Catalysts For Oxidative Dehydrogenation Research Articles

Acid-leaching treated ZnO-based materials as highly efficient microwave catalysts for oxidative dehydrogenation of propane by CO2

The oxidative dehydrogenation of propane by CO2 (CO2-ODHP) reaction has received much attention for its potential to produce high-value propylene from two underutilized reactants and to eliminate CO2. However, it is a great challenge to maintain high propylene selectivity with high propane conversion for CO2-ODHP reaction, especially for non-expensive and environmentally friendly catalysts. Herein, a new and effective ZnO/S-1(1.0)-based microwave catalyst for CO2-ODHP was developed by a simple impregnation method and acid leaching treatment. Interestingly, the acid leaching treatment significantly enhanced the catalytic activity and stability. Importantly, both the conversion of propane and the selectivity of propylene were significantly enhanced in the microwave catalytic reaction mode (MCRM). An amazingly high conversion of ∼60 % and selectivity of ∼92 % is achieved. This method of significantly improving the catalytic activity and stability after a simple acid leaching treatment can provide some inspiration for light alkane dehydrogenation reactions.

Promotional effect and mechanism of SiO2 on Cr-Al2O3 catalysts for N2O-assisted oxidative dehydrogenation of ethylbenzene

A series of Cr-SiO2-Al2O3 (abbreviated as Cr-Si-Al) catalysts were synthesized using an evaporation induced self-assembled (EISA) method and applied to the N2O-assisted dehydrogenation of ethylbenzene (EB), offering a versatile solution for greenhouse gas recycling and a novel pathway for styrene (ST) production. Notably, the Cr-5Si-Al catalyst calcinated at 850 °C exhibited remarkable performance with a high EB conversion of 71% and a good ST selectivity of 62%, resulting in an optimal ST yield of nearly 44%. To elucidate the impact of calcination temperature and reveal the underlying promoting mechanism, numerous types of characterization particularly in-situ DRIFTS were used. Results indicated that the Cr-5Si-Al-850 catalyst possessed a distinctively ordered mesoporous structure, which improved the specific surface area and facilitated the dispersion of active species. The interaction among the multiple species in the catalyst was enhanced under the high calcination temperatures, leading to an increased ratio of Cr6+ and Oα, and thus promoting the dehydrogenation of EB. Furthermore, in situ DRIFTS revealed two favorable reaction pathways: one involving the reaction between adsorbed N2O and EB, and the other involving the reaction between adsorbed EB and N2O. These pathways led to the formation of a similar intermediate, observed at a wavenumber of 1285 cm−1. The improved adsorption of EB played a pivotal role in the excellent catalytic performance of the Cr-5Si-Al-850 catalyst.

Deep Potential Molecular Dynamics Study of Propane Oxidative Dehydrogenation.

Oxidative dehydrogenation (ODH) of light alkanes is a key process in the oxidative conversion of alkanes to alkenes, oxygenated hydrocarbons, and COx (x = 1,2). Understanding the underlying mechanisms extensively is crucial to keep the ODH under control for target products, e.g., alkenes rather than COx, with minimal energy consumption, e.g., during the alkene production or maximal energy release, e.g., during combustion. In this work, deep potential (DP), a neural network atomic potential developed in recent years, was employed to conduct large-scale accurate reactive dynamic simulations. The model was trained on a sufficient data set obtained at the density functional theory level. The intricate reaction network was elucidated and organized in the form of a hierarchical network to demonstrate the key features of the ODH mechanisms, including the activation of propane and oxygen, the influence of propyl reaction pathways on the propene selectivity, and the role of rapid H2O2 decomposition for sustainable and efficient ODH reactions. The results indicate the more complex reaction mechanism of propane ODH than that of ethane ODH and are expected to provide insights in the ODH catalyst optimization. In addition, this work represents the first application of deep potential in the ODH mechanistic study and demonstrates the ample advantages of DP in the study of mechanism and dynamics of complex systems.

Intelligent Chemometric Modelling of Al2O3 Supported Mixed Metal Oxide Catalysts for Oxidative Dehydrogenation of n-Butane Using Simple Features

The development of efficient and selective catalysts for the oxidative dehydrogenation (ODH) of n-butane to produce butenes and butadiene with high performance has been subject of intense research in recent...

Explainable machine-learning predictions for catalysts in CO2-assisted propane oxidative dehydrogenation.

