Subnano Pt Particles from a First-Principles Stochastic Surface Walking Global Search

The Reactivity of Ptn + Clusters With N2O Facilitated by Dual Lewis-Acid Sites

The size dependence of metal cluster reactions frequently reveals valuable information on the mechanism of nanometal catalysis. Here, the reactivity of the Ptn + (n = 1-40) clusters with N2O is studied and a significant dependence on the size of these clusters is noticed. Interestingly, the small Ptn + clusters like Pt3 + and Pt4 + are inclined to form N2O complexes; some larger clusters, such as Pt19 +, Pt21 +, and Pt23 +, appear to be unreactive; however, the others such as Pt3 , 9,15 + and Pt18 + are capable of decomposing N2O. While Pt9 + rapidly reacts with N2O to form a stable quasitetrahedron Pt9O+ product, Pt18 + experiences a series of N2O decompositions to produce Pt18O1-7 +. Utilizing high-precision theoretical calculations, it is shown how the atomic structures and active sites of Ptn + clusters play a vital role in determining their reactivity. Cooperative dual Lewis-acid sites (CDLAS) can be achieved on specific metal clusters like Pt18 +, rendering accelerated N2O decomposition via both N- and O-bonding on the neighboring Pt atoms. The influence of CDLAS on the size-dependent reaction of Pt clusters with N2O is illustrated, offering insights into cluster catalysis in reactions that include the donation of electron pairs.

Entropy in catalyst dynamics under confinement

Entropy during the dynamic structural evolution of catalysts has a non-trivial influence on chemical reactions. Confinement significantly affects the catalyst dynamics and thus impacts the reactivity. However, a full understanding has not been clearly established. To investigate catalyst dynamics under confinement, we utilize the active learning scheme to effectively train machine learning potentials for computing free energies of catalytic reactions. The scheme enables us to compute the reaction free energies and entropies of O2 dissociation on Pt clusters with different sizes confined inside a carbon nanotube (CNT) at the timescale of tens of nanoseconds while keeping ab initio accuracy. We observe an entropic effect owing to liquid-to-solid phase transitions of clusters at finite temperatures. More importantly, the confinement effect enhances the structural dynamics of the cluster and leads to a lower melting temperature than those of the bare cluster and cluster outside the CNT, consequently facilitating the reaction to occur at lower temperatures and preventing the catalyst from forming unfavorable oxides. Our work reveals the important influence of confinement on structural dynamics, providing useful insight into entropy in dynamic catalysis.

New global minimum conformers for the Pt 19 and Pt 20 clusters: low symmetric species featuring different active sites

CONTEXT

The study of platinum (Pt) clusters and nanoparticles is essential due to their extensive range of potential technological applications, particularly in catalysis. The electronic properties that yield optimal catalytic performance at the nanoscale are significantly influenced by the size and structure of Pt clusters. This research aimed to identify the lowest-energy conformers for Pt18 , Pt19 , and Pt20 species using Density Functional Theory (DFT). We discovered new low-symmetry conformers for Pt19 and Pt20 , which are 3.0 and 1.0 kcal/mol more stable, respectively, than previously reported structures. Our study highlights the importance of using density functional approximations that incorporate moderate levels of exact Hartree-Fock exchange, alongside basis sets of at least quadruple-zeta quality. The resulting structures are asymmetric with varying active sites, as evidenced by sigma hole analysis on the electrostatic potential surface. This suggests a potential correlation between electronic structure and catalytic properties, warranting further investigation.

METHODS

An equivariant graph neural network interatomic potential (NequIP) within the Atomic Simulation Environment suite (ASE) was used to provide initial geometries of the aggregates under study. DFT calculations were performed with the ORCA 5 package, using functional approximations that included Generalized Gradient Approximation (PBE), meta-GGA (TPSS, M06-L), hybrid (PBE0, PBEh), meta-GGA hybrid (TPSSh), and range-separated hybrid ( ω B97x) functionals. Def2-TZVP and Def2-QZVP as well as members of the cc-pwCVXZ-PP family to check basis set convergence were used. QTAIM calculations were performed using the AIMAll suite. Structures were visualized with the AVOGADRO code.

