From the Sabatier principle to a predictive theory of transition-metal heterogeneous catalysis

Stabilization of Immobilized Enzymes via the Chaperone-Like Activity of Mixed Lipid Bilayers

Biomimetic lipid bilayers represent intriguing materials for enzyme immobilization, which is critical for many biotechnological applications. Here, through the creation of mixed lipid bilayers, the retention of immobilized enzyme structures and catalytic activity are dramatically enhanced. The enhancement in the retention of enzyme structures, which correlated with an increase in enzyme activity, is observed using dynamic single-molecule (SM) fluorescence methods. The results of SM analysis specifically show that lipid bilayers composed of mixtures of 1,2-dioleoyl- sn-glycero-3-phosphocholine (DOPC) and 1,2-dioleoyl- sn-glycero-3-phospho-(1'- rac-glycerol) (DOPG) stabilize the folded state of nitroreductase (NfsB), increasing the rate of refolding relative to unfolding of enzyme molecules on the bilayer surface. Remarkably, for optimal compositions with 15-50% DOPG, over 95% of NfsB remains folded while the activity of the enzyme is increased as much as 2 times over that in solution. Within this range of DOPG, the strength of the interaction of folded and unfolded NfsB with the bilayer surface was also significantly altered, which was evident by the change in the diffusion of folded and unfolded NfsB in the bilayer. Ultimately, these findings provide direct evidence for the chaperone-like activity of mixed DOPG/DOPC lipid bilayers, which can be controlled by tuning the fraction of DOPG in the bilayer.

Microkinetic Analysis and Scaling Relations for Catalyst Design

Microkinetic analysis plays an important role in catalyst design because it provides insight into the fundamental surface chemistry that controls catalyst performance. In this review, we summarize the development of microkinetic models and the inclusion of scaling relationships in these models. We discuss the importance of achieving stoichiometric and thermodynamic consistency in developing microkinetic models. We also outline how analysis of the maximum rates of elementary steps can be used to determine which transition states and adsorbed intermediates are kinetically significant, allowing the derivation of general reaction kinetics rate expressions in terms of changes in binding energies of the relevant transition states and intermediates. Through these analyses, we present how to predict optimal surface coverages and binding energies of adsorbed species, as well as the extent of potential rate improvement for a catalytic system. For systems in which the extent of potential rate improvement is small because of limitations imposed by scaling relations, different approaches, including the addition of promoters and formation of catalysts containing multiple functionalities, can be used to break the scaling relations and obtain further rate enhancement.

Distinct thermodynamic signatures of oligomer generation in the aggregation of the amyloid-β peptide

Mapping free-energy landscapes has proved to be a powerful tool for studying reaction mechanisms. Many complex biomolecular assembly processes, however, have remained challenging to access using this approach, including the aggregation of peptides and proteins into amyloid fibrils implicated in a range of disorders. Here, we generalize the strategy used to probe free-energy landscapes in protein folding to determine the activation energies and entropies that characterize each of the molecular steps in the aggregation of the amyloid-β peptide (Aβ42), which is associated with Alzheimer's disease. Our results reveal that interactions between monomeric Aβ42 and amyloid fibrils during fibril-dependent secondary nucleation fundamentally reverse the thermodynamic signature of this process relative to primary nucleation, even though both processes generate aggregates from soluble peptides. By mapping the energetic and entropic contributions along the reaction trajectories, we show that the catalytic efficiency of Aβ42 fibril surfaces results from the enthalpic stabilization of adsorbing peptides in conformations amenable to nucleation, resulting in a dramatic lowering of the activation energy for nucleation.

Heterogeneous Fe3 single-cluster catalyst for ammonia synthesis via an associative mechanism

The current industrial ammonia synthesis relies on Haber–Bosch process that is initiated by the dissociative mechanism, in which the adsorbed N₂ dissociates directly, and thus is limited by Brønsted–Evans–Polanyi (BEP) relation. Here we propose a new strategy that an anchored Fe₃ cluster on the θ-Al₂O₃(010) surface as a heterogeneous catalyst for ammonia synthesis from first-principles theoretical study and microkinetic analysis. We have studied the whole catalytic mechanism for conversion of N₂ to NH₃ on Fe₃/θ-Al₂O₃(010), and find that an associative mechanism, in which the adsorbed N₂ is first hydrogenated to NNH, dominates over the dissociative mechanism, which we attribute to the large spin polarization, low oxidation state of iron, and multi-step redox capability of Fe₃ cluster. The associative mechanism liberates the turnover frequency (TOF) for ammonia production from the limitation due to the BEP relation, and the calculated TOF on Fe₃/θ-Al₂O₃(010) is comparable to Ru B5 site.

