M. Erdem Günay

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.

Knowledge Extraction from Catalysis of the Past: A Case of Selective CO Oxidation over Noble Metal Catalysts between 2000 and 2012

The objective of this work is to demonstrate that some valuable knowledge can be extracted from past publications by using various data mining tools so that the continuously growing experience accumulated in the literature over the years can be used in a more effective manner. Selective CO oxidation over noble metal catalysts is chosen as a case to test the validity of this approach because a considerable number of papers were published on this subject in the last decade. Thus, 249 papers published in the last 12 years have been inspected, 80 of which were used to form a database containing 5610 data points. First, the database was analyzed by using decision tree classification to determine the conditions that lead to high CO conversion. Then, the relative importance of various catalyst preparation and operational variables for CO conversion were determined by using artificial neural networks. Finally, the database was separated into smaller clusters by using a genetic algorithm‐based clustering technique, and the data in each cluster was modeled by artificial neural networks to predict the effects of individual catalyst preparation and operational conditions on the catalytic activity. All these analyses were effective in the extraction of knowledge from the literature and the deduction of some useful trends, rules, and correlations, which are otherwise not easily comprehensible.

Analysis of O2 Adsorption Stability and Strength Over Gold Clusters Using DFT and Logistic Regression

In this work, the stability and strength of O2 adsorption over Au2–10 clusters were studied. The density functional theory (DFT) computed adsorption data were classified using multiple logistic regression. The effects of user defined descriptors were analyzed and it was found that stable O2 adsorption requires the presence of an unpaired electron while its strength depends on the size and the charge of the cluster. As the size of the cluster increases, the probability of strong adsorption decreases, and the odds of finding strong adsorption is higher for the anionic clusters compared to neutral and cationic clusters. The effects of the electronic properties were also studied and HOMO–LUMO gap was found to be the most significant property determining the stability of O2 adsorption; as its value increases, the probability of stable adsorption decreases. The strength of the adsorption, on the other hand, was found to be mostly dependent on the ionization potential, which has a negative effect.

An Overview of Energy Technologies for a Sustainable Future

Population and the economic growth are highly correlated with the energy demand. The world population was multiplied by a factor of 1.59 (reaching above 7 billion) from 1980 to 2013, while the total energy consumption of the world was multiplied by 1.84 (getting beyond 155,000 TWh) in the same time interval. Furthermore, the demand for energy is expected to increase even more with an average annual rate of 1.2 % in the near future. However, for the last 30 years, about 85–90 % of the energy demand is supplied by petroleum, natural gas, and coal, even though they are harmful for the environment and estimated to be depleted soon. Hence, building energy policies to satisfy the needs of increasing population and growing economy in a sustainable, reliable, and secure fashion has become quite important. This may involve optimizing the energy supplies, minimizing the environmental costs, promoting the utilization of clean and renewable energy resources and diversifying the type of energy sources. Thus, not only the conventional energy generation technologies must be developed more, but also environmentally friendly alternative energy sources (such as wind, solar, geothermal, hydro, and bio) must become more widespread to sustain the energy needs for the future. However, this requires a significant amount of research on energy technologies and an effective management of the energy sources.