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Guided by computational Pourbaix screening and high-throughput experiments aimed at the development of precious-metal-free fuel cells, we investigate rutile CoSb2O6 as an electrocatalyst for oxygen reduction in 1 M sulfuric acid. Following 4 h of catalyst conditioning at 0.7 V vs RHE, operation at this potential for 20 h yielded an average current density of −0.17 mA cm-2 with corrosion at a rate of 0.04 nm hour-1 that is stoichiometric with catalyst composition. Surface Pourbaix analysis of the (111) surface identified partial H coverage under operating conditions. The Sb active site has an HO* binding free energy of 0.49 eV, which is near the peak of the kinetic 4e- ORR volcano for transition-metal oxides in acidic conditions. The experimental demonstration of operational stability and computational identification of a reaction pathway with favorable energetics place rutile CoSb2O6 among the most promising precious-metal-free electrocatalysts for oxygen reduction in acidic media.

Catalytic systems whose properties can be systematically tuned via changes in synthesis conditions are highly desirable for the next-generation catalyst design and optimization. Herein, we present a two-dimensional (2D) conductive metal-organic framework consisting of M-N4 units (M = Ni, Cu) and a hexaaminobenzene (HAB) linker as a catalyst for the oxygen reduction reaction. By varying synthetic conditions, we prepared two Ni-HAB catalysts with different crystallinities, resulting in catalytic systems with different electric conductivities, electrochemical activity, and stability. We show that crystallinity has a positive impact on conductivity and demonstrate that this improved crystallinity/conductivity improves the catalytic performance of our model system. Additionally, density functional theory simulations were performed to probe the origin of M-HAB's catalytic activity, and they suggest that M-HAB's organic linker acts as the active site with the role of the metal being to modulate the linker sites' binding strength.

The discovery of high-performing and stable materials for sustainable energy applications is a pressing goal in catalysis and materials science. Understanding the relationship between a material's structure and functionality is an important step in the process, such that viable polymorphs for a given chemical composition need to be identified. Machine-learning-based surrogate models have the potential to accelerate the search for polymorphs that target specific applications. Herein, we report a readily generalizable active-learning (AL) accelerated algorithm for identification of electrochemically stable iridium oxide polymorphs of IrO2 and IrO3. The search is coupled to a subsequent analysis of the electrochemical stability of the discovered structures for the acidic oxygen evolution reaction (OER). Structural candidates are generated by identifying all 956 structurally unique AB2 and AB3 prototypes in existing materials databases (more than 38000). Next, using an active learning approach, we find 196 IrO2 polymorphs within the thermodynamic amorphous synthesizability limit and reaffirm the global stability of the rutile structure. We find 75 synthesizable IrO3 polymorphs and report a previously unknown FeF3-type structure as the most stable, termed α-IrO3. To test the algorithms performance, we compare to a random search of the candidate space and report at least a 2-fold increase in the rate of discovery. Additionally, the AL approach can acquire the most stable polymorphs of IrO2 and IrO3 with fewer than 30 density functional theory optimizations. Analysis of the structural properties of the discovered polymorphs reveals that octahedral local coordination environments are preferred for nearly all low-energy structures. Subsequent Pourbaix Ir-H2O analysis shows that α-IrO3 is the globally stable solid phase under acidic OER conditions and supersedes the stability of rutile IrO2. Calculation of theoretical OER surface activities reveal ideal weaker binding of the OER intermediates on α-IrO3 than on any other considered iridium oxide. We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal oxide structure with a defined stoichiometry.

Multimetallic Ir-based systems offer significant opportunities for enhanced oxygen evolution electrocatalysis by modifying the electronic and geometric properties of the active catalyst. Herein, a systematic investigation of bimetallic Ir-based thin films was performed to identify activity and stability trends across material systems for the oxygen evolution reaction (OER) in acidic media. Electron beam evaporation was used to co-deposit metallic films of Ir, IrSn2, IrCr, IrTi, and IrNi. The electrocatalytic activity of the electrochemically oxidized alloys was found to increase in the following order: IrTi < IrSn2 < Ir ∼ IrNi < IrCr. The IrCr system demonstrates two times the catalytic activity of Ir at 1.65 V versus RHE. Density functional theory calculations suggest that this enhancement is due to Cr active sites that have improved oxygen binding energetics compared to those of pure Ir oxide. This work identifies IrCr as a promising new catalyst system that facilitates reduced precious metal loadings for acid-based OER catalysis.