The current industrial ammonia synthesis relies on the Haber-Bosch process that is limited by the Brønsted–Evans–Polanyi relation. Here, the authors propose a new strategy that an anchored Fe₃ on θ-Al₂O₃(010) surface serves as a heterogeneous single cluster catalyst for ammonia synthesis from first-principles calculations and microkinetic analysis.

Active learning with non-ab initio input features toward efficient CO2 reduction catalysts

In this work, we propose the use of the d-band width of the muffin-tin orbital theory (to account for the local coordination environment) plus electronegativity (to account for adsorbate renormalization) as a simple set of alternative descriptors for chemisorption which do not require ab initio calculations for large-scale first-hand screening.

In a conventional chemisorption model, the d-band center theory (augmented sometimes with the upper edge of the d-band for improved accuracy) plays a central role in predicting adsorption energies and catalytic activity as a function of the d-band center of solid surfaces, but it requires density functional calculations that can be quite costly for the purposes of large scale screening of materials. In this work, we propose to use the d-band width of the muffin-tin orbital theory (to account for the local coordination environment) plus electronegativity (to account for adsorbate renormalization) as a simple set of alternative descriptors for chemisorption which do not require ab initio calculations for large-scale first-hand screening. This pair of descriptors is then combined with machine learning methods, namely, neural network (NN) and kernel ridge regression (KRR). We show, for a toy set of 263 alloy systems, that the CO adsorption energy on the (100) facet can be predicted with a mean absolute deviation error of 0.05 eV. We achieved this high accuracy by utilizing an active learning algorithm, without which the accuracy was 0.18 eV. In addition, the results of testing the method with other facets such as (111) terrace and (211) step sites suggest that the present model is also capable of handling different coordination environments effectively. As an example of the practical application of this machine, we identified Cu₃Y@Cu* as an active and cost-effective electrochemical CO₂ reduction catalyst to produce CO with an overpotential ∼1 V lower than a Au catalyst.

Local structure and composition of PtRh nanoparticles produced through cathodic corrosion

Alloy nanoparticles fulfill an important role in catalysis. As such, producing them in a simple and clean way is much desired. A promising alloy nanoparticle production method is cathodic corrosion, which generates particles by applying an AC voltage to an alloy electrode. However, this harsh AC potential program might affect the final elemental distribution of the nanoparticles. In this work, we address this issue by characterizing the time that is required to create 1 μmol of Rh, Pt₁₂Rh₈₈, Pt₅₅Rh₄₅ and Pt nanoparticles under various applied potentials. The corrosion time measurements are complemented by structural characterization through transmission electron microscopy, X-ray diffraction and X-ray absorption spectroscopy. The corrosion times indicate that platinum and rhodium corrode at different rates and that the cathodic corrosion rates of the alloys are dominated by platinum. In addition, the structure-sensitive techniques reveal that the elemental distributions of the created alloy nanoparticles indeed exhibit small degrees of elemental segregation. These results indicate that the atomic alloy structure is not always preserved during cathodic corrosion.

Probing the geometry of copper and silver adatoms on magnetite: quantitative experiment versus theory

Accurately modelling the structure of a catalyst is a fundamental prerequisite for correctly predicting reaction pathways, but a lack of clear experimental benchmarks makes it difficult to determine the optimal theoretical approach. Here, we utilize the normal incidence X-ray standing wave (NIXSW) technique to precisely determine the three dimensional geometry of Ag1 and Cu1 adatoms on Fe3O4(001). Both adatoms occupy bulk-continuation cation sites, but with a markedly different height above the surface (0.43 ± 0.03 Å (Cu1) and 0.96 ± 0.03 Å (Ag1)). HSE-based calculations accurately predict the experimental geometry, but the more common PBE + U and PBEsol + U approaches perform poorly.

DFT calculation of oxygen adsorption on platinum nanoparticles: coverage and size effects

DFT calculations are used to simultaneously explore the effects of nanoparticle size and coverage for O adsorption on Pt nanoparticles.

Exploring perovskites for methane activation from first principles

The compositional diversity of the ABO₃ perovskites was explored to establish the correlation of key steps of methane activation with descriptors.

Nano-designed semiconductors for electro- and photoelectro-catalytic conversion of carbon dioxide

This review describes a systematic overview on rational design of semiconductor catalysts for electro- and photoelectro-chemical CO₂ conversion.

