Experimental and Computational Investigation of the Role of P in Moderating Ethane Dehydrogenation Performance over Ni-Based Catalysts

Observation and Characterization of Vibrationally Active Surface Species Accessed with Nonthermal Nitrogen Plasmas

Polycrystalline Ni, Pd, Cu, Ag, and Au foils exposed to nonthermal plasma (NTP)-activated N2 are found to exhibit a vibrational feature near 2200 cm-1 in polarization-modulation infrared reflection-absorption spectroscopy (PM-IRAS) observations that are not present in the same materials exposed to N2 under nonplasma conditions. The feature is similar to that reported elsewhere and is typically assigned to chemisorbed N2. We employ a combination of temperature-dependent experiments, sequential dosing, X-ray photoelectron spectroscopy, isotopic labeling, and density functional theory calculations to characterize the feature. Results are most consistent with a triatomic species, likely NCO, with the C and O likely originating from ppm-level impurities in the ultrahigh-purity (UHP) Ar and/or N2 gas cylinders. The work highlights the potential for nonthermal plasmas to access adsorbates inaccessible thermally as well as the potential contributions of ppm-level impurities to corrupt the interpretation of plasma catalytic chemistry.

Mechanism research reveals the role of Fen (n = 2-5) supported C2N as single-cluster catalysts (SCCs) for the non-oxidative propane dehydrogenation in the optimization of catalytic performance

Single cluster catalysts show excellent potential for propane dehydrogenation, compensating for the limited catalytic performance of single-atom catalysts in reactions involving multiple reaction steps and intermediates. Herein, density functional theory is used to investigate the catalytic activity and mechanism for non-oxidized propane dehydrogenation on Fen-C2N (n = 2-5). Firstly, the stability of Fen-C2N (n = 2-5) is evaluated by comparing the mean values of binding energy and cohesive energy. The results show that Fen-C2N (n = 2-4) can exist stably, which is also verified by the molecular dynamics calculation at 873 K. Band structure analysis shows that the screened catalysts have metal properties, which are conducive to charge transfer. Fukui function analysis is used to predict the optimal adsorption site. The electronic properties of propane and propylene adsorbed on catalysts are further studied by the partial density of states and deformation charge density. The activation barrier (Ea) and reaction energy (ΔE) of the main reaction steps are evaluated. The results show that Fe2-C2N (Ea = 0.97 eV, ΔE= 0.22 eV) has the best catalytic activity. The Ea for further propylene dehydrogenation is also used to evaluate the yield of propylene. Compared with Fe-C2N, Fe2-C2N can regulate the adsorption strength of propane and propylene, showing better catalytic ability and higher selectivity for propylene. The above research provides ideas for the design of new catalysts with high selectivity and activity for non-oxidative propane dehydrogenation.

Nonlinear Reactor Design Optimization With Embedded Microkinetic Model Information

Despite the success of multiscale modeling in science and engineering, embedding molecular-level information into nonlinear reactor design and control optimization problems remains challenging. In this work, we propose a computationally tractable scale-bridging approach that incorporates information from multi-product microkinetic (MK) models with thousands of rates and chemical species into nonlinear reactor design optimization problems. We demonstrate reduced-order kinetic (ROK) modeling approaches for catalytic oligomerization in shale gas processing. We assemble a library of six candidate ROK models based on literature and MK model structure. We find that three metrics—quality of fit (e.g., mean squared logarithmic error), thermodynamic consistency (e.g., low conversion of exothermic reactions at high temperatures), and model identifiability—are all necessary to train and select ROK models. The ROK models that closely mimic the structure of the MK model offer the best compromise to emulate the product distribution. Using the four best ROK models, we optimize the temperature profiles in staged reactors to maximize conversions to heavier oligomerization products. The optimal temperature starts at 630–900K and monotonically decreases to approximately 560 K in the final stage, depending on the choice of ROK model. For all models, staging increases heavier olefin production by 2.5% and there is minimal benefit to more than four stages. The choice of ROK model, i.e., model-form uncertainty, results in a 22% difference in the objective function, which is twice the impact of parametric uncertainty; we demonstrate sequential eigendecomposition of the Fisher information matrix to identify and fix sloppy model parameters, which allows for more reliable estimation of the covariance of the identifiable calibrated model parameters. First-order uncertainty propagation determines this parametric uncertainty induces less than a 10% variability in the reactor optimization objective function. This result highlights the importance of quantifying model-form uncertainty, in addition to parametric uncertainty, in multi-scale reactor and process design and optimization. Moreover, the fast dynamic optimization solution times suggest the ROK strategy is suitable for incorporating molecular information in sequential modular or equation-oriented process simulation and optimization frameworks.

Computational screen of M2P metal phosphides for catalytic ethane dehydrogenation

Metal phosphide screening for ethane dehydrogenation.

Ethane dehydrogenation performance and high temperature stability of silica supported cobalt phosphide nanoparticles

Cobalt phosphide catalysts exhibit remarkable stability and selectivity for ethane dehydrogenation.

C-H bond activation in light alkanes: a theoretical perspective

Alkanes are the major constituents of natural gas and crude oil, the feedstocks for the chemical industry. The efficient and selective activation of C-H bonds can convert abundant and low-cost hydrocarbon feedstocks into value-added products. Due to the increasing global demand for light alkenes and their corresponding polymers as well as synthesis gas and hydrogen production, C-H bond activation of light alkanes has attracted widespread attention. A theoretical understanding of C-H bond activation in light hydrocarbons via density functional theory (DFT) and microkinetic modeling provides a feasible approach to gain insight into the process and guidelines for designing more efficient catalysts to promote light alkane transformation. This review describes the recent progress in computational catalysis that has addressed the C-H bond activation of light alkanes. We start with direct and oxidative C-H bond activation of methane, with emphasis placed on kinetic and mechanistic insights obtained from DFT assisted microkinetic analysis into steam and dry reforming, and the partial oxidation dependence on metal/oxide surfaces and nanoparticle size. Direct and oxidative activation of the C-H bond of ethane and propane on various metal and oxide surfaces are subsequently reviewed, including the elucidation of active sites, intriguing mechanisms, microkinetic modeling, and electronic features of the ethane and propane conversion processes with a focus on suppressing the side reaction and coke formation. The main target of this review is to give fundamental insight into C-H bond activation of light alkanes, which can provide useful guidance for the optimization of catalysts in future research.