Computational study of the adsorption of benzene and hydrogen on palladium–iridium nanoalloys

The Effect of the Iridium Alloying and Hydrogen Sorption on the Physicochemical and Electrochemical Properties of Palladium

Thin layers (up to 1 µm) of Pd-Ir alloys were electrodeposited from aqueous, galvanic baths of PdCl₂ and IrCl₃ mixtures. The morphology of the electrodeposits was examined by means of scanning electron microscopy. The composition of alloys was determined with the use of energy-dispersive spectroscopy, atomic absorption spectrometry, X-ray photoelectron spectroscopy, and Auger electron spectroscopy. For the studies of the electrochemical properties of alloys, cyclic voltammetry, chronoamperometry, and chronopotentiometry were used. It was found that Pd-Ir alloy electrodes were surface-enriched with Pd. Pd-Ir alloys subjected to different electrochemical treatment involving hydrogen sorption changed their surface state. The continuous hydrogen sorption enhanced the Ir ions' dissolution. The values of thermodynamic functions of hydrogen sorption in strong alkaline electrolytes were comparable with those in acidic electrolytes, whereas the kinetics of the process in alkaline medium was hindered. The miscibility gap in the Pd-Ir-H system vanished for the electrode containing ca. 93.7 at.% Pd.

Adsorption Sites on Pd Nanoparticles Unraveled by Machine-Learning Potential with Adaptive Sampling

Catalytic properties of noble-metal nanoparticles (NPs) are largely determined by their surface morphology. The latter is probed by surface-sensitive spectroscopic techniques in different spectra regions. A fast and precise computational approach enabling the prediction of surface-adsorbate interaction would help the reliable description and interpretation of experimental data. In this work, we applied Machine Learning (ML) algorithms for the task of adsorption-energy approximation for CO on Pd nanoclusters. Due to a high dependency of binding energy from the nature of the adsorbing site and its local coordination, we tested several structural descriptors for the ML algorithm, including mean Pd-C distances, coordination numbers (CN) and generalized coordination numbers (GCN), radial distribution functions (RDF), and angular distribution functions (ADF). To avoid overtraining and to probe the most relevant positions above the metal surface, we utilized the adaptive sampling methodology for guiding the ab initio Density Functional Theory (DFT) calculations. The support vector machines (SVM) and Extra Trees algorithms provided the best approximation quality and mean absolute error in energy prediction up to 0.12 eV. Based on the developed potential, we constructed an energy-surface 3D map for the whole Pd₅₅ nanocluster and extended it to new geometries, Pd₇₉, and Pd₈₅, not implemented in the training sample. The methodology can be easily extended to adsorption energies onto mono- and bimetallic NPs at an affordable computational cost and accuracy.

Hyper-Cross-Linked Polystyrene as a Stabilizing Medium for Small Metal Clusters

Among different polymers nanostructured cross-linked aromatics have the greatest potential as catalytic supports due to their exceptional thermal and chemical stability and preservation of the active phase morphology. This work studies the ability of hyper-cross-linked polystyrene (HPS) to stabilize small Pd_n and Pt_n (n = 4 or 9) clusters. Unrestricted DFT calculations were carried out for benzene (BZ) adsorption at the BP level of theory using triple-zeta basis sets. The adsorption of BZ rings (stepwise from one to four) was found to result in noticeable gain in energy and stabilization of resulting adsorption complexes. Moreover, the interaction of metal clusters with HPS micropores was also addressed. For the first time, the incorporation of small clusters in the HPS structure was shown to influences its geometry resulting in the stabilization of polymer due to its partial relaxation.

Palladium, Iridium, and Rhodium Supported Catalysts: Predictive H₂ Chemisorption by Statistical Cuboctahedron Clusters Model

Chemisorption of hydrogen on metallic particles is often used to estimate the metal dispersion (D), the metal particle size (d), and the metallic specific surface area (S_M), currently assuming a stoichiometry of one hydrogen atom H adsorbed per surface metal atom M. This assumption leads to a large error when estimating D, d, and S_M, and a rigorous method is needed to tackle this problem. A model describing the statistics of the metal surface atom and site distribution on perfect cuboctahedron clusters, already developed for Pt, is applied to Pd, Ir, and Rh, using the density functional theory (DFT) calculation of the literature to determine the most favorable adsorption sites for each metal. The model predicts the H/M values for each metal, in the range 0–1.08 for Pd, 0–2.77 for Ir, and 0–2.31 for Rh, depending on the particle size, clearly showing that the hypothesis of H/M = 1 is not always confirmed. A set of equations is then given for precisely calculating D, d, and S_M for each metal directly from the H chemisorption results determined experimentally, without any assumption about the H/M stoichiometry. This methodology provides a powerful tool for accurate determination of metal dispersion, metal particle size, and metallic specific surface area from chemisorption experiments.

How the hydrogen sorption properties of palladium are modified through interaction with iridium

The instability of hydrogen sorption properties in Ir (positive sorption energies) leads to the destabilization of hydrogen sorption in a Pd layer interacting with Ir.

DFT study of the structure, chemical ordering and molecular adsorption of Pd–Ir nanoalloys

The stability, chemical ordering and CO adsorption properties of Pd–Ir nanoalloys are studied at the DFT level.