C2 Product Formation over the C1 Product and HER on the 111 Plane of Specific Cu Alloy Nanoparticles Identified through Multiparameter Optimization

C2 products are more desirable than C1 products during CO2 electroreduction (CO2ER) because the former possess higher energy density and greater industrial value. For CO2ER, Cu is a well-known catalyst, but the selectivity toward C2 products is still a big challenge for researchers due to complex intermediates, different final products, and large space of the catalyst due to its morphology, plane, size, host surface etc. Using density functional theory (DFT) calculations, we find that alloying of Cu nanoparticles can help to enhance the selectivity toward C2 products during CO2ER with a low overpotential. By a systematic investigation of 111 planes (which prefer the C1 product in the case of bulk Cu), the alloys show the generation of C2 products via *CO-*CO dimerization (* indicates adsorbed state). It also suppresses the counter-pathway of hydrogenation of *CO to *CHO, which leads to C1 products. Further, we find that *CH2CHO is the bifurcating intermediate to distinguish between ethanol and ethylene as the final product. We have used simple graphical construction to identify the catalyst for CO2ER over HER, and vice versa. We have also defined the case of hydrogen poisoning and projected a parity plot to recognize the catalyst for C2 product evolution over the C1 product. Our study reveals that Cu-Ag and Cu-Zn catalysts selectively promote ethanol production on 111 planes. Moreover, an edge-doped 2SO2 graphene nanoribbon as the host layer further lowers the barrier and selectively promotes ethanol on Cu38- and Cu79-based alloys. This work provides new theoretical insights into designing Cu-based nanoalloy catalysts for C2 product formation on the 111 plane.

On the structure and electronic properties of Ptn clusters: new most stable structures for n = 16-17

The lowest energy structures and electronic properties of Ptn clusters up to n = 17 are investigated by using a genetic algorithm in combination with density functional theory calculations. There are several putative global minimum structures for platinum clusters which have been reported by using different approaches, but a comprehensive study for n = 15-17 has not been carried out so far. Herein, we perform a consensus using GGA (PBE), meta-GGA (TPSS) and hybrid (B3PW91, PBE0, PBEh-3c, M06-L) functionals in conjunction with the Def2-TZVP basis set. New most stable structures are found for Pt16 and Pt17, which are slightly lower in energy than the previously reported global minima. Molecular dynamics simulations show that the clusters are rigid at room temperature. We analyze the structural, electronic, energy and vibrational data of the investigated clusters in detail.

Rh19-: A high-spin super-octahedron cluster

Probing atomic clusters with magic numbers is of supreme importance but challenging in cluster science. Pronounced stability of a metal cluster often arises from coincident geometric and electronic shell closures. However, transition metal clusters do not simply abide by this constraint. Here, we report the finding of a magic-number cluster Rh19- with prominent inertness in the sufficient gas-collision reactions. Photoelectron spectroscopy experiments and global-minimum structure search have determined the geometry of Rh19- to be a regular Oh‑[Rh@Rh12@Rh6]- with unusual high-spin electronic configuration. The distinct stability of such a strongly magnetic cluster Rh19- consisting of a nonmagnetic element is fully unveiled on the basis of its unique bonding nature and superatomic states. The 1-nanometer-sized Oh-Rh19- cluster corresponds to a fragment of the face-centered cubic lattice of bulk rhodium but with altered magnetism and electronic property. This cluster features exceptional electron-spin state isomers confirmed in photoelectron spectra and suggests potential applications in atomically precise manufacturing involving spintronics and quantum computing.

Enhancing the Performance of Global Optimization of Platinum Cluster Structures by Transfer Learning in a Deep Neural Network

The global optimization of metal cluster structures is an important research field. The traditional deep neural network (T-DNN) global optimization method is a good way to find out the global minimum (GM) of metal cluster structures, but a large number of samples are required. We developed a new global optimization method which is the combination of the DNN and transfer learning (DNN-TL). The DNN-TL method transfers the DNN parameters of the small-sized cluster to the DNN of the large-sized cluster to greatly reduce the number of samples. For the global optimization of Pt₉ and Pt₁₃ clusters in this research, the T-DNN method requires about 3-10 times more samples than the DNN-TL method, and the DNN-TL method saves about 70-80% of time. We also found that the average amplitude of parameter changes in the T-DNN training is about 2 times larger than that in the DNN-TL training, which rationalizes the effectiveness of transfer learning. The average fitting errors of the DNN trained by the DNN-TL method can be even smaller than those by the T-DNN method because of the reliability of transfer learning. Finally, we successfully obtained the GM structures of Pt_n (n = 8-14) clusters by the DNN-TL method.

Homogeneous Distribution of Pt16(C4O4SH5)26 Clusters in ZIF-67 for Efficient Hydrogen Generation and Oxygen Reduction

