Activity Trends for Catalytic CO and NO Co-Oxidation at Low Temperature Diesel Emission Conditions

Screening of bimetallic electrocatalysts for water purification with machine learning

Electrocatalysis provides a potential solution to [Formula: see text] pollution in wastewater by converting it to innocuous N2 gas. However, materials with excellent catalytic activity are typically limited to expensive precious metals, hindering their commercial viability. In response to this challenge, we have conducted the most extensive computational search to date for electrocatalysts that can facilitate [Formula: see text] reduction reaction, starting with 59 390 candidate bimetallic alloys from the Materials Project and Automatic-Flow databases. Using a joint machine learning- and computation-based screening strategy, we evaluated our candidates based on corrosion resistance, catalytic activity, N2 selectivity, cost, and the ability to synthesize. We found that only 20 materials will satisfy all criteria in our screening strategy, all of which contain varying amounts of Cu. Our proposed list of candidates is consistent with previous materials investigated in the literature, with the exception of Cu–Co and Cu–Ag based compounds that merit further investigation.

Partial reduction of NO to N2O on Cu{311}: role of intermediate N2O2

Reflection absorption infrared spectroscopy (RAIRS) and first-principles density functional theory (DFT) combine to suggest a pathway for NOx reduction on Cu{311} involving a flat-lying N2O2 intermediate.

Effect of Ce Addition on Adsorption and Oxidation of NO over MnO x /Al2O3

The MnO ${}_x$ /Al2O3 catalysts with different Ce content doping were prepared by an ultrasonic impregnation method, and the catalytic activity for NO oxidation removal was tested in a fixed-bed quartz tube furnace. Simultaneously, the catalysts were characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), full-automatic physical-chemical adsorption instrument, and field emission scanning electron microscope (FESEM) to analyze the effect of Ce addition on the adsorption capacity and catalytic activity. Experimental results validated that the activity of the MnO ${}_x$ /Al2O3 catalyst was greatly promoted with Ce addition. According to the characterization results, it could be concluded that Ce doping led to significant changes in the crystalline phase on the catalyst surface, which increased the relative content of surface lattice oxygen and promoted the catalytic oxidation of NO. By observing the physical properties of the surface and analyzing the surface elements of the catalyst, it could be inferred that a manganese-cerium solid solution was formed on the surface of Mn0.4Ce0.05/Al. Moreover, Ce addition increased the catalyst pore size, which enhanced the adsorption and contact of NO and O2 with the active sites on the catalyst surface, and reduced the resistance of the reactants during internal diffusion. All these variations assigned to Mn0.4Ce0.05/Al were favorable for the catalytic oxidation of NO.

Predicting metal-metal interactions. II. Accelerating generalized schemes through physical insights

Operando-computational frameworks that integrate descriptors for catalyst stability within catalyst screening paradigms enable predictions of rates and selectivity on chemically faithful representations of nanoparticles under reaction conditions. These catalyst stability descriptors can be efficiently predicted by density functional theory (DFT)-based models. The alloy stability model, for example, predicts the stability of metal atoms in nanoparticles with site-by-site resolution. Herein, we use physical insights to present accelerated approaches of parameterizing this recently introduced alloy-stability model. These accelerated approaches meld quadratic functions for the energy of metal atoms in terms of the coordination number with linear correlations between model parameters and the cohesive energies of bulk metals. By interpolating across both the coordination number and chemical space, these accelerated approaches shrink the training set size for 12 fcc p- and d-block metals from 204 to as few as 24 DFT calculated total energies without sacrificing the accuracy of our model. We validate the accelerated approaches by predicting adsorption energies of metal atoms on extended surfaces and 147 atom cuboctahedral nanoparticles with mean absolute errors of 0.10 eV and 0.24 eV, respectively. This efficiency boost will enable a rapid and exhaustive exploration of the vast material space of transition metal alloys for catalytic applications.

Predicting metal-metal interactions. I. The influence of strain on nanoparticle and metal adlayer stabilities

Strain-engineering of bimetallic nanomaterials is an important design strategy for developing new catalysts. Herein, we introduce an approach for including strain effects into a recently introduced, density functional theory (DFT)-based alloy stability model. The model predicts adsorption site stabilities in nanoparticles and connects these site stabilities with catalytic reactivity and selectivity. Strain-based dependencies will increase the model's accuracy for nanoparticles affected by finite-size effects. In addition to the stability of small nanoparticles, strain also influences the heat of adsorption of epitaxially grown metal-on-metal adlayers. In this respect, we successfully benchmark the strain-including alloy stability model with previous experimentally determined trends in the heats of adsorption of Au and Cu adlayers on Pt (111). For these systems, our model predicts stronger bimetallic interactions in the first monolayer than monometallic interactions in the second monolayer. We explicitly quantify the interplay between destabilizing strain effects and the energy gained by forming new metal-metal bonds. While tensile strain in the first Cu monolayer significantly destabilizes the adsorption strength, compressive strain in the first Au monolayer has a minimal impact on the heat of adsorption. Hence, this study introduces and, by comparison with previous experiments, validates an efficient DFT-based approach for strain-engineering the stability, and, in turn, the catalytic performance, of active sites in bimetallic alloys with atomic level resolution.