Surfaces of Pd-based alloys as catalysts for CO2 activation and hydrogenation to methanol

This thesis investigates the active sites and reactions involved in CO2 hydrogenation to methanol on the promising Pd-based catalysts. Using state-of-the-art simulation techniques, the study aims to identify catalyst features enhancing CO2 activation via the formate mechanism and develop descriptors for high-throughput computational analysis of Pd-alloy surfaces. The mechanism of CO2 hydrogenation to methanol on low-index Pd facets is explored in detail using density functional theory calculations. The rate-determining step involves HCOOH to H2COOH conversion; however, the Gibbs free energy analysis reveals that the reaction on Pd surfaces is limited at the initial hydrogenation of CO2 to formate. Further, hydrogen adsorption on Pd (111) and Pd (100) surfaces is analysed, considering an exhaustive number of configurations at coverages of up to 2 monolayers. We predict that subsurface hydrogen is present on Pd surfaces at hydrogen coverages expected at reaction conditions relevant for CO2 hydrogenation. Initial stages of CO2 hydrogenation are modelled on low-index metallic Cu, Pd, Zn and alloy CuPd and PdZn surfaces. CuPd (110) surface shows a CO2 hydrogenation barrier similar to that on Pd (100) but inhibits CO2 dissociation linked to lower selectivity of CO2 hydrogenation to methanol. Moreover, the CuPd (110) surface shows a significantly stronger hydrogen adsorption as compared to Cu surfaces. The PdZn surfaces show lower activation energy barriers for CO2 hydrogenation than Pd facets, but CO2 chemisorption constitutes most of the barrier, suggesting an Eley-Rideal mechanism of CO2 hydrogenation on PdZn surfaces. The observed reaction energy barriers correlate with the characteristics of the monodentate formate intermediate. Overall, this thesis aims to understand the behaviour of CO2 and hydrogen on Pd-based catalysts, illustrating the power of simulation in explaining experimental observations and facilitating rational catalyst design to address climate change in the transportation sector

A computational study of direct CO₂ hydrogenation to methanol on Pd surfaces

The reaction mechanism of direct CO2 hydrogenation to methanol is investigated in detail on Pd (111), (100) and (110) surfaces using density functional theory (DFT), supporting investigations into emergent Pd-based catalysts. Hydrogen adsorption and surface mobility are firstly considered, with high-coordination surface sites having the largest adsorption energy and being connected by diffusion channels with low energy barriers. Surface chemisorption of CO2, forming a partially charged CO2δ−, is weakly endothermic on a Pd (111) whilst slightly exothermic on Pd (100) and (110), with adsorption enthalpies of 0.09, −0.09 and −0.19 eV, respectively; the low stability of CO2δ− on the Pd (111) surface is attributed to negative charge accumulating on the surface Pd atoms that interact directly with the CO2δ− adsorbate. Detailed consideration for sequential hydrogenation of the CO2 shows that HCOOH hydrogenation to H2COOH would be the rate determining step in the conversion to methanol, for all surfaces, with activation barriers of 1.41, 1.51, and 0.84 eV on Pd (111), (100) and (110) facets, respectively. The Pd (110) surface exhibits overall lower activation energies than the most studied Pd (111) and (100) surfaces, and therefore should be considered in more detail in future Pd catalytic studies

Investigation of the Pd(1−x)Znx alloy phase diagram using ab initio modelling approaches

The identification of the stable phases in alloy materials is challenging because composition affects the structural stability of different intermediate phases. Computational simulation, via multiscale modelling approaches, can significantly accelerate the exploration of phase space and help to identify stable phases. Here, we apply such new approaches to understand the complex phase diagram of binary alloys of PdZn, with the relative stability of structural polymorphs considered through application of density functional theory coupled with cluster expansion (CE). The experimental phase diagram has several competing crystal structures, and we focus on three different closed-packed phases that are commonly observed for PdZn, namely the face-centred cubic (FCC), body-centred tetragonal (BCT) and hexagonal close packed (HCP), to identify their respective stability ranges. Our multiscale approach confirms a narrow range of stability for the BCT mixed alloy, within the Zn concentration range from 43.75% to 50%, which aligns with experimental observations. We subsequently use CE to show that the phases are competitive across all concentrations, but with the FCC alloy phase favoured for Zn concentrations below 43.75%, and that the HCP structure favoured for Zn-rich concentrations. Our methodology and results provide a platform for future investigations of PdZn and other close-packed alloy systems with multiscale modelling techniques

