systems as well.