Hydrogen Chemisorption on Pd-Doped Copper Clusters

Abstract

The structural evolution, electronic, and magnetic properties of Pd-doped Cu n (n = 1–12) clusters and the dissociative chemisorption of H2 on the lowest energy structures are investigated on the basis of density functional theory. The Pd impurity occupies the surface site and changes the geometry of the pure copper clusters; i.e., a transition from 2D to 3D occurs at n = 5. The relative stability and chemical activity of the minimum energy structures are analyzed through the binding energy per atom, second-order energy difference, and energy gap. The results show that the stability of Cu n Pd containing an even number of Cu atoms is higher than that of Cu n+1, which is related to the closed-shell electronic structure. The electronic properties such as the ionization potential and electron affinity are calculated for Cu n Pd and Cu n+1 and compared with experimental data available. By studying the H2 chemisorption on the Pd-doped Cu n clusters, the results show that the doped clusters possess similar interactions with atomic H compared to the pure ones. Interestingly, the hydrogen dissociation pathway on Cu n Pd clusters shows that certain clusters possess significantly lower activation barriers than the pure ones. For Cu4–6Pd, the energy barriers for the release of chemisorbed hydrogen range from 0.63 to 0.75 eV, while from 1.19 to 2.53 eV on Cu5–7, suggesting that the doped clusters are more suitable for H2 dissociation and utilization. These results can be understood in terms of the localization of the spin-polarization density.

Ab Initio Insights into the Formation Mechanisms of 55-Atom Pt-Based Core–Shell Nanoalloys

“…A wide range of initial trial configurations (core−shell, homogeneous, random, etc) was selected for the Pt n TM 55−n systems based on atom substitutions in the initial frames for n = {13; 42}. Those particular chemical compositions were selected because they favor the formation of perfect core−shell structures based on the icosahedron 55-atom model, for example, icosahedron Pt 13 Cu 42 (Pt 42 Cu 13 ) with 13 (42) Pt atoms in the core (surface) and 42 (13) Cu atoms in the surface (core). Beyond core−shell structures, we designed additional configurations based on various homogeneous and segregated chemical ordering distributions.…”

“…Additional notable examples are PtCo for hydrogenolysis, PtPd for oxygen reduction, as well as PtAu, PtRu, and PtRh for electro-oxidation and reforming . In general, an atom-level modeling based on quantum-chemistry of stable nanoalloys is crucial to understand these materials in a fundamental level, which allows their optimal utilization in technological applications. − …”

Extraction of Uncertain Parameters of Double-Diode Model of a Photovoltaic Panel Using Simulated Annealing Optimization

“…Simulated annealing (SA) is a random search technique for global optimization problems characterized by its robustness and efficiency. − It mimics the annealing process for metal cooling. The basic idea of the simulated annealing algorithm is to use the Markov chain for a random search, which not only accepts the best modifications that optimize the objective function but also retains some modifications that are not ideal with a probability p .…”

“…They do not need to satisfy the conditions of convexity or differentiability of objective functions. Because of these advantages, different metaheuristic methods have been applied to solve PV parameter estimation problems, , such as particle swarm optimization (PSO), simulated annealing algorithm (SA), − genetic algorithm (GA), pattern search (PS), biogeography based optimization (BBO), artificial bee colony (ABC), chaotic asexual reproduction (CAR), adaptive differential evolution (ADE), symbiotic organic search (SOS), improved shuffled complex evolution (ISCE), hybrid firefly algorithm and patter search (HFAPS), multiple learning backtracking search (MLBTS), firefly algorithm (FA), ant lion optimization (ALO), particle swarm optimization/adaptive mutation strategy (PSOAMS), improved cuckoo search algorithm (ImCSA), improved teaching learning based optimization (ITLBO), hybridizing cuckoo search/biogeography based optimization (BHCS), and three point based approach (TPBA) …”

“…It also works with low memory requirements and is very simple to implement. These characteristics motivated the use of SA in several optimization problems in engineering. − The proposed extraction procedure consists of three steps. First, the SA algorithm is used to extract the parameters without taking into account the uncertainty.…”