No Arabic abstract
A range of chemical reactions occurring on the surfaces of metal nanoparticles exhibit enhanced rates under plasmonic excitation. Recent analyses based on Arrhenius law fitting have argued in favor of a purely photothermal mechanism of enhancement and suggested the lack of an involvement of hot electrons. However, there is a caveat as shown here: under certain scenarios, it is practically impossible to distinguish between a photochemical (non-thermal) effect of plasmonic excitation and a purely photothermal one using a phenomenological Arrhenius fitting of the reaction rates alone.
Electrostatic reaction inhibition in heterogeneous catalysis emerges if charged reactants and products are adsorbed on the catalyst and thus repel the approaching reactants. In this work, we study the effects of electrostatic inhibition on the reaction rate of unimolecular reactions catalyzed on the surface of a spherical model nanoparticle by using particle-based reaction-diffusion simulations. Moreover, we derive closed rate equations based on approximate Debye-Smoluchowski rate theory, valid for diffusion-controlled reactions, and a modified Langmuir adsorption isotherm, relevant for reaction-controlled reactions, to account for electrostatic inhibition in the Debye-Huckel limit. We study the kinetics of reactions ranging from low to high adsorptions on the nanoparticle surface and from the surface- to diffusion-controlled limits for charge valencies 1 and 2. In the diffusion-controlled limit, electrostatic inhibition drastically slows down the reactions for strong adsorption and low ionic concentration, which is well described by our theory. In particular, the rate decreases with adsorption affinity, because in this case the inhibiting products are generated at high rate. In the (slow) reaction-controlled limit, the effect of electrostatic inhibition is much weaker, as semi-quantitatively reproduced by our electrostatic-modified Langmuir theory. We finally propose and verify a simple interpolation formula that describes electrostatic inhibition for all reaction speeds (`diffusion-influenced reactions) in general.
The quantum computation of electronic energies can break the curse of dimensionality that plagues many-particle quantum mechanics. It is for this reason that a universal quantum computer has the potential to fundamentally change computational chemistry and materials science, areas in which strong electron correlations present severe hurdles for traditional electronic structure methods. Here, we present a state-of-the-art analysis of accurate energy measurements on a quantum computer for computational catalysis, using improved quantum algorithms with more than an order of magnitude improvement over the best previous algorithms. As a prototypical example of local catalytic chemical reactivity we consider the case of a ruthenium catalyst that can bind, activate, and transform carbon dioxide to the high-value chemical methanol. We aim at accurate resource estimates for the quantum computing steps required for assessing the electronic energy of key intermediates and transition states of its catalytic cycle. In particular, we present new quantum algorithms for double-factorized representations of the four-index integrals that can significantly reduce the computational cost over previous algorithms, and we discuss the challenges of increasing active space sizes to accurately deal with dynamical correlations. We address the requirements for future quantum hardware in order to make a universal quantum computer a successful and reliable tool for quantum computing enhanced computational materials science and chemistry, and identify open questions for further research.
The determination of the fundamental parameters of the Standard Model (and its extensions) is often limited by the presence of statistical and theoretical uncertainties. We present several models for the latter uncertainties (random, nuisance, external) in the frequentist framework, and we derive the corresponding $p$-values. In the case of the nuisance approach where theoretical uncertainties are modeled as biases, we highlight the important, but arbitrary, issue of the range of variation chosen for the bias parameters. We introduce the concept of adaptive $p$-value, which is obtained by adjusting the range of variation for the bias according to the significance considered, and which allows us to tackle metrology and exclusion tests with a single and well-defined unified tool, which exhibits interesting frequentist properties. We discuss how the determination of fundamental parameters is impacted by the model chosen for theoretical uncertainties, illustrating several issues with examples from quark flavour physics.
Methanol occupies a central role in chemical synthesis and is considered an ideal candidate for cleaner fuel storage and transportation. It can be catalyzed from water and volatile organic compounds such as carbon dioxide, thereby offering an attractive solution for reducing carbon emissions. However molecular-level experimental observations of the catalytic process are scarce, and most existing catalysts tend to rely on empirically optimized, expensive and complex nano- composite materials. This lack of molecular-level insights has precluded the development of simpler, more cost-effective alternatives. Here we show that graphite immersed in ultrapure water is able to spontaneously catalyze methanol from volatile organic compounds in ambient conditions. Using single-molecule resolution atomic force microscopy (AFM) in liquid, we directly observe the formation and evolution of methanol-water nanostructures at the surface of graphite. These molecularly ordered structures nucleate near catalytically active surface features such as atomic step edges and grow progressively as further methanol is being catalyzed. Complementary nuclear magnetic resonance analysis of the liquid confirms the formation of methanol and quantifies its concentration. We also show that electric fields significantly enhance the catalysis rate, even when as small as that induced by the natural surface potential of the silicon AFM tip. These findings could have a significant impact on the development of organic catalysts and on the function of nanoscale carbon devices.
The rational design of hydrogen evolution reaction (HER) electrocatalysts which are competitive with platinum is an outstanding challenge to make power-to-gas technologies economically viable. Here, we introduce the delafossites PdCrO$_2$, PdCoO$_2$ and PtCoO$_2$ as a new family of electrocatalysts for the HER in acidic media. We show that in PdCoO$_2$ the inherently strained Pd metal sublattice acts as a pseudomorphic template for the growth of a strained (by +2.3%) Pd rich capping layer under reductive conditions. The surface modification continuously improves the electrocatalytic activity by simultaneously increasing the exchange current density j$_0$ from 2 to 5 mA/cm$^2_{geo}$ and by reducing the Tafel slope down to 38 mV/decade, leading to overpotentials $eta_{10}$ < 15 mV for 10 mA/cm$^2_{geo}$, superior to bulk platinum. The greatly improved activity is attributed to the in-situ stabilization of a $beta$-palladium hydride phase with drastically enhanced surface catalytic properties with respect to pure or nanostructured palladium. These findings illustrate how operando induced electrodissolution can be used as a top-down design concept for rational surface and property engineering through the strain-stabilized formation of catalytically active phases.