Practical Microkinetic Modeling Approach for Methanol Synthesis from Syngas over a Cu-Based Catalyst

Composite interfaces of g-C3N4 fragments loaded on a Cu substrate for CO2 reduction

Designing an electrocatalyst with high efficiency and product selectivity is always crucial for an electrocatalytic CO2 reduction reaction (CO2RR). Inspired by the great progress of two-dimensional (2D) nanomaterials growing on Cu surfaces and their promising CO2RR catalytic efficiencies at their interfaces, the unique performance of Cu-based 2D materials as high-efficiency and low-cost CO2RR electrocatalysts has attracted extensive attention. Herein, based on density functional theory (DFT) calculations, we proposed a composite structure of graphitic carbon nitride (g-C3N4) fragments loaded on a Cu surface to explore the CO2RR catalytic property of the interface between g-C3N4 and the Cu surface. Three composite interfaces of C3N4/Cu(111), C3N4/Cu(110) and C3N4/Cu(100) have been studied by considering the reaction sites of vertex nitrogen atoms, edge nitrogen atoms and the nearby Cu atoms. It was found that the C3N4/Cu interfaces where nitrogen atoms contact the Cu substrate present competitive CO2RR activity. Among them, C3N4/Cu(111)-N3 exhibited a better activity for CH3OH production, with a low overpotential of 0.38 V. For HCOOH and CH4 production, C3N4/Cu(111)-Cu and C3N4/Cu(100)-N1 have overpotentials of 0.26 V and 0.44 V. The electronic analysis indicates the electron transfer from the Cu substrate to the g-C3N4 fragment and mainly accumulates on the nitrogen atoms of the interface. Such charge accumulation can activate the adsorbed CO bond of CO2 and lead to lower energetic barriers of CO2RR. DFT calculations indicate that the boundary nitrogen sites reduced the energy barrier of *CHO, which is crucial for CO2RR, compared with that of the pristine Cu surface. Our study explores a new Cu-based electrocatalyst and indicates that the C3N4/Cu interface can enhance the activities and selectivity of CO2RR and open a new strategy to design high-efficiency electrocatalysts for CO2RR.

Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling

Modern heterogeneous catalysis has benefitted immensely from computational predictions of catalyst structure and its evolution under reaction conditions, first-principles mechanistic investigations, and detailed kinetic modeling, which are rungs on a multiscale workflow. Establishing connections across these rungs and integration with experiments have been challenging. Here, operando catalyst structure prediction techniques using density functional theory simulations and ab initio thermodynamics calculations, molecular dynamics, and machine learning techniques are presented. Surface structure characterization by computational spectroscopic and machine learning techniques is then discussed. Hierarchical approaches in kinetic parameter estimation involving semi-empirical, data-driven, and first-principles calculations and detailed kinetic modeling via mean-field microkinetic modeling and kinetic Monte Carlo simulations are discussed along with methods and the need for uncertainty quantification. With these as the background, this article proposes a bottom-up hierarchical and closed loop modeling framework incorporating consistency checks and iterative refinements at each level and across levels.

Overreact, an in silico lab: Automative quantum chemical microkinetic simulations for complex chemical reactions

Today's demand for precisely predicting chemical reactions from first principles requires research to go beyond Gibbs' free energy diagrams and consider other effects such as concentrations and quantum tunneling. The present work introduces overreact, a novel Python package for propagating chemical reactions over time using data from computational chemistry only. The overreact code infers all differential equations and parameters from a simple input that consists of a set of chemical equations and quantum chemistry package outputs for each chemical species. We evaluate some applications from the literature: gas-phase eclipsed-staggered isomerization of ethane, gas-phase umbrella inversion of ammonia, gas-phase degradation of methane by chlorine radical, and three solvation-phase reactions. Furthermore, we comment on a simple solvation-phase acid-base equilibrium. We show how it is possible to achieve reaction profiles and information matching experiments.

A Dual-Bed Strategy for Direct Conversion of Syngas to Light Paraffins

The authors studied the direct conversion of syngas to light paraffins in a dual-bed fixed-bed reactor. A dual-bed catalyst composed of three catalysts, a physically mixed methanol synthesis catalyst (CZA), and a methanol dehydration to dimethyl ether (DME) catalyst (Al2O3(C)) were put in the upper bed for direct conversion of syngas to DME, while the SAPO-34 (SP34-C) zeolite was put in the lower bed for methanol and DME conversion. The effects of the mass ratio of CZA to Al2O3(C), the H2/CO molar ratio, and the space velocity on catalytic performance of syngas to DME were studied in the upper bed. Moreover, a feed gas with a CO/CO2/DME/N2/H2 molar ratio of 9/6/4/5 balanced with H2 was simulated and studied in the lower bed over SP34-C; after optimizing the reaction conditions, the selectivity of light paraffins reached 90.8%, and the selectivity of propane was as high as 76.7%. For the direct conversion of syngas to light paraffins in a dual bed, 88.9% light paraffins selectivity in hydrocarbons was obtained at a CO conversion of 33.4%. This dual-bed strategy offers a potential route for the direct conversion of syngas to valuable chemicals.