Scaling Relations for Acidity and Reactivity of Zeolites

Zeolites are widely applied as solid acid catalysts in various technological processes. In this work we have computationally investigated how catalytic reactivity scales with acidity for a range of zeolites with different topologies and chemical compositions. We found that straightforward correlations are limited to zeolites with the same topology. The adsorption energies of bases such as carbon monoxide (CO), acetonitrile (CH3CN), ammonia (NH3), trimethylamine (N(CH3)3), and pyridine (C5H5N) give the same trend of acid strength for FAU zeolites with varying composition. Crystal orbital Hamilton populations (COHP) analysis provides a detailed molecular orbital picture of adsorbed base molecules on the Brønsted acid sites (BAS). Bonding is dominated by strong σ donation from guest molecules to the BAS for the adsorbed CO and CH3CN complexes. An electronic descriptor of acid strength is constructed based on the bond order calculations, which is an intrinsic parameter rather than adsorption energy that contains additional contributions due to secondary effects such as van der Waals interactions with the zeolite walls. The bond order parameter derived for the CH3CN adsorption complex represents a useful descriptor for the intrinsic acid strength of FAU zeolites. For FAU zeolites the activation energy for the conversion of π-adsorbed isobutene into alkoxy species correlates well with the acid strength determined by the NH3 adsorption energies. Other zeolites such as MFI and CHA do not follow the scaling relations obtained for FAU; we ascribe this to the different van der Waals interactions and steric effects induced by zeolite framework topology.

Dependent Relationship between Quantitative Lattice Contraction and Enhanced Oxygen Reduction Activity over Pt-Cu Alloy Catalysts

Lattice contraction has been regarded as an important factor influencing oxygen reduction reaction (ORR) activity of Pt-based alloys. However, the relationship between quantitative lattice contraction and ORR activity has rarely been reported. Herein, using Pt-Cu alloy nanoparticles (NPs) with similar particle sizes but different compositions as examples, we investigated the relationship between quantitative lattice contraction and ORR activity by defining the shrinking percentage of Pt-Pt bond distance as lattice contraction percentage. The results show that the ORR activities of Pt-Cu alloy NPs exhibit a well-defined volcano-type dependent relationship toward the lattice contraction percentage. The dependent correlation can be explained by the Sabatier principle. This study not only proposes a valid descriptor to bridge the activity and atomic composition but also provides a reference for understanding the composition-structure-activity relationship of Pt-based alloys.

Surface Reaction Barriometry: Methane Dissociation on Flat and Stepped Transition-Metal Surfaces

Accurately simulating heterogeneously catalyzed reactions requires reliable barriers for molecules reacting at defects on metal surfaces, such as steps. However, first-principles methods capable of computing these barriers to chemical accuracy have yet to be demonstrated. We show that state-resolved molecular beam experiments combined with ab initio molecular dynamics using specific reaction parameter density functional theory (SRP-DFT) can determine the molecule-metal surface interaction with the required reliability. Crucially, SRP-DFT exhibits transferability: the functional devised for methane reacting on a flat (111) face of Pt (and Ni) also describes its reaction on stepped Pt(211) with chemical accuracy. Our approach can help bridge the materials gap between fundamental surface science studies on regular surfaces and heterogeneous catalysis in which defected surfaces are important.

Decorating of Ag and CuO on Cu Nanoparticles for Enhanced High Catalytic Activity to the Degradation of Organic Pollutants

Metal/semiconductor composites are promising catalysts with superior catalytic activity. In this work, a Cu/CuO-Ag composite with structure that consisted of Ag and CuO nanoparticles (NPs) decorated on the surface of Cu were fabricated via a facile in situ method. With characterization by transmission electron microscopy (TEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), energy dispersive X-ray spectroscopy (EDX), and inductively coupled plasma atomic emission spectrometry (ICP-AES), the structure and components of the Cu/CuO-Ag composite were well-defined. The Cu/CuO-Ag composite exhibited superior catalytic activities for the reduction of 4-nitrophenol (4-NP) in the presence of NaBH₄ with just a trace amount of Ag NPs (1.28 wt %). The reduction reaction is completed in 75 s with an apparent rate constant k_app of 4.60 × 10^-2 s^-1. The Cu/CuO-Ag composite also showed excellent durable catalytic stability, as no significant activity loss was detected in the consecutive five reaction runs. With the aid of the Sabatier principle and volcano plot, the opportune chemical adsorption energy of the reagent 4-NP on the Cu/CuO-Ag composite was inferred to be the key to its high reaction rate. The CuO NPs as a semiconductor with narrow band gap also could help the Cu/CuO-Ag composite to capture the electrons/hydride ions and increase opportunities for 4-NP to be reduced. Furthermore, the Cu/CuO-Ag composite exhibited outstanding activity on the oxidative degradation of methylene blue (MB). This work enriched the bimetal/semiconductor catalyst system and supplied new insight into the catalysis mechanism.

General Structure-Reactivity Relationship for Oxygen on Transition-Metal Oxides

Despite many recent developments in designing and screening catalysts for improved performance, transition-metal oxides continue to prove to be challenging due to the myriad inherent complexities of the systems and the possible morphologies that they can exhibit. Herein we propose a structural descriptor, the adjusted coordination number (ACN), which can generalize the reactivity of the oxygen sites over the many possible surface facets and defects of a transition-metal oxide. We demonstrate the strong correlation of this geometric descriptor with the electronic and energetic properties of the active sites on five facets of four transition-metal oxides. We then use the structural descriptor to predict C-H activation energies to show the great potential of using ACN for the prediction of catalytic performance. This study presents a first look into quantifying the relation between active site structure and reactivity of transition-metal-oxide catalysts.