A computational study of the properties of low- and high-index Pd, Cu and Zn surfaces

We report a detailed Density Functional Theory (DFT) based investigation of the structure and stability of bulk and surface structures for the Group 10–12 elements Pd, Cu and Zn, considering the effect of the choice of exchange–correlation density functional and computation parameters. For the initial bulk structures, the lattice parameter and cohesive energy are calculated, which are then augmented by calculation of surface energies and work functions for the lower-index surfaces. Of the 22 density functionals considered, we highlight the mBEEF density functional as providing the best overall agreement with experimental data. The optimal density functional choice is applied to the study of higher index surfaces for the three metals, and Wulff constructions performed for nanoparticles with a radius of 11 nm, commensurate with nanoparticle sizes commonly employed in catalytic chemistry. For Pd and Cu, the low-index (111) facet is dominant in the constructed nanoparticles, covering ∼50% of the surface, with (100) facets covering a further 10 to 25%; however, non-negligible coverage from higher index (332), (332) and (210) facets is also observed for Pd, and (322), (221) and (210) surfaces are observed for Cu. In contrast, only the (0001) and (10−10) facets are observed for Zn. Overall, our results highlight the need for careful validation of computational settings before performing extensive density functional theory investigations of surface properties and nanoparticle structures of metals

A computational study of direct CO2 hydrogenation to methanol on Pd surfaces

The reaction mechanism of direct CO2 hydrogenation to methanol is investigated in detail on Pd (111), (100) and (110) surfaces using density functional theory (DFT), supporting investigations into emergent Pd-based catalysts. Hydrogen adsorption and surface mobility are firstly considered, with high-coordination surface sites having the largest adsorption energy and being connected by diffusion channels with low energy barriers. Surface chemisorption of CO2, forming a partially charged CO2δ−, is weakly endothermic on a Pd (111) whilst slightly exothermic on Pd (100) and (110), with adsorption enthalpies of 0.09, −0.09 and −0.19 eV, respectively; the low stability of CO2δ− on the Pd (111) surface is attributed to negative charge accumulating on the surface Pd atoms that interact directly with the CO2δ− adsorbate. Detailed consideration for sequential hydrogenation of the CO2 shows that HCOOH hydrogenation to H2COOH would be the rate determining step in the conversion to methanol, for all surfaces, with activation barriers of 1.41, 1.51, and 0.84 eV on Pd (111), (100) and (110) facets, respectively. The Pd (110) surface exhibits overall lower activation energies than the most studied Pd (111) and (100) surfaces, and therefore should be considered in more detail in future Pd catalytic studies

Investigation of the Pd (1− x ) Zn x alloy phase diagram using ab initio modelling approaches

The identification of the stable phases in alloy materials is challenging because composition affects the structural stability of different intermediate phases. Computational simulation, via multiscale modelling approaches, can significantly accelerate the exploration of phase space and help to identify stable phases. Here, we apply such new approaches to understand the complex phase diagram of binary alloys of PdZn, with the relative stability of structural polymorphs considered through application of density functional theory coupled with cluster expansion (CE). The experimental phase diagram has several competing crystal structures, and we focus on three different closed-packed phases that are commonly observed for PdZn, namely the face-centred cubic (FCC), body-centred tetragonal (BCT) and hexagonal close packed (HCP), to identify their respective stability ranges. Our multiscale approach confirms a narrow range of stability for the BCT mixed alloy, within the Zn concentration range from 43.75% to 50%, which aligns with experimental observations. We subsequently use CE to show that the phases are competitive across all concentrations, but with the FCC alloy phase favoured for Zn concentrations below 43.75%, and that the HCP structure favoured for Zn-rich concentrations. Our methodology and results provide a platform for future investigations of PdZn and other close-packed alloy systems with multiscale modelling techniques

