Probing the morphological effects of ReOx/CeO2 catalysts on CO2 hydrogenation reaction

The performance optimization of ceria (CeO2)-supported catalysts in heterogeneous reactions can be enabled by the control of crystal morphology. However, the insights into how CeO2-morphology affects the CO2 hydrogenation performance...

Template-free fabrication of magnetic mesoporous poly(ionic liquid)s: efficient interfacial catalysts for hydrogenation reaction and transesterification of soybean oil

Green sustainable production is very important for the development of human society and environment. In this study, Pickering interfacial catalysts (Pd-p(xTEMPA-yFDABCO-zDVB)@Fe3O4) with CO2 and magnetic double response were prepared by...

Intensifying strategy of ionic liquid for Pd-based catalysts in anthraquinone hydrogenation

A novel strategy was first employed to prepare the Pd-IL complex catalysts for the anthraquinone hydrogenation reaction, taking advantage of the ionic liquid (IL). The IL microphase provides an excellent...

Prediction of Optimal Conditions of Hydrogenation Reaction Using the Likelihood Ranking Approach

The selection of experimental conditions leading to a reasonable yield is an important and essential element for the automated development of a synthesis plan and the subsequent synthesis of the target compound. The classical QSPR approach, requiring one-to-one correspondence between chemical structure and a target property, can be used for optimal reaction conditions prediction only on a limited scale when only one condition component (e.g., catalyst or solvent) is considered. However, a particular reaction can proceed under several different conditions. In this paper, we describe the Likelihood Ranking Model representing an artificial neural network that outputs a list of different conditions ranked according to their suitability to a given chemical transformation. Benchmarking calculations demonstrated that our model outperformed some popular approaches to the theoretical assessment of reaction conditions, such as k Nearest Neighbors, and a recurrent artificial neural network performance prediction of condition components (reagents, solvents, catalysts, and temperature). The ability of the Likelihood Ranking model trained on a hydrogenation reactions dataset, (~42,000 reactions) from Reaxys® database, to propose conditions that led to the desired product was validated experimentally on a set of three reactions with rich selectivity issues.

Highly Enhanced Catalytic Stability of Copper by the Synergistic Effect of Porous Hierarchy and Alloying for Selective Hydrogenation Reaction

Supported copper has a great potential for replacing the commercial palladium-based catalysts in the field of selective alkynes/alkadienes hydrogenation due to its excellent alkene selectivity and relatively high activity. However, fatally, it has a low catalytic stability owing to the rapid oligomerization of alkenes on the copper surface. In this study, 2.5 wt% Cu catalysts with various Cu:Zn ratios and supported on hierarchically porous alumina (HA) were designed and synthesized by deposition–precipitation with urea. Macropores (with diameters of 1 μm) and mesopores (with diameters of 3.5 nm) were introduced by the hydrolysis of metal alkoxides. After in situ activation at 350 °C, the catalytic stability of Cu was highly enhanced, with a limited effect on the catalytic activity and alkene selectivity. The time needed for losing 10% butadiene conversion for Cu1Zn3/HA was ~40 h, which is 20 times higher than that found for Cu/HA (~2 h), and 160 times higher than that found for Cu/bulky alumina (0.25 h). It was found that this type of enhancement in catalytic stability was mainly due to the rapid mass transportation in hierarchically porous structure (i.e., four times higher than that in bulky commercial alumina) and the well-dispersed copper active site modified by Zn, with identification by STEM–HAADF coupled with EDX. This study offers a universal way to optimize the catalytic stability of selective hydrogenation reactions.

