Philippe Sautet

Electronic structure and photocatalytic activity of wurtzite Cu–Ga–S nanocrystals and their Zn substitution

Stoichiometric and gallium-rich wurtzite Cu–Ga–S ternary nanocrystals were synthesized via a facile solution-based hot injection method using 1-dodecanethiol as a sulfur source. The use of 1-dodecanethiol was found to be essential not only as a sulfur source but also as a structure-directing reagent to form a metastable wurtzite structure. In addition, the substitution of zinc in the wurtzite gallium-rich Cu–Ga–S nanocrystals was also investigated. The obtained nanocrystals were characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), diffuse reflectance spectroscopy (DRS), photoluminescence (PL), and inductively coupled plasma atomic emission spectroscopy (ICP-OES). Electronic structures of pristine and the Zn-substituted Cu–Ga–S system were investigated using density functional theory (DFT) with HSE06 exchange-correlation functional. The calculated bandgaps accurately reflect the measured ones. The allowed electronic transitions occur upon the photon absorption from the (Cu + S) band towards the (Ga + S) one. The Zn substitution was found not to contribute to the band edge structure and hence altered the bandgaps only slightly, the direct transition nature remaining unchanged with the Zn substitution. The photocatalytic activities of H2 evolution from an aqueous Na2S/Na2SO3 solution under visible-light illumination on the synthesized nanocrystals were investigated. While the stoichiometric CuGaS2 exhibited negligible activity, the gallium-rich Cu–Ga–S ternary nanocrystals displayed reasonable activity. The optimum Zn substitution in the gallium-rich Cu–Ga–S ternary nanocrystals enhanced the H2 evolution rate, achieving an apparent quantum efficiency of >6% at 400 nm.

Solvation free energies for periodic surfaces: comparison of implicit and explicit solvation models

The evaluation of solvation energies is a great challenge. We focus here on an organic molecule chemisorbed at a metal-liquid interface, as a prototypical system, essential in tribology, electrochemistry and heterogeneous catalysis. We compare an established implicit solvation scheme with a strategy based on molecular mechanics (MM) free energy perturbation (FEP) seeded by QM computations. First, we benchmark the approaches against experimental hydration energies of standard (organic) molecules and find acceptable errors in the order of 0.06 eV (1.3 kcal mol-1). Then, the impact of various parameters on the solvation energy of an adsorbate have been assessed on a typical system of interest, levulinic acid adsorbed at a Ru(0001)/water interface. We identify the need for dipole corrections or symmetric slabs when including solvation effects on metallic surfaces. The MM-FEP scheme is revealed to be as reliable as the implicit solvent for water. In the case of levulinic acid, both PCM and MM-FEP agree that the bulk solvation effect is not sufficient to change the adsorption mode from bidentate to mono-dentate, despite the fact that the COOH group is desolvated in the bidentate case. MM-FEP has the great advantage of being more easily generalized to other solvents and to be further improved which will be particularly useful to study solvent and (counter-)ion effects on interfacial reactions.

Towards more accurate prediction of activation energies for polyalcohol dehydrogenation on transition metal catalysts in water

To sustain the development of novel metal-supported catalysts for transformation of polyalcohol molecules in silico, the development of efficient and accurate linear relationships to predict activation energies represents a major milestone. We establish here Bronsted–Evans–Polanyi relationships for CH and OH cleavages in monoalcohol molecules H-bonded with a chemisorbed water molecule over a series of transition metal surfaces. Then, we demonstrate that these relations can be used to predict glycerol and 1,2-propanediol dehydrogenation over Rh with remarkable accuracy and almost no systematic deviation. Lastly, we discuss the conditions to use these relations safely for screening dense catalytic reaction networks on different metallic surfaces.

Metastable Structures in Cluster Catalysis from First-Principles: Structural Ensemble in Reaction Conditions and Metastability Triggered Reactivity

Reactivity studies on catalytic transition metal clusters are usually performed on a single global minimum structure. With the example of a Pt13 cluster under a pressure of hydrogen, we show from first-principle calculations that low energy metastable structures of the cluster can play a major role for catalytic reactivity and that hence consideration of the global minimum structure alone can severely underestimate the activity. The catalyst is fluxional with an ensemble of metastable structures energetically accessible at reaction conditions. A modified genetic algorithm is proposed to comprehensively search for the low energy metastable ensemble (LEME) structures instead of merely the global minimum structure. In order to reduce the computational cost of density functional calculations, a high dimensional neural network potential is employed to accelerate the exploration. The presence and influence of LEME structures during catalysis is discussed by the example of H covered Pt13 clusters for two reactions of major importance: hydrogen evolution reaction and methane activation. The results demonstrate that although the number of accessible metastable structures is reduced under reaction condition for Pt13 clusters, these metastable structures can exhibit high activity and dominate the observed activity due to their unique electronic or structural properties. This underlines the necessity of thoroughly exploring the LEME structures in catalysis simulations. The approach enables one to systematically address the impact of isomers in catalysis studies, taking into account the high adsorbate coverage induced by reaction conditions.

