Direct Activation of Light Alkanes to Value-Added Chemicals Using Supported Metal Oxide Catalysts

One of the most challenging aspects of modern-day catalysis is the conversion of methane. Direct conversion of methane via dehydroaromatization (MDHA) is a well-known process which can produce valuable hydrocarbons. Mo oxide supported on ZSM-5/MCM-22 has been studied extensively in recent years for MDHA. Mo carbides are responsible for activating methane by forming CHx species. These are dimerized into C2Hy and oligomerized on ZSM-5/MCM-22 Brønsted acid sites to form aromatics. Sulfated zirconia (SZ) supported Mo catalyst contains the acid sites necessary to produce benzene in MDHA. Here, sulfated hafnia (SH), a homologous oxide like SZ, has been proposed to provide the necessary acid sites as a novel support for Mo in MDHA. Conversion increased with higher temperature and lower space velocity and gradually deactivated with time. This can be attribute to catalytic surface coking, confirmed with subsequent TPO analysis. Benzene product selectivity increased with higher Mo loading, lower temperature and lower space velocity, while gradually decreasing with time. A direct comparison of conventional Mo/HZSM-5 synthesized here and under identical reaction conditions showed lower activity compared to the Mo-SH catalyst. To address catalytic coking and improve aromatics selectivity, several extensions of this project were carried out in this work. Additional of promoters like Pt, Cr, Pd to Mo-SZ catalysts showed improved benzene selectivity and overall activity of the modified catalysts. MDHA studies using group VIB metals (Cr, Mo, W) supported on SZ were also carried out to understand the effect of these active metals on SZ, which showed the superiority of Mo in terms of catalytic activity and benzene selectivity. Direct conversion of methane to C2 hydrocarbons using W/SZ is another demonstration of the versatility of this catalytic process. Additionally, Mo/SH was used to directly activate ethane and propane and selectively produce important industrial feedstocks like ethylene and propylene. Few alternate routes were suggested for low temperature conversion of methane using CO2 via bimetallic catalytic approaches to produce high value oxygenates. Based on thermodynamic analysis, prospective catalytic reaction mechanisms are discussed to overcome the thermodynamic energy constraints for CO2 and methane activation at low temperature with selective production of target oxygenates

Probing the surface acidity of supported aluminum bromide catalysts

Solid acid catalysis is an important class of reactions. The principal advantages of solid acid catalysts as compared to their corresponding fluid acids include minimal waste and ease of product separation. One type of these catalysts is based on aluminum bromide (Al2Br6), which is a stronger Lewis acid than Al2Cl6. In this report, Al2Br6 is grafted on commercial mesoporous silica (CMS), SBA-15 and silica gel to create a solid catalyst similar to the silica-supported Al2Cl6 superacid. These supported Al2Br6 catalysts were characterized by NH3-Temperature Programmed Desorption (TPD), pyridine Diffuse Reflectance for Infrared Fourier Transform Spectroscopy (DRIFTS) and Magic Angle Spinning Nuclear Magnetic Resonance (MAS NMR). Formation of acid sites was confirmed and quantified with NH3-TPD. Both Lewis and Brønsted sites were observed with DRIFTS using pyridine as a probe molecule. In addition, thermal stability of acid sites was also studied using DRIFTS. 27Al MAS NMR analysis showed tetrahedral, pentahedral and octahedral co-ordination of Al, confirming that Al2Br6 reacted with –OH groups on silica surface. Performance of these catalysts was evaluated using acid-catalyzed 1-butene isomerization. Conversion above 80% was observed at 200 °C, corresponding to thermodynamic equilibrium

Probing the Surface Acidity of Supported Aluminum Bromide Catalysts

Solid acid catalysis is an important class of reactions. The principal advantages of solid acid catalysts as compared to their corresponding fluid acids include minimal waste and ease of product separation. One type of these catalysts is based on aluminum bromide (Al2Br6), which is a stronger Lewis acid than Al2Cl6. In this report, Al2Br6 is grafted on commercial mesoporous silica (CMS), SBA-15 and silica gel to create a solid catalyst similar to the silica-supported Al2Cl6 superacid. These supported Al2Br6 catalysts were characterized by NH3-Temperature Programmed Desorption (TPD), pyridine Diffuse Reflectance for Infrared Fourier Transform Spectroscopy (DRIFTS) and Magic Angle Spinning Nuclear Magnetic Resonance (MAS NMR). Formation of acid sites was confirmed and quantified with NH3-TPD. Both Lewis and Brønsted sites were observed with DRIFTS using pyridine as a probe molecule. In addition, thermal stability of acid sites was also studied using DRIFTS. 27Al MAS NMR analysis showed tetrahedral, pentahedral and octahedral co-ordination of Al, confirming that Al2Br6 reacted with –OH groups on silica surface. Performance of these catalysts was evaluated using acid-catalyzed 1-butene isomerization. Conversion above 80% was observed at 200 °C, corresponding to thermodynamic equilibrium

Self-supervised language grounding by active sensing combined with Internet acquired images and text

For natural and efficient verbal communication between a robot and humans, the robot should be able to learn names and appearances of new objects it encounters. In this paper we present a solution combining active sensing of images with text based and image based search on the Internet. The approach allows the robot to learn both object name and how to recognise similar objects in the future, all self-supervised without human assistance. One part of the solution is a novel iterative method to determine the object name using image classi- fication, acquisition of images from additional viewpoints, and Internet search. In this paper, the algorithmic part of the proposed solution is presented together with evaluations using manually acquired camera images, while Internet data was acquired through direct and reverse image search with Google, Bing, and Yandex. Classification with multi-classSVM and with five different features settings were evaluated. With five object classes, the best performing classifier used a combination of Pyramid of Histogram of Visual Words (PHOW) and Pyramid of Histogram of Oriented Gradient (PHOG) features, and reached a precision of 80% and a recall of 78%