PERFECTION of PREPARATION TECHNOLOGY of the CATALYST CARRIER

Сибунит – носитель для катализатора, использующегося в различных процессах химической промышленности, в том числе и в реакции гидрирования. В настоящее время для его подготовки и проведения технологических операций используется так называемый «сухой» способ, обладающий обширными недостатками, основными из которых являются пыление, а также значительная доля ручного труда на производстве и, как следствие, большая продолжительность отдельных стадий.Предлагается использовать «мокрый» способ с применением аппаратуры роторно-пульсационного типа для подготовки носителя катализатора с целью исключения указанных недостатков.В результате проведённой работы было установлено, что активность образцов катализаторов, полученных с использованием молотковой мельницы и с применением роторно-пульсационного аппарата в реакции гидрогенолиза составила 0,606 моль/мин и 0,642 моль/мин, при выходе целевого продукта 76,15% и 80,00%, соответственно. Также определено, что доля потерь, обусловленная образованием пыли, была снижена более чем в 10 раз по сравнению с действующей технологией. Sibunit is carrying agent for the catalyst used in various processes of the chemical industry, including in hydrogenation reaction. Now for its preparation and conducting of technological operations the so-called "dry" way possessing extensive deficiencies is used, basic of which the fluffing, and also a considerable share of muscle work on manufacture and, as consequence, the big duration of separate stages are.It is offered to use a "wet" way with application of rotor-stator equipment for preparation of a catalyst carrier for the purpose of exclusion of the specified deficiencies.As a result of the spent work it has been established, that activity of the catalysts samples gained with use of the hammer mill and with application rotor-stator system in hydrogenation reaction has made 0,606 gramme-molecules/mines and 0,642 gramme-molecules/mines, at an yield of a target product of 76,15% and 80,00%, accordingly. Also it is defined, that the share of losses caused by a dust generation, it has been lowered more than in 10 times in comparison with acting technology.

Promotive Selectivity to C2-C3 Polyols From Alkaline Cellulose Hydrogenated by Ionic Liquid-stablized Ru Nanoparticles

Alkaline cellulose hydrogenolysis on metal catalyst was an effective way to get C2~C3 polyols. The alkaline cellulose was obtained by treating cellulose with 4 wt% NaOH solution. Ionic liquid-stablized Ru nanoparticles were prepared by reducing metal salt in ionic liquid. The SEM results indicate that the amorphous part of alkaline cellulose is helpful for getting the catalyst into the cavities to have a further hydrogenation reaction. When hydrogenolysis of alkaline cellulose over Ru/[Bmim]BF4 nanoparticles was conducted at 433 K, 63.78% of the substrate was converted with glycerol, 1,2-propanediol and ethylene glycol as main products of which selectivity was up to 58.91 %, whereas the conversion rate over Ru/C catalyst of alkaline cellulose was 59.23 % and only 26.11 % C2~C3 polyols were detected. Moreover, if the ionic liquid-stablized Ru nanoparticles were doped with 53.7 % Ni, the selectivity of C2~C3 polyols was promoted to 65.07 %. These results suggested the advantages of the ionic liquid-stablized Ru nanoparticles, especially doping with Ni, have potentials for promotive selectivity to C2~C3 alcohols. Put forward the plausible mechanism finally.

Reductive Hydroformylation of Isosorbide Diallyl Ether

Isosorbide and its functionalized derivatives have numerous applications as bio-sourced building blocks. In this context, the synthesis of diols from isosorbide diallyl ether by hydrohydroxymethylation reaction is of extreme interest. This hydrohydroxymethylation, which consists of carbon-carbon double bonds converting into primary alcohol functions, can be obtained by a hydroformylation reaction followed by a hydrogenation reaction. In this study, reductive hydroformylation was achieved using isosorbide diallyl ether as a substrate in a rhodium/amine catalytic system. The highest yield in bis-primary alcohols obtained was equal to 79%.

Dynamic restructuring of supported metal nanoparticles and its implications for structure insensitive catalysis

Some fundamental concepts of catalysis are not fully explained but are of paramount importance for the development of improved catalysts. An example is the concept of structure insensitive reactions, where surface-normalized activity does not change with catalyst metal particle size. Here we explore this concept and its relation to surface reconstruction on a set of silica-supported Ni metal nanoparticles (mean particle sizes 1–6 nm) by spectroscopically discerning a structure sensitive (CO2 hydrogenation) from a structure insensitive (ethene hydrogenation) reaction. Using state-of-the-art techniques, inter alia in-situ STEM, and quick-X-ray absorption spectroscopy with sub-second time resolution, we have observed particle-size-dependent effects like restructuring which increases with increasing particle size, and faster restructuring for larger particle sizes during ethene hydrogenation while for CO2 no such restructuring effects were observed. Furthermore, a degree of restructuring is irreversible, and we also show that the rate of carbon diffusion on, and into nanoparticles increases with particle size. We finally show that these particle size-dependent effects induced by ethene hydrogenation, can make a structure sensitive reaction (CO2 hydrogenation), structure insensitive. We thus postulate that structure insensitive reactions are actually apparently structure insensitive, which changes our fundamental understanding of the empirical observation of structure insensitivity.