Identification of Active Metal Carbide and Nitride Catalytic Facets for Hydrodeoxygenation Reactions

Abstract

The catalytic hydrodeoxygenation (HDO) reaction is of considerable interest for biomass conversion to valuable chemicals and fuels, where one of the critical bottlenecks is the lack of cost-effective and efficient catalysts. To discover cost-efficient catalysts for the HDO reaction, we employed a density functional theory-based hierarchical catalyst design strategy based on catalytic descriptors, reaction energy profiles, and microkinetic modeling (MKM). We focused on the carbide and nitride catalyst space, for which we calculated 121 catalyst surfaces of Mo2C, MoC, Mo2N, W2C, NbC, VC, VN, and NbN catalysts. Based on the computed surface energies, reaction energies of oxygen removal, carbon binding strength, and the surface area of nanoparticles, the likely active facets are the Mo2C­(111), MoC(011), VN(100), Mo2N­(001), Mo2N­(011), and Mo2N­(100) surfaces. Further, detailed energy profiles were obtained, and MKM was performed for a model reaction (glycolaldehyde + 2H2 → ethylene + 2H2O) on the Mo2C­(111), VN(100), and MoC(100) surfaces. Based on the computed volcano map obtained from MKM, the predicted active facets for this HDO reaction are the Mo2C­(111), MoC(011), VN(011), Mo2N­(001), Mo2N­(011), and Mo2N­(100) surfaces. Additionally, none of the carbide and nitride catalyst surfaces are located in the optimal catalytic activity part. Therefore, it is essential to modify the catalyst via adding dopants or alloying to improve the catalytic activity. Catalytic modifications that can destabilize the surface adsorption of O*/H2O* and decrease the energy barriers of O–H bond formation are recommended to facilitate the HDO on the carbide and nitride catalysts. These a priori investigations provide guidelines for future low-cost HDO catalyst development.

Construction of a highly active and water-resistant Ni-based catalyst for the HDO reaction of phenol

“…8,9 The development of highly active catalysts for HDO technology is an important factor but still remains a great challenge. Many kinds of catalysts, such as noble metal catalysts, [10][11][12] metal sulde catalysts, 13,14 metal carbide catalysts 15,16 and transition metal catalysts [17][18][19] are widely used. However, noble metal catalysts are not appropriate for industrial utilization because of their high price, difficult availability, poor reusability and easy deactivation caused by carbon deposition.…”

Rational design of catalysts with earth‐abundant elements

“…For the reverse reaction, Haider et al investigated the hydrodeoxygenation of ethanol on metal and bimetallic alloy catalysts using C* and O* as descriptors, where Ir, Ru, Rh, and Ni were found to produce mainly methane, while bimetallic catalysts Co 3 Ni and Ni 3 Fe produced more ethane 80 . Moreover, Assary et al predicted Mo 2 C(111) as a good candidate catalyst for ethylene production from glycolaldehyde hydrodeoxygenation, where the intermediates HOCH 2 CHO* and O* were used as the descriptors 81 . For the n‐butane oxidation reaction, Schweitzer et al identified Ag 3 Pd as a promising nanoparticle composition for selective 1‐butanol production using OH* and C* as descriptors 82 .…”

Accelerating the evaluation of crucial descriptors for catalyst screening via message passing neural network

“…The high oxygen content in bio-oils is undesirable and oen removed using hydrodeoxygenation (HDO), a catalytic process in which C-O bond cleavage is carried out in the presence of H 2 gas as the reductant. 2 Transition metal carbides have been considered the promising catalyst candidates for HDO reactions [3][4][5][6][7][8][9] as they are of low cost and, in the case of Mo 2 C, can achieve Pt-like catalytic activity. 7 Furthermore, strategies to modify Mo 2 C catalysts with transition metals to enhance HDO activity has been employed in both experimental and computational studies.…”

“…In recent years, computational screenings of descriptors have been carried out for various catalytic systems including bimetallic alloys, [17][18][19] single-atom catalysts, 20 and transition metal carbides and nitrides 6 via density functional theory (DFT) simulations. Nevertheless, the high cost of DFT calculations remains the bottleneck of high-throughput descriptor generation.…”