Heterogeneous Trimetallic Nanoparticles as Catalysts

The development and application of trimetallic nanoparticles continues to accelerate rapidly as a result of advances in materials design, synthetic control, and reaction characterization. Following the technological successes of multicomponent materials in automotive exhausts and photovoltaics, synergistic effects are now accessible through the careful preparation of multielement particles, presenting exciting opportunities in the field of catalysis. In this review, we explore the methods currently used in the design, synthesis, analysis, and application of trimetallic nanoparticles across both the experimental and computational realms and provide a critical perspective on the emergent field of trimetallic nanocatalysts. Trimetallic nanoparticles are typically supported on high-surface-area metal oxides for catalytic applications, synthesized via preparative conditions that are comparable to those applied for mono- and bimetallic nanoparticles. However, controlled elemental segregation and subsequent characterization remain challenging because of the heterogeneous nature of the systems. The multielement composition exhibits beneficial synergy for important oxidation, dehydrogenation, and hydrogenation reactions; in some cases, this is realized through higher selectivity, while activity improvements are also observed. However, challenges related to identifying and harnessing influential characteristics for maximum productivity remain. Computation provides support for the experimental endeavors, for example in electrocatalysis, and a clear need is identified for the marriage of simulation, with respect to both combinatorial element screening and optimal reaction design, to experiment in order to maximize productivity from this nascent field. Clear challenges remain with respect to identifying, making, and applying trimetallic catalysts efficiently, but the foundations are now visible, and the outlook is strong for this exciting chemical field

Heterogeneous trimetallic nanoparticles as catalysts

The development and application of trimetallic nanoparticles continues to accelerate rapidly as a result of advances in materials design, synthetic control, and reaction characterization. Following the technological successes of multicomponent materials in automotive exhausts and photovoltaics, synergistic effects are now accessible through the careful preparation of multielement particles, presenting exciting opportunities in the field of catalysis. In this review, we explore the methods currently used in the design, synthesis, analysis, and application of trimetallic nanoparticles across both the experimental and computational realms and provide a critical perspective on the emergent field of trimetallic nanocatalysts. Trimetallic nanoparticles are typically supported on high-surface-area metal oxides for catalytic applications, synthesized via preparative conditions that are comparable to those applied for mono- and bimetallic nanoparticles. However, controlled elemental segregation and subsequent characterization remain challenging because of the heterogeneous nature of the systems. The multielement composition exhibits beneficial synergy for important oxidation, dehydrogenation, and hydrogenation reactions; in some cases, this is realized through higher selectivity, while activity improvements are also observed. However, challenges related to identifying and harnessing influential characteristics for maximum productivity remain. Computation provides support for the experimental endeavors, for example in electrocatalysis, and a clear need is identified for the marriage of simulation, with respect to both combinatorial element screening and optimal reaction design, to experiment in order to maximize productivity from this nascent field. Clear challenges remain with respect to identifying, making, and applying trimetallic catalysts efficiently, but the foundations are now visible, and the outlook is strong for this exciting chemical field

Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants with Treatment Resistance in Schizophrenia

Importance: About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts. Objective: To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples. Design, Setting, and Participants: Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n = 10501) and individuals with non-TRS (n = 20325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]). Main Outcomes and Measures: GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition. Results: The study included a total of 85490 participants (48635 [56.9%] male) in its GWAS stage and 1380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41-0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (r2 = 2.03%; P =.001), which was replicated in the STRATA-G incidence sample (r2 = 1.09%; P =.04). Conclusions and Relevance: In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance

A Computational Study of Direct CO2 Hydrogenation to Methanol on Pd Catalysts

The reaction mechanism of direct CO2 hydrogenation to methanol is investigated in detail on Pd (111), (100) and (110) surfaces using density functional theory (DFT), supporting investigations into emergent Pd-based catalysts. Hydrogen adsorption and surface mobility is firstly considered, with high-coordination surface sites having the largest adsorption energy and being connected by diffusion channels with low energy barriers. Surface chemisorption of CO2, forming a partially charged CO2δ-, is weakly endothermic on a Pd (111) whilst slightly exothermic on Pd (100) and (110), with adsorption energies of 0.09, -0.09 and -0.19 eV, respectively; the low stability of CO2δ- on the Pd (111) surface is attributed to negative charge accumulating on the surface Pd atoms that interacts directly with the CO2δ- adsorbate. Detailed consideration for sequential hydrogenation of the CO2 shows that HCOOH hydrogenation to H2COOH would be the rate determining step in the conversion to methanol, for all surfaces, with activation barriers of 1.41, 1.51, and 0.84 eV on Pd (111), (100) and (110) facets, respectively. The Pd (110) surface exhibits overall lower activation energies than the most studied Pd (111) and (100) surfaces, and therefore should be considered in more detail in future Pd catalytic studies