Micro-kinetic Description of Electrocatalytic Reactions: The Role of Self-organized Phenomena

In this perspective we proposed a workflow for the construction of micro-kinetic models that consists of at least four stages, starting with information gathering that allows proposing possible reaction mechanisms....

Microkinetic Modeling: A Tool for Rational Catalyst Design

The design of heterogeneous catalysts relies on understanding the fundamental surface kinetics that controls catalyst performance, and microkinetic modeling is a tool that can help the researcher in streamlining the process of catalyst design. Microkinetic modeling is used to identify critical reaction intermediates and rate-determining elementary reactions, thereby providing vital information for designing an improved catalyst. In this review, we summarize general procedures for developing microkinetic models using reaction kinetics parameters obtained from experimental data, theoretical correlations, and quantum chemical calculations. We examine the methods required to ensure the thermodynamic consistency of the microkinetic model. We describe procedures required for parameter adjustments to account for the heterogeneity of the catalyst and the inherent errors in parameter estimation. We discuss the analysis of microkinetic models to determine the rate-determining reactions using the degree of rate control and reversibility of each elementary reaction. We introduce incorporation of Brønsted-Evans-Polanyi relations and scaling relations in microkinetic models and the effects of these relations on catalytic performance and formation of volcano curves are discussed. We review the analysis of reaction schemes in terms of the maximum rate of elementary reactions, and we outline a procedure to identify kinetically significant transition states and adsorbed intermediates. We explore the application of generalized rate expressions for the prediction of optimal binding energies of important surface intermediates and to estimate the extent of potential rate improvement. We also explore the application of microkinetic modeling in homogeneous catalysis, electro-catalysis, and transient reaction kinetics. We conclude by highlighting the challenges and opportunities in the application of microkinetic modeling for catalyst design.

An Integrated Approach to the Design of Centralized and Decentralized Biorefineries with Environmental, Safety, and Economic Objectives

Biorefineries provide economic, environmental, and social benefits towards sustainable development. Because of the relatively small size of typical biorefineries compared to oil and gas processes, it is necessary to evaluate the options of decentralized (or distributed) plants that are constructed near the biomass resources and product markets versus centralized (or consolidated) facilities that collect biomass from different regions and distribute the products to the markets, benefiting from the economy of scale but suffering from the additional transportation costs. The problem is further compounded when, in addition to the economic factors, environmental and safety aspects are considered. This work presents an integrated approach to the design of biorefining facilities while considering the centralized and decentralized options and the economic, environmental, and safety objectives. A superstructure representation is constructed to embed the various options of interest. A mathematical programming formulation is developed to transform the problem into an optimization problem. A new correlation is developed to estimate the capital cost of biorefineries and to facilitate the inclusion of the economic functions in the optimization program without committing to the type of technology or the size of the plant. A new metric called Total Process Risk is also introduced to evaluate the relative risk of the process. Life cycle analysis is applied to evaluate environmental emissions. The environmental and safety objectives are used to establish tradeoffs with the economic objectives. A case study is solved to illustrate the value and applicability of the proposed approach.

Trends and Outlook of Computational Chemistry and Microkinetic Modeling for Catalytic Synthesis of Methanol and DME

The first-principle modeling of heterogeneous catalysts is a revolutionarily approach, as the electronic structure of a catalyst is closely related to its reactivity on the surface with reactant molecules. In the past, detailed reaction mechanisms could not be understood, however, computational chemistry has made it possible to analyze a specific elementary reaction of a reaction system. Microkinetic modeling is a powerful tool for investigating elementary reactions and reaction mechanisms for kinetics. Using a microkinetic model, the dominant pathways and rate-determining steps can be elucidated among the competitive reactions, and the effects of operating conditions on the reaction mechanisms can be determined. Therefore, the combination of computational chemistry and microkinetic modeling can significantly improve computational catalysis research. In this study, we reviewed the trends and outlook of this combination technique as applied to the catalytic synthesis of methanol (MeOH) and dimethyl ether (DME), whose detailed mechanisms are still controversial. Although the scope is limited to the catalytic synthesis of limited species, this study is expected to provide a foundation for future works in the field of catalysis research based on computational catalysis.