In recent years, based on the high catalytic activities of metal nanoclusters (MNCs) and the unique porous structure of metal-organic frameworks (MOFs), much work has focused on MOF-confined small MNCs for catalysis applications. However, the commonly used "ship-in-boat" approach is unfeasible for precisely controlling the size and composition of the formed MNCs and meanwhile often causes structural distortion/degradation. On the other hand, the "bottle-around-ship" method usually has the disadvantages that MOFs show uncontrollable self-nucleation outside the MNCs and the stabilizers on the surface of MNCs may greatly reduce their catalytic activities. In this work, monodispersed Pt₁₆(C₄O₄SH₅)₂₆ clusters (Pt₁₆(MSA)₂₆) were first prepared and used as a precursor for the synthesis of Pt(MSA)@ZIF-67 via the typical Co-carboxylate type of linkage at the interface under ambient atmosphere. After encapsulating the Pt clusters in ZIF-67, the protecting ligands were removed under 300 °C to get surface-clean Pt₁₆ clusters confined in ZIF-67 (Pt@ZIF-67). The obtained Pt@ZIF-67 exhibited high catalytic activity for the hydrolysis of ammonia borane that was superior to that of most of the reported noble-metal catalysts. Meanwhile, by annealing the Pt(MSA)@ZIF-67 at 800 °C to form highly conductive graphitic carbon-coated Pt NCs and Co nanoparticles (NPs) (Pt/Co@NC), the obtained composite showed high catalytic activity for the oxygen reduction reaction (ORR). The formed Pt/Co@NC showed 9.6 times higher ORR mass activity (at 0.8 V) than Pt/C. This work provides a strategy to fabricate highly dispersed and stable metal clusters confined in the porous matrix for catalysis and shows that highly porous MOFs have promising catalysis applications by combining them with other active components.

Tetrahedral Pt10- Cluster with Unique Beta Aromaticity and Superatomic Feature in Mimicking Methane

Utilizing a customized metal cluster source in tandem with a flow tube reactor and a reflectron time-of-flight mass spectrometer, we have obtained well-resolved pure metal clusters Pt_n^- and observed their gas-phase reactions with a few small gas molecules. Interestingly, the remarkable inertness of Pt₁₀^- was repeatedly observed in different reactions. Meanwhile, we have determined the structure of Pt₁₀^- within a regular tetrahedron. Considering that Pt possesses 5d⁹6s¹ electron configuration, the tetrahedral Pt₁₀^- exhibits unexpected stability at neither a magic number of valence electrons nor a shell closure of geometric structure. Comprehensive theoretical calculations unveil the stability of Pt₁₀^- is significantly associated with the all-metal aromaticity. In addition to the classical total aromaticity, which is mainly due to 6s electrons, there is unique beta-aromaticity ascribed to spin-polarized beta 5d electrons pertaining to singly occupied multicenter bonds. Further, we demonstrate the superatomic feature of such a transition metal cluster Pt₁₀^-, as Pt₆@Pt₄^-, in mimicking methane.

From platinum atoms in molecules to colloidal nanoparticles: A review on reduction, nucleation and growth mechanisms

Platinum (Pt) is one of the most studied materials in catalysis today and considered for a wide range of applications: chemical synthesis, energy conversion, air treatment, water purification, sensing, medicine etc. As a limited and non-renewable resource, optimized used of Pt is key. Nanomaterial design offers multiple opportunities to make the most of Pt resources down to the atomic scale. In particular, colloidal syntheses of Pt nanoparticles are well documented and simple to implement, which accounts for the large interest in research and development. For further breakthroughs in the design of Pt nanomaterials, a deeper understanding of the intricate synthesis-structures-properties relations of Pt nanoparticles must be obtained. Understanding how Pt nanoparticles form from molecular precursors is both a challenging and rewarding area of investigation. It is directly relevant to develop improved Pt nanomaterials but is also a source of inspiration to design other precious metal nanostructures. Here, we review the current understanding of Pt nanoparticle formation. This review is aimed at readers with interest in Pt nanoparticles in general and their colloidal syntheses in particular. Readers with a strongest interest on the study of nanomaterial formation will find here the case study of Pt. The preferred model systems and characterization techniques used to perform the study of Pt nanoparticle syntheses are discussed. In light of recent achievements, further direction and areas of research are proposed.

Structure and Stability of the Ionic Liquid Clusters [EMIM]n[BF4]n+1- (n = 1-9): Implications for Electrochemical Separations

Precise functionalization of electrodes with size-selected ionic liquid (IL) clusters may improve the application of ILs in electrochemical separations. Herein we report our combined experimental and theoretical investigation of the IL clusters 1-ethyl-3-methylimidazolium tetrafluoroborate [EMIM]_n[BF₄]_n+1^- (n = 1-9) and demonstrate their selectivity and efficiency toward targeted adsorption of ions from solution. The structures and energies of the IL clusters, predicted with global optimization, agree with and help interpret the ion abundances and stabilities measured by high-mass-resolution electrospray ionization mass spectrometry and collision-induced dissociation experiments. The [EMIM][BF₄]₂^- cluster, which was identified as the most stable IL cluster, was selectively soft-landed onto a working electrode. Electrochemical impedance spectroscopy revealed a lower charge transfer resistance on the soft-landed electrode containing [EMIM][BF₄]₂^- compared with an electrode prepared by drop-casting of an IL solution containing the full range of IL clusters. Our findings indicate that specific IL clusters may be used to increase the efficiency of electrochemical separations by lowering the overpotentials involved.

