MCM-41-nPrNH2 as a Recoverable Nanocatalyst for the Synthesis of New Phenylpyrido[4,3-d]pyrimidin-2-amine Derivatives

MCM-41 anchored n-propylamine (MCM-41-nPrNH2) was found to be a highly efficient and recoverable nanocatalyst for the synthesis of new class of phenylpyrido[4,3-d]pyrimidin-2-amine derivatives under solvent free conditions in high to quantitative yields. All the structures of title compounds 3a-j were elucidated by comprehensive 1H NMR, 13C NMR, IR and Mass spectra When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3500

Hollow alumina nanospheres as novel catalyst for the conversion of methanol to dimethyl ether

This paper investigates hollow and porous alumina nanospheres that were previously synthesized to be used for the dehydration of methanol to dimethyl ether (DME). As hollow nanostructures possess characteristics such as low density and high surface to volume ratio, their catalytic activity between hollow and porous structure is compared. For this purpose, three most important parameters (acidity, temperature and weight hourly space velocity (WHSV)) affecting the performance of these catalysts were investigated. The catalysts were characterized by scanning electron microscopy (SEM), BET, X-ray diffraction (XRD), and the temperature programmed desorption of ammonia (NH3-TPD) techniques. Results show that the optimum operating condition for hollow alumina nanosphere can be achieved at temperature of 275 ºC and WHSV of 20 h-1 compared with operating condition for porous alumina at temperature of 325 ºC and WHSV of 20 h-1

MLSys: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically different development and deployment profile of modern ML methods, and the range of practical concerns that come with broader adoption. We propose to foster a new systems machine learning research community at the intersection of the traditional systems and ML communities, focused on topics such as hardware systems for ML, software systems for ML, and ML optimized for metrics beyond predictive accuracy. To do this, we describe a new conference, MLSys, that explicitly targets research at the intersection of systems and machine learning with a program committee split evenly between experts in systems and ML, and an explicit focus on topics at the intersection of the two