Catalytic oxidation of carbon monoxide over radiolytically prepared Pt nanoparticles supported on glass

Platinum nanoparticles have been prepared by radiolytic and chemical methods in the presence of stabilizer gelatin and SiO2 nanoparticles. The formation of Pt nanoparticles was confirmed using UV-vis absorption spectroscopy and transmission electron microscopy (TEM). The prepared particles were coated on the inner walls of the tubular pyrex reactor and tested for their catalytic activity for oxidation of CO. It was observed that Pt nanoparticles prepared in the presence of a stabilizer (gelatin) showed a higher tendency to adhere to the inner walls of the pyrex reactor as compared to that prepared in the presence of silica nanoparticles. The catalyst was found to be active at ≥ 150 ° C giving CO2. Chemically reduced Pt nanoparticles stabilized on silica nanoparticles gave ~7% CO conversion per hour. However, radiolytically prepared Pt nanoparticles stabilized by gelatin gave ~10% conversion per hour. Catalytic activity of radiolytically prepared platinum catalyst, coated on the inner walls of the reactor, was evaluated as a function of CO concentration and reaction temperature. The rate of reaction increased with increase in reaction temperature and the activation energy for the reaction was found to be ~108.8 kJ mol-1. The rate of CO2 formation was almost constant (~1.5 × 10-4 mol dm-3 h-1) at constant O2 concentration (6.5 × 10-3 mol dm-3) with increase in CO concentration from 2 × 10-4 mol dm-3 to 3.25 × 10-3 mol dm-3. The data indicate that catalytic oxidation of CO takes place by Eley-Rideal mechanism

Higher Order Apriori

Frequent itemset mining (FIM) is a well known technique for discovering relationships between items. Most FIM algorithms are based on first-order associations between items in the same record. Although a few algorithms capable of discovering indirect propositional rules exist, they do not extend beyond secondorder. In addition, although multi-relational ARM discovers higher-order rules, the rules are non-propositional and the algorithm is NP-complete. This article introduces Higher Order Apriori, a novel algorithm for mining higher-order rules. We extend the itemset definition to incorporate k-itemsets up to n th-order, and present our levelwise order-first algorithm: levelwise meaning that the size of k-itemsets increases in each iteration (as with Apriori), and order-first meaning that at each level, itemsets are generated across all orders. Support is calculated based on the order of itemsets and the number of higher-order associations connecting items