Propylene is an important raw material in the chemical industry that needs new routes for its production to meet the demand. The CO2-assisted oxidative dehydrogenation of propane (CO2-ODHP) represents an ideal way to produce propylene and uses the greenhouse gas CO2. The design of catalysts with high efficiency is crucial in CO2-ODHP research. Data-driven machine learning is currently of great interest and gaining popularity in the heterogeneous catalysis field for guiding catalyst development. In this study, the reaction results of CO2-ODHP reported in the literature are combined and analyzed with varied machine learning algorithms such as artificial neural network (ANN), k-nearest neighbors (KNN), support vector regression (SVR) and random forest regression (RF)and were used to predict the propylene space-time yield. Specifically, the RF method serves as a superior performing algorithm for propane conversion and propylene selectivity prediction, and SHapley Additive exPlanations (SHAP) based on the Shapley value performs fine model interpretation. Reaction conditions and chemical components show different impacts on catalytic performance. The work provides a valuable perspective for the machine learning in light alkane conversion, and helps us to design catalyst by catalytic performance hidden in the data of literatures.

Coffee charcoal as a green catalyst for oxidative dehydrogenation

Nitrogen heterocyclic (N-heterocyclic) compounds are extensively present in natural products and active drug molecules. However, their synthesis typically requires the use of noble metal catalysts and harsh conditions. In this study, we report a novel approach using coffee charcoal as a green catalyst for the mildly mechanically driven oxidative dehydrogenation of N-heterocyclic compounds. A one-step strategy was employed to construct a microporous coffee charcoal catalyst with C defects, in-situ co-doping of multi-atoms (N, O), and abundant modification of functional groups (–OH and –COOH). Under room temperature and with H2O as solvent, the coffee charcoal catalyst demonstrates a high yield of approximately 94 % and allows for gram-scale reactions with H2O being the only by-product. Moreover, the catalyst exhibits excellent reusability, maintaining its activity for over 13 cycles. Results indicate that the defects and atom doping can enhance the adsorption of O2, which is then activated to singlet oxygen (1O2) through the conversion of –OH functional groups on the catalyst’s surface. The singlet oxygen (1O2) can strengthen the oxidative dehydrogenation of N-heterocyclic compounds. Furthermore, the mechanically driven technique both activates the reaction interface and supplies the necessary reactive energy, thus providing a comprehensive explanation for the mild reaction conditions observed. In conclusion, our research proposes a sustainable and environmentally friendly method for the oxidative dehydrogenation of N-heterocyclic compounds, which can drive crucial reactions with significant relevance to sustainability.

Closed-loop optimization of catalysts for oxidative propane dehydrogenation with CO2 using artificial intelligence

Oxidative propane dehydrogenation with CO2 (ODPC) is an economical and ecofriendly process that produces propylene and consumes CO2 simultaneously. In this study, the catalyst composition for the ODPC reaction was optimized using a closed-loop optimization framework. A machine learning (ML) model was trained to predict the propylene yield and CO2 conversion using an in-house experimental database obtained from metal oxide catalysts containing various elements. The trained ML model optimized the chemical composition of the catalysts and simultaneously maximized the propylene yield and CO2 conversion using a metaheuristic algorithm. The proposed catalysts were prepared and their ODPC performance was evaluated. The data were included in the initial database to retrain the ML model. After this closed-loop optimization for 4 cycles, the proposed catalysts, which comprised four or five metal components, exhibited an enhanced ODPC performance compared with that of the initial database, which contained up to three metal components. Density functional theory calculations and characterization techniques were performed to investigate the role of each metal in the proposed catalyst. This study suggested a framework to optimize the chemical composition of multi-component catalysts to enhance the propylene yield and CO2 activity in the ODPC reaction.

