CO-PrOx over nano-Au/TiO2: Monolithic catalyst performance and empirical kinetic model fitting

In this work, the performance of ceramic monoliths washcoated with Au/TiO2 is studied on CO preferential oxidation (CO-PrOx) reaction in H2-rich environments under a wide range of operating conditions of practical interest. The parameter estimation of a nonlinear kinetic empirical model representing this system is made via genetic algorithms by fitting the model predictions against our laboratory observations. Parameter uncertainty leading to inaccurate predictions is often present when kinetic models with nonlinear rate equations are considered. Here, after the fitting was concluded, a statistical study was conducted to determine the accuracy of the parameter estimation. Activation energies of ca. 30 kJ/mol and 55 kJ/mol were adjusted for CO and H2 oxidations, respectively. The catalyst showed appropriate activity and selectivity values on the CO oxidation on a H2-rich environment. After ca. 45 h on stream the catalyst showed no deactivation. Results show that the model is suitable for reproducing the behavior of the CO-PrOx reactions and it can be used in the design of reactors for hydrogen purification.Peer ReviewedPostprint (author's final draft

First-principles molecular structure search with a genetic algorithm

The identification of low-energy conformers for a given molecule is a fundamental problem in computational chemistry and cheminformatics. We assess here a conformer search that employs a genetic algorithm for sampling the low-energy segment of the conformation space of molecules. The algorithm is designed to work with first-principles methods, facilitated by the incorporation of local optimization and blacklisting conformers to prevent repeated evaluations of very similar solutions. The aim of the search is not only to find the global minimum, but to predict all conformers within an energy window above the global minimum. The performance of the search strategy is: (i) evaluated for a reference data set extracted from a database with amino acid dipeptide conformers obtained by an extensive combined force field and first-principles search and (ii) compared to the performance of a systematic search and a random conformer generator for the example of a drug-like ligand with 43 atoms, 8 rotatable bonds and 1 cis/trans bond

Optimal design of nanoplasmonic materials using genetic algorithms as a multi-parameter optimization tool

An optimal control approach based on multiple parameter genetic algorithms is applied to the design of plasmonic nanoconstructs with pre-determined optical properties and functionalities. We first develop nanoscale metallic lenses that focus an incident plane wave onto a pre-specified, spatially confined spot. Our results illustrate the role of symmetry breaking and unravel the principles that favor dimeric constructs for optimal light localization. Next we design a periodic array of silver particles to modify the polarization of an incident, linearly-polarized plane wave in a desired fashion while localizing the light in space. The results provide insight into the structural features that determine the birefringence properties of metal nanoparticles and their arrays. Of the variety of potential applications that may be envisioned, we note the design of nanoscale light sources with controllable coherence and polarization properties that could serve for coherent control of molecular or electronic dynamics in the nanoscale.Comment: 13 pages, 6 figures. submitted to J. Chem. Phy

Artificial intelligence in nanotechnology

This is the author’s version of a work that was accepted for publication in Nanotechnology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Nanotechnology 24.45 (2013): 452002During the last decade there has been an increasing use of artificial intelligence tools in nanotechnology research. In this paper we review some of these efforts in the context of interpreting scanning probe microscopy, the study of biological nanosystems, the classification of material properties at the nanoscale, theoretical approaches and simulations in nanoscience, and generally in the design of nanodevices. Current trends and future perspectives in the development of nanocomputing hardware that can boost artificial intelligence based applications are also discussed. Convergence between artificial intelligence and nanotechnology can shape the path for many technological developments in the field of information sciences that will rely on new computer architectures and data representations, hybrid technologies that use biological entities and nanotechnological devices, bioengineering, neuroscience and a large variety of related disciplines