Ammonia Decomposition on Ir(100): From Ultrahigh Vacuum to Elevated Pressures

Ammonia decomposition on Ir(100) has been studied over the pressure range from ultrahigh vacuum to 1.5 Torr and at temperatures ranging from 200 to 800 K. The kinetics of the ammonia decomposition reaction was monitored by total pressure change. The apparent activation energy obtained in this study (84 kJ/mol) is in excellent agreement with our previous studies using supported Ir catalysts (Ir/Al₂O₃ 82 kJ/mol). Partial pressure dependence studies of the reaction rate yielded a positive order (0.9±0.1) with respect to ammonia and negative order (–0.7 ±0.1) with respect to hydrogen. Temperature-programmed desorption data from clean and hydrogen co-adsorbed Ir(100) surfaces indicate that ammonia undergoes facile decomposition on both these surfaces. Recombinative desorption of N₂ is the rate-determining step with a desorption activation energy of 63 kJ/mol. Co-adsorption data also indicate that the observed negative order with respect to hydrogen pressure is due to enhancement of the reverse reaction (NH _x + H NH _x+1, x=0–2) in the presence of excess H atoms on the surface.     

Acetylene Hydrogenation on Au-Based Catalysts

Hydrogenation of acetylene has been investigated on Au/TiO₂, Pd/TiO₂ and Au-Pd/TiO₂ catalysts at high acetylene conversion levels. The Au/TiO₂ catalyst (avg. particle size: 4.6 nm) synthesized by the temperature-programmed reduction-oxidation of an Au-phosphine complex on TiO₂ showed a remarkably high selectivity to ethylene formation even at 100% acetylene conversion. Au/TiO₂ prepared by the conventional incipient wet impregnation method (avg. particle size: 30 nm), on the other hand, showed negligible activity for acetylene hydrogenation. Although the Au catalysts showed a high selectivity for ethylene, the acetylene conversion activity and catalyst stability were inferior to the Pd-based catalysts. Au-Pd catalysts prepared by the redox method showed high acetylene conversions as well as high selectivity for ethylene. Interestingly Au-Pd catalysts prepared by depositing Pd via the incipient wetness method on Au/TiO₂ showed very poor selectivity (comparable to mono-metallic Pd catalysts) for ethylene. High-resolution transmission electron microscopy (TEM) studies coupled with energy dispersive X-ray spectroscopy (EDS) showed that while the redox method produced bimetallic Au-Pd catalysts, the latter method produced individual Pd and Au particles on the support.     

Biosynthesis and PBAN-Regulated Transport of Pheromone Polyenes in the Winter Moth, Operophtera brumata

The trienoic and tetraenoic polyenes, (3Z,6Z,9Z)-3,6,9-nonadecatriene, (3Z,6Z,9Z)-3,6,9-henicosatriene, and (3Z,6Z,9Z)-1,3,6,9-henicosatetraene were found in the abdominal cuticle and pheromone gland of the winter moth Operophtera brumata L. (Lepidoptera: Geometridae), in addition to the previously identified single component sex pheromone (3Z,6Z,9Z)-1,3,6,9-nonadecatetraene. The pheromone biosynthesis activating neuropeptide (PBAN) is involved in the regulation of polyene transport from abdominal cuticle to the pheromone gland. In vivo deuterium labeling experiments showed that (11Z,14Z,17Z)-11,14,17-icosatrienoic acid, the malonate elongation product of linolenic acid, (9Z,12Z,15Z)-9,12,15-octadecatrienoic acid, is used to produce (3Z,6Z,9Z)-3,6,9-nonadecatriene and (3Z,6Z,9Z)-1,3,6,9-nonadecatetraene.     

The Formation of PdCx over Pd-Based Catalysts in Vapor-Phase Vinyl Acetate Synthesis: Does a Pd–Au Alloy Catalyst Resist Carbide Formation?

The formation of Pd carbide (PdC _x ) during the synthesis of vinyl acetate (VA) was investigated over Pd/SiO₂ catalysts with two different Pd particle sizes, as well as over a Pd–Au/SiO₂ mixed-metal catalyst. XRD data show that PdC _x was produced in the pure Pd catalysts after reaction based on the downshift of the Pd(111) and (200) XRD features. The smaller Pd particles showed greater resistance to the formation of PdC _x . The XRD and XPS data are consistent with formation of a PdC _x species at the surface of the Pd–Au catalyst, however, the primary contributor to the downshift of the Pd(111) feature subsequent to reaction in the mixed-metal catalyst is believed to arise from reaction-induced alloying of Au with Pd. The alloying of Au with Pd is apparently very effective in preventing PdC _x formation in Pd-based catalysts for VA synthesis.     

Integration of machine learning and first principles models

Model building and parameter estimation are traditional concepts widely used in chemical, biological, metallurgical, and manufacturing industries. Early modeling methodologies focused on mathematically capturing the process knowledge and domain expertise of the modeler. The models thus developed are termed first principles models (or white-box models). Over time, computational power became cheaper, and massive amounts of data became available for modeling. This led to the development of cutting edge machine learning models (black-box models) and artificial intelligence (AI) techniques. Hybrid models (gray-box models) are a combination of first principles and machine learning models. The development of hybrid models has captured the attention of researchers as this combines the best of both modeling paradigms. Recent attention to this field stems from the interest in explainable AI (XAI), a critical requirement as AI systems become more pervasive. This work aims at identifying and categorizing various hybrid models available in the literature that integrate machine-learning models with different forms of domain knowledge. Benefits such as enhanced predictive power, extrapolation capabilities, and other advantages of combining the two approaches are summarized. The goal of this article is to consolidate the published corpus in the area of hybrid modeling and develop a comprehensive framework to understand the various techniques presented. This framework can further be used as the foundation to explore rational associations between several models.     

Catalytic ammonia decomposition: COx-free hydrogen production for fuel cell applications

Catalytic decomposition of ammonia has been investigated as a method to produce hydrogen for fuel cell applications. The absence of any undesirable by-products (unlike, e.g., CO_x, formed during reforming of hydrocarbons and alcohols) makes this process an ideal source of hydrogen for fuel cells. In this study a variety of supported metal catalysts have been studied. Supported Ru catalysts were found to be the most active, whereas supported Ni catalysts were the least active. The supports were found to play a profound role in the ammonia decomposition process. The activation energies for the ammonia decomposition process varied from 17 to 22 kcal/mol depending upon the catalyst employed. The activation energies of the supported Ir catalysts were found to be in excellent agreement with our recent studies addressing ammonia decomposition on single crystal Ir.     

Adaptive optimal control without weight transport

Many neural control systems are at least roughly optimized, but how is optimal control learned? There are algorithms for this purpose, but in their current forms, they are not suited for biological neural networks because they rely on a type of communication that is not available in the brain, namely, weight transport-transmitting the strengths, or "weights," of individual synapses to other synapses and neurons. Here we show how optimal control can be learned without weight transport. Our method involves a set of simple mechanisms that can compensate for the absence of weight transport in the brain and so may be useful for neural computation generally.     

CO oxidation over Au/TiO2 prepared from metal-organic gold complexes

A series of Au/TiO₂ catalysts has been prepared from precursors of various metal-organic gold complexes (Au _n , n = 2–4) and their catalytic activity for CO oxidation studied. The Au/TiO₂ catalyst synthesized from a tetranuclear gold complex shows the best performance for CO oxidation with transmission electron microscopy of this catalyst indicating an average gold particle size of 3.1 nm.