A machine learning approach to graph-theoretical cluster expansions of the energy of adsorbate layers

The accurate description of the energy of adsorbate layers is crucial for the understanding of chemistry at interfaces. For heterogeneous catalysis, not only the interaction of the adsorbate with the surface but also the adsorbate-adsorbate lateral interactions significantly affect the activation energies of reactions. Modeling the interactions of the adsorbates with the catalyst surface and with each other can be efficiently achieved in the cluster expansion Hamiltonian formalism, which has recently been implemented in a graph-theoretical kinetic Monte Carlo (kMC) scheme to describe multi-dentate species. Automating the development of the cluster expansion Hamiltonians for catalytic systems is challenging and requires the mapping of adsorbate configurations for extended adsorbates onto a graphical lattice. The current work adopts machine learning methods to reach this goal. Clusters are automatically detected based on formalized, but intuitive chemical concepts. The corresponding energy coefficients for the cluster expansion are calculated by an inversion scheme. The potential of this method is demonstrated for the example of ethylene adsorption on Pd(111), for which we propose several expansions, depending on the graphical lattice. It turns out that for this system, the best description is obtained as a combination of single molecule patterns and a few coupling terms accounting for lateral interactions.

Capturing Solvation Effects at a Liquid/Nanoparticle Interface by Ab Initio Molecular Dynamics: Pt201 Immersed in Water.

Solvation can substantially modify the adsorption properties of heterogeneous catalysts. Although essential for achieving realistic theoretical models, assessing such solvent effects over nanoparticles is challenging from a computational standpoint due to the complexity of those liquid/metal interfaces. This effect is investigated by ab initio molecular dynamics simulations at 350 K of a large platinum nanoparticle immersed in liquid water. The first solvation layer contains twice as much physisorbed water molecules above the terraces, than chemisorbed ones located only at edges and corners. The solvent stabilizes the binding energy of chemisorbates: 66% of the total gain comes from interactions with physisorbed molecules and 34% from the influence of bulk liquid.

Direct Amination of Alcohols Catalyzed by Aluminum Triflate: An Experimental and Computational Study

Among the best-performing homogeneous catalysts for the direct amination of activated secondary alcohols with electron-poor amine derivatives, metal triflates, such as aluminum triflate, Al(OTf)3 , stand out. Herein we report the extension of this reaction to electron-rich amines and activated primary alcohols. We provide detailed insight into the structure and reactivity of the catalyst under working conditions in both nitromethane and toluene solvent, through experiment (cyclic voltammetry, conductimetry, NMR spectroscopy), and density functional theory (DFT) simulations. Competition between aniline and benzyl alcohol for Al in the two solvents explains the different reactivities. The catalyst structures predicted from the DFT calculations were validated by the experiments. Whereas a SN 1-type mechanism was found to be active in nitromethane, we propose a SN 2 mechanism in toluene to rationalize the much higher selectivity observed when using this solvent. Also, unlike what is commonly assumed in homogeneous catalysis, we show that different active species may be active instead of only one.

Supported gold–nickel nano-alloy as a highly efficient catalyst in levulinic acid hydrogenation with formic acid as an internal hydrogen source

Gamma-valerolactone (GVL) is one of the key products of future biorefineries. We show here for the first time the superior activity of Ni-based, Au doped catalysts in levulinic acid hydrogenation towards GVL using formic acid as a hydrogen source. Their performances are strongly influenced by the preparation method, and the highest GVL yield is achieved for bimetallic Au–Ni catalysts prepared via co-impregnation of both metallic salts with a reductive thermal treatment under hydrogen. The very high catalytic activity is explained by the use of DFT calculations and the extensive characterization of the catalyst surface and bulk properties. We highlight the pivotal role played by the incorporated isolated metallic Ni atoms within Au nanoparticles. The nano-alloy composition is determined. It allows establishment of a surface model of such an alloy, thanks to which the high activity can be explained by the presence of an optimum energetic span of FA adsorption. The existence of strong interaction between Au and Ni in a surface alloy, Au–Ni, favors selective and fast decomposition of formic acid into hydrogen that consequently facilitates strongly the combined hydrogenation process.