Affinity descriptor of metal catalysts: concept, measurement and application of oxygen affinity in the catalytic transformation of oxygenates

Multiple oxygenate groups in biomass-based feedstocks are open to multiple catalytic pathways and products, typically resulting in low selectivity for the desired products. In this context, strategies for rational catalyst design are critical to obtain high selectivity for the desired products in biomass upgrading. The Sabatier principle provides a conceptual framework for designing optimal catalysts by following the volcanic relationship between catalyst activity for a reaction and the binding strength of a substrate on a catalyst. The affinity descriptor of catalysts, which scales the interaction strength of the functional groups of substrates with catalysts, can potentially be developed to correlate with catalyst performance in reactions. Specifically, the oxygen affinity of catalysts, as a measure of the interaction strength between oxygenates and catalysts, can be applied to rationalize the oxygenate transformation and guide the rational design of efficient catalysts based on the Sabatier principle. This review highlights the fundamental basis and applications of affinity descriptors in catalysis, with a focus on the oxygen affinity of catalysts for the transformation of biomass-derived oxygenate compounds.

Sodium Silicate Catalyst for Synthesis Monoacylglycerol and Diacylglycerol-Rich Structured Lipids: Product Characteristic and Glycerolysis–Interesterification Kinetics

Sodium silicate as heterogeneous base catalysts is more environmentally friendly and easily separated by filtration. The objective of this research was to evaluate the activated sodium silicate as catalyst for synthesis of monoacylglycerol (MAG) and diacylglycerol (DAG)-rich structured lipids (SLs) from a palm olein-stearin blend. Sodium silicate was activated and functional group was characterized. Reaction was performed using 5% catalyst (w/w) at various reaction temperature (70–120 °C) for 3 h in a batch stirred tank reactor. Physical properties of SLs, such as melting point, slip melting point, and hardness of SLs were determined. Reaction kinetics were also evaluated. The results show that Si−O bending was reduced and shifted to a Si−O−Na and Si−O−Si functional groups after sodium silicate activation. Temperature had a significant effect on SLs composition at higher than 90 °C. An increase in temperature produced more MAG, resulting in better product physical properties. The best reaction condition was at 110 °C. Rate constants and the Arrhenius equation were also obtained for each reaction step. In summary, the activated sodium silicate catalyzed glycerolysis-interesterification reaction, which produced MAG and DAG at temperature higher than 90 °C. Therefore, the physical properties of SLs were improved. Copyright © 2022 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0). 

Extracting kinetic information in catalysis: an automated tool for the exploration of small data

Kinetically relevant information for heterogeneously catalysed reactions is automatically extracted from small datasets by means of a newly-developed machine learning chemically-enriched tool.

A quantitative multiscale perspective on primary olefin formation from methanol

The formation of the first C-C bond and primary olefins from methanol over zeolite and zeotype catalysts has been studied for over 40 years. Over 20 mechanisms have been proposed for the formation of the first C-C bond. In this quantitative multiscale perspective, we decouple the adsorption, desorption, mobility, and surface reactions of early species through a combination of vacuum and sub-vacuum studies using temporal analysis of products (TAP) reactor systems, and through studies with atmospheric fixed bed reactors. These results are supplemented with density functional theory calculations and data-driven physical models, using partial differential equations, that describe the temporal and spatial evolution of species. We consider the effects of steam, early degradation species, and product masking due to the inherent autocatalytic nature of the process, which all complicate the observation of the primary olefin(s). Although quantitative spectroscopic determination of the lifetimes, surface mobility, and reactivity of adspecies is still lacking in the literature, we observe that reaction barriers are competitive with adsorption enthalpies and/or activation energies of desorption, while facile diffusion occurs in the porous structures of the zeolite/zeotype catalysts. Understanding the various processes allows for quantitative evaluation of their competing energetics, which leads to molecular insights as to what governs the catalytic activity during the conversion of methanol to primary olefins over zeolite/zeotype catalysts.

First-Principles-Based Simulation of an Industrial Ethanol Dehydration Reactor

The achievement of new economically viable chemical processes often involves the translation of observed lab-scale phenomena into performance in an industrial reactor. In this work, the in silico design and optimization of an industrial ethanol dehydration reactor were performed, employing a multiscale model ranging from nano-, over micro-, to macroscale. The intrinsic kinetics of the elementary steps was quantified through ab initio obtained rate and equilibrium coefficients. Heat and mass transfer limitations for the industrial design case were assessed via literature correlations. The industrial reactor model developed indicated that it is not beneficial to utilize feeds with high ethanol content, as they result in lower ethanol conversion and ethene yield. Furthermore, a more pronounced temperature drop over the reactor was simulated. It is preferred to use a more H2O-diluted feed for the operation of an industrial ethanol dehydration reactor.

Towards operando computational modeling in heterogeneous catalysis

An increased synergy between experimental and theoretical investigations in heterogeneous catalysis has become apparent during the last decade. Experimental work has extended from ultra-high vacuum and low temperature towards operando conditions. These developments have motivated the computational community to move from standard descriptive computational models, based on inspection of the potential energy surface at 0 K and low reactant concentrations (0 K/UHV model), to more realistic conditions. The transition from 0 K/UHV to operando models has been backed by significant developments in computer hardware and software over the past few decades. New methodological developments, designed to overcome part of the gap between 0 K/UHV and operando conditions, include (i) global optimization techniques, (ii) ab initio constrained thermodynamics, (iii) biased molecular dynamics, (iv) microkinetic models of reaction networks and (v) machine learning approaches. The importance of the transition is highlighted by discussing how the molecular level picture of catalytic sites and the associated reaction mechanisms changes when the chemical environment, pressure and temperature effects are correctly accounted for in molecular simulations. It is the purpose of this review to discuss each method on an equal footing, and to draw connections between methods, particularly where they may be applied in combination.