Agglomerated Oxidative Dehydrogenation (ODH) Catalyst

Agglomerated Oxidative Dehydrogenation (ODH) Catalyst

Mesoporous Gallium-Based Catalysts for Oxidative Dehydrogenation of Propane in the Presence of Carbon Dioxide

Mesoporous Gallium-Based Catalysts for Oxidative Dehydrogenation of Propane in the Presence of Carbon Dioxide

Rational design of vanadia-based propane ODH catalysts: A multiple operando spectroscopic investigation of VOx/TiO2/CeO2

The understanding of reaction mechanisms of supported metal oxide catalysts has significantly increased over the last years due to new methods being applied. This increased knowledge allows to develop approaches towards catalytic materials for reactions with low selectivities by rational design, such as the oxidative dehydrogenation (ODH) of propane, which is of great technical importance. Vanadia (VOx) supported on reducible oxides (TiO2, CeO2, etc.) has shown promising catalytic properties in propane ODH. In this study, we followed a rational-design approach employing atomic layer deposition (ALD) to synthesize a VOx/TiOx/CeO2 catalyst with superior selectivity, by combining favourable properties of the individual catalysts (VOx/TiO2, VOx/CeO2). By applying multiple spectroscopies, including multi-wavelength Raman, UV-Vis, DRIFT, and XP spectroscopy as well as XRD, we were able to identify the functions of each oxide and develop a mechanistic picture. The increased selectivity is the result of distinct interactions between the oxides that slow down the oxygen dynamics in the catalyst and favour reaction pathways beneficial to propylene formation. Our findings highlight the use of rational design to develop improved catalysts based on previously established mechanistic knowledge.

Pathway to defective highly active and stable MoVSbOx catalysts for ethane oxidative dehydrogenation through a dislodging process involving controlled combustion of amino-organic compounds

MoVSbOx are synthesized using a new methodology that allows phase M1 nanorods exhibiting {001}, {120} and {210} lateral facets with accessible active sites to be created. During thermal activation, the combustion of the amino-organic compounds used in the synthesis generates defects along lateral facets, while the treatment with nitrogen takes octahedrally coordinated Mo, V and Sb units out of M1 structure. These defects facilitate the accessibility to additional active sites thereby producing very efficient catalysts for ethane oxidative dehydrogenation (E-ODH). Besides active phase M1, several additional crystalline phases are produced, monitored by TG/DSC and identified/quantified by XRD. The (trans)formation mechanism of them is elucidated by in-situ E-STEM analysis. The active site is determined by high resolution HAADF-STEM. Mo, V and Sb work synergically in the RedOx process, and the oxidation state balance is crucial for high activity and ethylene selectivity. The catalysts produced are very active (75 %), selective (90 C%) and stable over time in E-ODH. In-situ E-STEM under E-ODH conditions probes the stability of the MoVSbOx, a feature that is catalytically demonstrated in a continuous 480 h experiment in a bench-scale reactor on the quadrilobe-shaped scaled-up catalyst.

Surface Chemistry and Catalytic Reactivity of Borocarbonitride in Oxidative Dehydrogenation of Propane

AbstractBorocarbonitride (BCN) materials are newly developed oxidative dehydrogenation catalysts that can efficiently convert alkanes to alkenes. However, BCN materials tend to form bulky B2O3 due to over‐oxidation at the high reaction temperature, resulting in significant deactivation. Here, we report a series of super stable BCN nanosheets for the oxidative dehydrogenation of propane (ODHP) reaction. The catalytic performance of the BCN nanosheets can be easily regulated by changing the guanine dosage. The control experiment and structural characterization indicate that the introduction of a suitable amount of carbon could prevent the formation of excessive B2O3 from BCN materials and maintain the 2D skeleton at a high temperature of 520 °C. The best‐performing catalyst BCN exhibits 81.9 % selectivity towards olefins with a stable propane conversion of 35.8 %, and the propene productivity reaches 16.2 mmol h−1 g−1, which is much better than hexagonal BN (h‐BN) catalysts. Density functional theory calculation results show that the presence of dispersed rather than aggregated carbon atoms can significantly affect the electronic microenvironment of h‐BN, thereby boosting the catalytic activity of BCN.

Surface Chemistry and Catalytic Reactivity of Borocarbonitride in Oxidative Dehydrogenation of Propane.

Borocarbonitride (BCN) materials are newly developed oxidative dehydrogenation catalysts that can efficiently convert alkanes to alkenes. However, BCN materials tend to form bulky B2 O3 due to over-oxidation at the high reaction temperature, resulting in significant deactivation. Here, we report a series of super stable BCN nanosheets for the oxidative dehydrogenation of propane (ODHP) reaction. The catalytic performance of the BCN nanosheets can be easily regulated by changing the guanine dosage. The control experiment and structural characterization indicate that the introduction of a suitable amount of carbon could prevent the formation of excessive B2 O3 from BCN materials and maintain the 2D skeleton at a high temperature of 520 °C. The best-performing catalyst BCN exhibits 81.9 % selectivity towards olefins with a stable propane conversion of 35.8 %, and the propene productivity reaches 16.2 mmol h-1 g-1 , which is much better than hexagonal BN (h-BN) catalysts. Density functional theory calculation results show that the presence of dispersed rather than aggregated carbon atoms can significantly affect the electronic microenvironment of h-BN, thereby boosting the catalytic activity of BCN.