Shifting electrochemical oxygen reduction towards 2e- pathway to hydrogen peroxide (H2O2), instead of the traditional 4e- to water, becomes increasingly important as a green method for H2O2 generation. Here, through a flexible control of oxygen reduction pathways on different transition metal single atom coordination in carbon nanotube, we discovered Fe-C-O as an efficient H2O2 catalyst, with an unprecedented onset of 0.822 V versus reversible hydrogen electrode in 0.1 M KOH to deliver 0.1 mA cm-2 H2O2 current, and a high H2O2 selectivity of above 95% in both alkaline and neutral pH. A wide range tuning of 2e-/4e- ORR pathways was achieved via different metal centers or neighboring metalloid coordination. Density functional theory calculations indicate that the Fe-C-O motifs, in a sharp contrast to the well-known Fe-C-N for 4e-, are responsible for the H2O2 pathway. This iron single atom catalyst demonstrated an effective water disinfection as a representative application.

A major challenge in the modeling of electrochemical phenomena is the accurate description of the interface between an electrolyte and a charged conductor. Polarizable continuum models (PCM) have been gaining popularity because they offer a computationally inexpensive method of modeling the electrolyte. In this Perspective, we discuss challenges from using one such model which treats the ions using a linearized Poisson-Boltzmann (LPB) distribution. From a physical perspective, this model places charge unphysically close to the surface and adsorbates, and it includes excessively steep ramping of the dielectric constant from the surface to the bulk solvent. Both of these issues can be somewhat mitigated by adjusting parameters built into the model, but in doing so, the resultant capacitance deviates from experimental values. Likewise, hybrid explicit-implicit approaches to the solvent may offer a more realistic description of hydrogen bonding and solvation to reaction intermediates, but the corresponding capacitances also deviate from experimental values. These deviations highlight the need for a careful adjustment of parameters in order to reproduce not only solvation energies but also other physical properties of solid-liquid interfaces. Continuum approaches alone also necessarily do not capture local variations in the electric field from cations at the interface, which can affect the energetics of intermediates with substantial dipoles or polarizability. Finally, since the double-layer charge can be varied continuously, LPB/PCM models provide a way to determine electrochemical barriers at constant potential. However, double-layer charging and the atomic motion associated with reaction events occur on significantly different timescales. We suggest that more detailed approaches, such as the modified Poisson-Boltzmann model and/or the addition of a Stern layer, may be able to mitigate some but not all of the challenges discussed.

Precise control of elemental configurations within multimetallic nanoparticles (NPs) could enable access to functional nanomaterials with significant performance benefits. This can be achieved down to the atomic level by the disorder-to-order transformation of individual NPs. Here, by systematically controlling the ordering degree, we show that the atomic ordering transformation, applied to AuCu NPs, activates them to perform as selective electrocatalysts for CO2 reduction. In contrast to the disordered alloy NP, which is catalytically active for hydrogen evolution, ordered AuCu NPs selectively converted CO2 to CO at faradaic efficiency reaching 80%. CO formation could be achieved with a reduction in overpotential of ∼200 mV, and catalytic turnover was enhanced by 3.2-fold. In comparison to those obtained with a pure gold catalyst, mass activities could be improved as well. Atomic-level structural investigations revealed three atomic gold layers over the intermetallic core to be sufficient for enhanced catalytic behavior, which is further supported by DFT analysis.

While the search for catalysts capable of directly converting methane to higher value commodity chemicals and liquid fuels has been active for over a century, a viable industrial process for selective methane activation has yet to be developed. Electronic structure calculations are playing an increasingly relevant role in this search, but large-scale materials screening efforts are hindered by computationally expensive transition state barrier calculations. The purpose of the present letter is twofold. First, we show that, for the wide range of catalysts that proceed via a radical intermediate, a unifying framework for predicting C-H activation barriers using a single universal descriptor can be established. Second, we combine this scaling approach with a thermodynamic analysis of active site formation to provide a map of methane activation rates. Our model successfully rationalizes the available empirical data and lays the foundation for future catalyst design strategies that transcend different catalyst classes.