NWPEsSe: An Adaptive-Learning Global Optimization Algorithm for Nanosized Cluster Systems

Global optimization constitutes an important and fundamental problem in theoretical studies in many chemical fields, such as catalysis, materials, or separations problems. In this paper, a novel algorithm has been developed for the global optimization of large systems including neat and ligated clusters in the gas phase and supported clusters in periodic boundary conditions. The method is based on an updated artificial bee colony (ABC) algorithm method, that allows for adaptive-learning during the search process. The new algorithm is tested against four classes of systems of diverse chemical nature: gas phase Au₅₅, ligated Au₈²⁺, Au₈ supported on graphene oxide and defected rutile, and a large cluster assembly [Co₆Te₈(PEt₃)₆][C₆₀]_n, with sizes ranging between 1 and 3 nm and containing up to 1300 atoms. Reliable global minima (GMs) are obtained for all cases, either confirming published data or reporting new lower energy structures. The algorithm and interface to other codes in the form of an independent program, Northwest Potential Energy Search Engine (NWPEsSe), is freely available, and it provides a powerful and efficient approach for global optimization of nanosized cluster systems.

Understanding supported noble metal catalysts using first-principles calculations

Heterogeneous catalysis on supported and nonsupported nanoparticles is of fundamental importance in the energy and chemical conversion industries. Rather than laboratory analysis, first-principles calculations give us an atomic-level understanding of the structure and reactivity of nanoparticles and supports, greatly reducing the efforts of screening and design. However, unlike catalysis on low index single crystalline surfaces, nanoparticle catalysis relies on the tandem properties of a support material as well as the metal cluster itself, often with charge transfer processes being of key importance. In this perspective, we examine current state-of-the-art quantum-chemical research for the modeling of reactions that utilize small transition metal clusters on metal oxide supports. This should provide readers with useful insights when dealing with chemical reactions on such systems, before discussing the possibilities and challenges in the field.

Structural properties of sub-nanometer metallic clusters

At the nanoscale, the investigation of structural features becomes fundamental as we can establish relationships between cluster geometries and their physicochemical properties. The peculiarity lies in the variety of shapes often unusual and far from any geometrical and crystallographic intuition clusters can assume. In this respect, we should treat and consider nanoparticles as a new form of matter. Nanoparticle structures depend on their size, chemical composition, ordering, as well as external conditions e.g. synthesis method, pressure, temperature, support. On top of that, at finite temperatures nanoparticles can fluctuate among different structures, opening new and exciting horizons for the design of optimal nanoparticles for advanced applications. This article aims to overview geometrical features of transition metal clusters and of their various rearrangements.

Material discovery by combining stochastic surface walking global optimization with a neural network

While the underlying potential energy surface (PES) determines the structure and other properties of a material, it has been frustrating to predict new materials from theory even with the advent of supercomputing facilities. The accuracy of the PES and the efficiency of PES sampling are two major bottlenecks, not least because of the great complexity of the material PES. This work introduces a "Global-to-Global" approach for material discovery by combining for the first time a global optimization method with neural network (NN) techniques. The novel global optimization method, named the stochastic surface walking (SSW) method, is carried out massively in parallel for generating a global training data set, the fitting of which by the atom-centered NN produces a multi-dimensional global PES; the subsequent SSW exploration of large systems with the analytical NN PES can provide key information on the thermodynamics and kinetics stability of unknown phases identified from global PESs. We describe in detail the current implementation of the SSW-NN method with particular focuses on the size of the global data set and the simultaneous energy/force/stress NN training procedure. An important functional material, TiO2, is utilized as an example to demonstrate the automated global data set generation, the improved NN training procedure and the application in material discovery. Two new TiO2 porous crystal structures are identified, which have similar thermodynamics stability to the common TiO2 rutile phase and the kinetics stability for one of them is further proved from SSW pathway sampling. As a general tool for material simulation, the SSW-NN method provides an efficient and predictive platform for large-scale computational material screening.

Origin of high oxygen reduction reaction activity of Pt12 and strategy to obtain better catalyst using sub-nanosized Pt-alloy clusters

In the present study, methods to enhance the oxygen reduction reaction (ORR) activity of sub-nanosized Pt clusters were investigated in a theoretical manner. Using ab initio molecular dynamics and Monte Carlo simulations based on density functional theory, we have succeeded in determining the origin of the superior ORR activity of Pt12 compared to that of Pt13. That is, it was clarified that the electronic structure of Pt12 fluctuates to a greater extent compared to that of Pt13, which leads to stronger resistance against catalyst poisoning by O/OH. Based on this conclusion, a set of sub-nanosized Pt-alloy clusters was also explored to find catalysts with better ORR activities and lower financial costs. It was suggested that Ga4Pt8, Ge4Pt8, and Sn4Pt8 would be good candidates for ORR catalysts.