Cu-Based Bimetallic Catalysts for Electrocatalytic Oxidative Dehydrogenation of Furfural with Practical Rates.

Electrocatalytic oxidative dehydrogenation (EOD) of aldehydes enables ultra-low voltage, bipolar H2 production with co-generation of carboxylic acid. Herein, we reported a simple galvanic replacement method to prepare CuM (M = Pt, Pd, Au, and Ag) bimetallic catalysts to improve the EOD of furfural to reach industrially relevant current densities. The redox potential difference between Cu/Cu2+ and a noble metal M/My+ can incorporate the noble metal on the Cu surface and enlarge its surface area. Particularly, dispersing Pt in Cu (CuPt) achieved a record-high current density of 498 mA cm-2 for bipolar H2 production at a low cell voltage of 0.6 V and a Faradaic efficiency of >80% to H2. Future research is needed to deeply understand the synergistic effects of Cu-M toward EOD of furfural, and improve the Cu-M catalyst stability, thus offering great opportunities for future distributed manufacturing of green hydrogen and carbon chemicals with practical rates and low-carbon footprints.

Developing Machine Learning Models for Catalysts in Oxidative Dehydrogenation of n‐butane

Abstract1,3‐butadiene is widely used as a raw material in the synthesis of rubber and automobile tires. In this study, machine learning models were utilized to predict the performance of catalysts in the oxidative dehydrogenation of n‐butane to 1,3‐butadiene., The datasets consisted of 304 data points generated from experiments conducted in a fixed‐bed reactor in the temperature range of 400– 500 °C and O2/C4 molar feed ratio of 1–4 mol/mol, using metal oxides of Nickel (Ni), Iron (Fe), Cobalt (Co), Bismuth (Bi), Molybdenum (Mo), Manganese (Mn) and Tungsten (W) supported on gamma alumina catalysts. Repeated 10‐fold cross‐validation was used, with 70 % of the datasets randomly selected for training and optimizing the models, whereas the remaining 30 % were used for testing the models. Among the various models examined, support vector machine with radial basis function (SVMR) model had the best coefficient of determination (R2) performance for the n‐butane conversion (training set: 98.3 % vs. test set: 88.3 %) and 1,3‐butadiene selectivity (training set: 94.0 % vs. test set: 88.6 %). In addition, the models had the lowest training and test mean absolute errors (MAE) and root mean square errors (RMSE). This study highlights the notable role of machine learning algorithms in enhancing the prediction of n‐butane oxidative dehydrogenation catalyst performance, thus promoting the development of more effective catalyst designs that exhibit high activity and selectivity, contributing positively to the field of heterogeneous catalysis. Notably, a user‐friendly application incorporating the developed machine learning models was developed, providing an invaluable tool for the real‐time on‐site prediction of catalyst performance in the oxidative dehydrogenation of n‐butane to 1,3‐butadiene. This application is a significant advancement, facilitating swift and accurate catalyst performance prediction, fostering more efficient catalyst design, streamlining, and revolutionizing the rubber and automobile tire synthesis industries.

A metal-free carbon catalyst for oxidative dehydrogenation of aryl cyclohexenes to produce biaryl compounds

A metal-free route based on a carbon catalyst to synthesize biphenyls through oxidative dehydrogenation (ODH) of phenyl cyclohexene has been investigated. Among the samples examined, an air-oxidized active carbon exhibits the best activity with a 9.1 × 10-2 h-1 rate constant, yielding 74% biphenyl in 28 h at 140 °C under five bar O2 in anisole. The apparent activation energy is measured as 54.5 kJ⋅mol-1. The extended reaction scope, consisting of 15 differently substituted phenyl cyclohexenes, shows the wide applicability of the proposed method. The catalyst's good recyclability over six runs suggests this ODH method as a promising route to access the biaryl compounds. In addition, the reaction mechanism is investigated with a combination of X-ray photoelectron spectroscopy, functional group blocking, and model compounds of carbon catalysts and is proposed to be based on the redox cycle of the quinoidic groups on the carbon surface. Additional experiments prove that the addition of the catalytic amount of acid (methanesulfonic acid) accelerates the reaction. In addition, Hammett plot examination suggests the formation of a carbonium intermediate, and its possible structure is outlined.