This study presents simulations of temperature-programmed desorption (TPD) profiles using desorption energy data from density functional theory (DFT) calculations. We apply this method to investigate the desorption of oxygen (O2) from IrO2(110) to gain insight into the kinetics of oxygen coupling and desorption, important elementary steps in the oxygen evolution reaction (OER). Initially, we confirm the thermodynamically stable adsorption site for oxygen in the pristine IrO2(110) as IrCUS, even with a high oxygen coverage. We successfully simulate TPD for O2 desorption, achieving good agreement with experimental TPD data for different initial oxygen exposures when including more than one adsorption site. We identify a new adsorption site, related to the formation of steps on IrO2(110)(IrCUS-step-0.5), that is essential for reproducing the experimental TPD. Our findings suggest that the observed TPD peaks are the result of different adsorption sites on the surface, rather than solely a lateral interactions effect. This work provides insight into the behavior of oxygen adsorption on IrO2, with implications for understanding surface reactivity and catalytic processes involving this material. [ABSTRACT FROM AUTHOR]

Highly active catalysts for the oxygen evolution reaction (OER) are required for the development of photoelectrochemical devices that generate hydrogen efficiently from water using solar energy. Here, we identify the origin of a 500-fold OER activity enhancement that can be achieved with mixed (Ni,Fe)oxyhydroxides (Ni(1-x)Fe(x)OOH) over their pure Ni and Fe parent compounds, resulting in one of the most active currently known OER catalysts in alkaline electrolyte. Operando X-ray absorption spectroscopy (XAS) using high energy resolution fluorescence detection (HERFD) reveals that Fe(3+) in Ni(1-x)Fe(x)OOH occupies octahedral sites with unusually short Fe-O bond distances, induced by edge-sharing with surrounding [NiO6] octahedra. Using computational methods, we establish that this structural motif results in near optimal adsorption energies of OER intermediates and low overpotentials at Fe sites. By contrast, Ni sites in Ni(1-x)Fe(x)OOH are not active sites for the oxidation of water.

Introduction: This protocol describes a systematic review and meta‐analysis to evaluate the clinical effects of mixing short‐ and long‐acting local anaesthetics in peripheral nerve blocks. Clinicians often combine short‐ and long‐acting local anaesthetics to achieve a briefer onset time. However, this may come with a prize, namely a shorter total duration of the block, which is of clinical importance. Objective: This systematic review aims to strengthen the knowledge of the clinical effects associated with this practice. The primary outcome is the duration of block analgesia. Secondary outcomes are block onset time, sensory and motor block duration. Exploratory outcomes are postoperative pain scores, cumulative 24‐h opioid consumption and the prevalence of serious adverse events. Methods: We will conduct a meta‐analysis of the extracted data, and the risk of bias for each study will be evaluated. We will perform a Trial Sequential Analysis, subgroup, and sensitivity analyses and assess the overall risk of publication bias. Finally, we will evaluate the review using the GRADE principles. [ABSTRACT FROM AUTHOR]

Linear scaling relations have led to an understanding of trends in catalytic activity and selectivity of many reactions in heterogeneous and electro-catalysis. However, linear scaling between the chemisorption energies of any two small molecule adsorbates is not guaranteed. A prominent example is the lack of scaling between the chemisorption energies of carbon and oxygen on transition metal surfaces. In this work, we show that this lack of scaling originates from different re-normalized adsorbate valence energies of lower-lying oxygen vs higher-lying carbon. We develop a model for chemisorption of small molecule adsorbates within the d-band model by combining a modified form of the Newns–Anderson hybridization energy with an effective orthogonalization term. We develop a general descriptor to a priori determine if two adsorbates are likely to scale with each other. [ABSTRACT FROM AUTHOR]

The presence of layered cobalt oxides has been identified experimentally in Co-based anodes under oxygen-evolving conditions. In this work, we report the results of theoretical investigations of the relative stability of layered and spinel bulk phases of Co oxides, as well as the stability of selected surfaces as a function of applied potential and pH. We then study the oxygen evolution reaction (OER) on these surfaces and obtain activity trends at experimentally relevant electro-chemical conditions. Our calculated volume Pourbaix diagram shows that β-CoOOH is the active phase where the OER occurs in alkaline media. We calculate relative surface stabilities and adsorbate coverages of the most stable low-index surfaces of β-CoOOH: (0001), (0112), and (1014). We find that at low applied potentials, the (1014) surface is the most stable, while the (0112) surface is the more stable at higher potentials. Next, we compare the theoretical overpotentials for all three surfaces and find that the (1014) surface is the most active one as characterized by an overpotential of η = 0.48 V. The high activity of the (1014) surface can be attributed to the observation that the resting state of Co in the active site is Co(3+) during the OER, whereas Co is in the Co(4+) state in the less active surfaces. Lastly, we demonstrate that the overpotential of the (1014) surface can be lowered further by surface substitution of Co by Ni. This finding could explain the experimentally observed enhancement in the OER activity of Ni(y)Co(1-y)O(x) thin films with increasing Ni content. All energetics in this work were obtained from density functional theory using the Hubbard-U correction.