Grand challenges for catalysis in the Science and Technology Roadmap on Catalysis for Europe: moving ahead for a sustainable future

This perspective discusses the general concepts that will guide future catalysis and related grand challenges based on the Science and Technology Roadmap on Catalysis for Europe prepared by the European Cluster on Catalysis.

Homogenous Electrocatalytic Oxygen Reduction Rates Correlate with Reaction Overpotential in Acidic Organic Solutions

Improved electrocatalysts for the oxygen reduction reaction (ORR) are critical for the advancement of fuel cell technologies. Herein, we report a series of 11 soluble iron porphyrin ORR electrocatalysts that possess turnover frequencies (TOFs) from 3 s^-1 to an unprecedented value of 2.2 × 10⁶ s^-1. These TOFs correlate with the ORR overpotential, which can be modulated by changing the E_1/2 of the catalyst using different ancillary ligands, by changing the solvent and solution acidity, and by changing the catalyst's protonation state. The overpotential is well-defined for these homogeneous electrocatalysts by the E_1/2 of the catalyst and the proton activity of the solution. This is the first such correlation for homogeneous ORR electrocatalysis, and it demonstrates that the remarkably fast TOFs are a consequence of high overpotential. The correlation with overpotential is surprising since the turnover limiting steps involve oxygen binding and protonation, as opposed to turnover limiting electron transfer commonly found in Tafel analysis of heterogeneous ORR materials. Computational studies show that the free energies for oxygen binding to the catalyst and for protonation of the superoxide complex are in general linearly related to the catalyst E_1/2, and that this is the origin of the overpotential correlations. This analysis thus provides detailed understanding of the ORR barriers. The best catalysts involve partial decoupling of the influence of the second coordination sphere from the properties of the metal center, which is suggested as new molecular design strategy to avoid the limitations of the traditional scaling relationships for these catalysts.

Surface Adsorption Energetics Studied with "Gold Standard" Wave-Function-Based Ab Initio Methods: Small-Molecule Binding to TiO2(110)

Coupled-cluster theory with single, double, and perturbative triple excitations (CCSD(T)) is widely considered to be the "gold standard" of ab initio quantum chemistry. Using the domain-based pair natural orbital local correlation concept (DLPNO-CCSD(T)), these calculations can be performed on systems with hundreds of atoms at an accuracy of ∼99.9% of the canonical CCSD(T) method. This allows for ab initio calculations providing reference adsorption energetics at solid surfaces with an accuracy approaching 1 kcal/mol. This is an invaluable asset, not least for the assessment of density functional theory (DFT) as the prevalent approach for large-scale production calculations in energy or catalysis applications. Here we use DLPNO-CCSD(T) with embedded cluster models to compute entire adsorbate potential energy surfaces for the binding of a set of prototypical closed-shell molecules (H₂O, NH₃, CH₄, CH₃OH, CO₂) to the rutile TiO₂(110) surface. The DLPNO-CCSD(T) calculations show excellent agreement with available experimental data, even for the "infamous" challenge of correctly predicting the CO₂ adsorption geometry. The numerical efficiency of the approach is within 1 order of magnitude of hybrid-level DFT calculations, hence blurring the borders between reference and production technique.

Analysis of reaction schemes using maximum rates of constituent steps

We show that the steady-state kinetics of a chemical reaction can be analyzed analytically in terms of proposed reaction schemes composed of series of steps with stoichiometric numbers equal to unity by calculating the maximum rates of the constituent steps, rmax,i, assuming that all of the remaining steps are quasi-equilibrated. Analytical expressions can be derived in terms of rmax,i to calculate degrees of rate control for each step to determine the extent to which each step controls the rate of the overall stoichiometric reaction. The values of rmax,i can be used to predict the rate of the overall stoichiometric reaction, making it possible to estimate the observed reaction kinetics. This approach can be used for catalytic reactions to identify transition states and adsorbed species that are important in controlling catalyst performance, such that detailed calculations using electronic structure calculations (e.g., density functional theory) can be carried out for these species, whereas more approximate methods (e.g., scaling relations) are used for the remaining species. This approach to assess the feasibility of proposed reaction schemes is exact for reaction schemes where the stoichiometric coefficients of the constituent steps are equal to unity and the most abundant adsorbed species are in quasi-equilibrium with the gas phase and can be used in an approximate manner to probe the performance of more general reaction schemes, followed by more detailed analyses using full microkinetic models to determine the surface coverages by adsorbed species and the degrees of rate control of the elementary steps.