Dimensional Understanding of Boron-Based Catalysts for Oxidative Propane Dehydrogenation: Structure and Mechanism

Dimensional Understanding of Boron-Based Catalysts for Oxidative Propane Dehydrogenation: Structure and Mechanism

Lattice Oxygen Regulation of Perovskites for Chemical Looping Oxidative Dehydrogenation of Ethylbenzene to Styrene

The direct dehydrogenation process produces over 90% of global styrene. However, it is hampered by high energy consumption due to equilibrium limitation, a high steam/ethylbenzene feeding ratio, and complicated separation procedures. In this study, we propose the synthesis of a novel perovskite-structured metal oxide as the redox catalyst for chemical looping oxidative dehydrogenation (CL-ODH) of ethylbenzene into styrene. Lower ethylbenzene activation temperature and higher oxygen storage capacity contributing to high ethylbenzene conversion are achieved by Mn doping of the B site in SrFeO3−δ, and styrene yield is further enhanced by Ba doping of the A site. The optimized Sr0.8Ba0.2Fe0.2Mn0.8O3−δ catalyst exhibits favorable dehydrogenation activity but is deactivated in the first five redox cycles due to the severe carbonation and perovskite structure decomposition. The decarbonization treatment on the catalyst at 950 °C in an O2 atmosphere restores the perovskite structure and dehydrogenation activity, resulting in 85% ethylbenzene conversion and 89% styrene selectivity at 550 °C. Ethylbenzene on the catalyst undergoes full combustion via adsorbed oxygen, ODH of ethylbenzene into styrene and H2O using lattice oxygen, and direct dehydrogenation without available lattice oxygen. The proposed redox catalyst demonstrates efficient conversion of ethylbenzene into styrene, showing great potential for the energy conservation and process intensification of styrene production.

Support Effect of Ga-Based Catalysts in the CO2-Assisted Oxidative Dehydrogenation of Propane

Carbon dioxide (CO2) assisted oxidative dehydrogenation of propane over Ga-modified catalysts is highly sensitive to the identity of support, but the underlying cause of support effects has not been well established. In this article, SSZ-13, SSZ-39, ZSM-5, silica and γ-Al2O3 were used to load Ga species by incipient wet impregnation. The structure, textural properties, acidity of the Ga-based catalysts and the process of CO2-assisted oxidative dehydrogenation of propane were examined by X-ray diffraction (XRD), nitrogen physisorption (N2 physisorption), ammonia temperature-programmed desorption (NH3-TPD), pyridine chemisorbed Fourier transform infrared spectra (Py-FTIR), OH-FTIR and in situ FTIR. Evaluation of the catalytic performance combined with detailed catalyst characterization suggests that their dehydrogenation activity is positively associated with the number of acid sites in middle strength, confirming that the Lewis acid sites generated by Ga cations are the active species in the reaction. Ga/Na-SSZ-39(9) also has feasible acidic strength and a unique channel structure, which is conducive to the dissociative adsorption of propane and desorption of olefins. The Ga/Na-SSZ-39(9) catalysts showed superior olefins selectivity and catalytic stability at 600 ℃ compared to any other catalysts. This approach to quantifying support acid strength, and channel structure and applying it as a key catalytic descriptor of support effects is a useful tool to enable the rational design of next-generation CO2-assisted oxidative dehydrogenation catalysts.

Formation of active phases of Fe/C, Cr/C and Fe–Cr/C catalysts in oxidative dehydrogenation of ethane

The oxidative dehydrogenation of ethane into ethylene using CO2 as an oxidant at temperatures of 650–750 °C was carried out over Fe/C, Cr/C and Fe–Cr/C catalysts deposited on a carbon support. Before and after the reaction the catalysts were investigated by X-ray powder diffraction (XRD), in situ magnetometry and transmission electron microscopy methods. The correlation between activity of Fe/C, Cr/C and Fe–Cr/C catalytic systems and their phase composition was established.