Investigation of the Stability Mechanisms of Eight-Atom Binary Metal Clusters Using DFT Calculations and k-means Clustering Algorithm

Data-driven stabilization of NimPdn-m nanoalloys: a study using density functional theory and data mining approaches

Green hydrogen, generated through the electrolysis of water, is a viable alternative to fossil fuels, although its adoption is hindered by the high costs associated with the catalysts. Among a wide variety of potential materials, binary nickel-palladium (NiPd) systems have garnered significant attention, particularly at the nanoscale, for their efficacious roles in catalyzing hydrogen and oxygen evolution reactions. However, our atom-level understanding of the descriptors that drive their energetic stability at the nanoscale remains largely incomplete. Here, we investigate by density functional theory calculations the descriptors that drives the stability of the NimPdn-m clusters for different sizes (n = 13, 27, 41) and compositions. To achieve our goals, a large number of trial configurations were generated and selected using data mining algorithms (k-means, t-SNE) and genetic algorithms, while the most important physical-chemical descriptors were identified using Spearman correlation analysis. We have found that core-shell formation, with the smaller Ni atoms lying in the center of the particle, plays a major role in the stabilization of the nanoalloys, and this effect causes the alloys to assume a icosahedral-fragment configuration (as the unary nickel cluster) instead of a fcc fragment (as the unary palladium cluster). However, the core-shell formation in this alloy is unique in that Pd poor compositions exhibit scattered Pd atoms on the surface. As the palladium content increases, this gives rise to the complete Pd shell. This stabilization mechanism is quantitatively supported by the different correlations observed in the number of Ni-Ni and Pd-Pd bonds with energy, in which the latter tends to decrease alloy stability. Furthermore, a notable trend is the correlation between the coordination number of Ni atoms with alloy stabilization, while the coordination of Pd atoms shows an inverse correlation.

Underlying mechanisms of gold nanoalloys stabilization

Gold nanoclusters have attracted significant attention due to their unique physical-chemical properties, which can be tuned by alloying with elements such as Cu, Pd, Ag, and Pt to design materials for various applications. Although Au-nanoalloys have promising applications, our atomistic understanding of the descriptors that drive their stability is far from satisfactory. To address this problem, we considered 55-atom model nanoalloys that have been synthesized by experimental techniques. Here, we combined data mining techniques for creating a large sample of representative configurations, density functional theory for performing total energy optimizations, and Spearman correlation analyses to identify the most important descriptors. Among our results, we have identified trends in core-shell formation in the AuCu and AuPd systems and an onion-like design in the AuAg system, characterized by the aggregation of gold atoms on nanocluster surfaces. These features are explained by Au's surface energy, packing efficiency, and charge transfer mechanisms, which are enhanced by the alloys' preference for adopting the structure of the alloying metal rather than the low-symmetry one presented by Au55. These generalizations provide insights into the interplay between electronic and structural properties in gold nanoalloys, contributing to the understanding of their stabilization mechanisms and potential applications in various fields.

A new method for identifying potential hazardous areas of heavy metal pollution in sediments

The combined effect of pollution source discharge and sediment adsorption leads to the rapid enrichment of heavy metals and other pollutants in lake sediments, which poses a serious threat to the lake ecosystem. Accurately identifying the risk areas of heavy metals in sediments is the key to lake sediment pollution control. Taking Taihu Lake as the study area, combined with the ecological risk status of heavy metals in sediments, the spatial clustering characteristics of pollution sources and the clustering information of sediment attributes, a potential toxic risk area identification method based on sediment source aggregation class (SLISA-SCA) was established. Through the source analysis of heavy metals in sediments, heavy metals such as Cr, Mn, Cu and Zn in Taihu Lake sediments were identified to have originated from natural sources and were subsequently disturbed by human activities to a certain extent. Cd was found to be strongly affected by human activities, and almost all Taihu Lake sediments were affected to varying degrees. In addition, the anthropogenic sources of heavy metals show high concentration clustering characteristics in the lake bay. By K-means cluster analysis of sediment attributes, three significant differences were obtained, which were determined as potential high pollution risk areas, potential medium risk areas and potential low risk areas, and the proportions were 5.6%, 27.6% and 66.8%, respectively. The SLISA-SCA model established in this study, from the perspective of source sinks, comprehensively considers the risks caused by pollution sources and sediment attributes to sediments and divides Taihu Lake into five different risk control areas (high-risk control area, potential high-risk control area, potential risk control area, potential low-risk control area and low-risk control area). This study identified areas with different levels of heavy metal pollution in Taihu Lake sediments, proposes corresponding treatment measures, and provides a scientific and systematic method and technology for the pollution management of other river and lake sediments in the world.

Ab initio study of the adsorption properties of CO2 reduction intermediates: The effect of Ni5Ga3 alloy and the Ni5Ga3/ZrO2 interface

The Ni5Ga3 alloy supported on ZrO2 is a promising catalyst for the reduction of CO2 due to its higher selectivity to methanol at ambient pressure, e.g., activity comparable to industrial catalysts. However, our atomistic understanding of the role of the cooperative effects induced by the Ni5Ga3 alloy formation and its Ni5Ga3/ZrO2 interface in the CO2 reduction is still far from satisfactory. In this work, we tackle these questions by employing density functional theory calculations to investigate the adsorption properties of key CO2 reduction intermediates (CO2, H2, cis-COOH, trans-COOH, HCOO, CO, HCO, and COH) on Ni8, Ga8, Ni5Ga3, (ZrO2)16, and Ni5Ga3/(ZrO2)16. We found that Ni containing clusters tended to assume wetting configurations on the (ZrO2)16 cluster, while the presence of Ga atoms weakens the adsorption energies on the oxide surface. We also observed that CO2 was better activated on the metal–oxide interfaces and on the oxide surface, where it was able to form CO3-like structures. Meanwhile, H2 activation was only observed on Ni sites, which indicates the importance of distinct adsorption sites that can favor different CO2 reduction steps. Moreover, the formation of the metal–oxide interface showed to be beneficial for the adsorption of COOH isomers and unfavorable for the adsorption of HCOO.

An Improved Self-Adaptive Differential Evolution with the Neighborhood Search Algorithm for Global Optimization of Bimetallic Clusters

Global optimization of multicomponent cluster structures is considerably time-consuming due to the existence of a vast number of isomers. In this work, we proposed an improved self-adaptive differential evolution with the neighborhood search (SaNSDE) algorithm and applied it to the global optimization of bimetallic cluster structures. The cross operation was optimized, and an improved basin hopping module was introduced to enhance the searching efficiency of SaNSDE optimization. Taking (PtNi)_N (N = 38 or 55) bimetallic clusters as examples, their structures were predicted by using this algorithm. The traditional SaNSDE algorithm was carried out for comparison with the improved SaNSDE algorithm. For all the optimized clusters, the excess energy and the second difference of the energy were calculated to examine their relative stabilities. Meanwhile, the bond order parameters were adopted to quantitatively characterize the cluster structures. The results reveal that the improved SaNSDE algorithm possessed significantly higher searching capability and faster convergence speed than the traditional SaNSDE algorithm. Furthermore, the lowest-energy configurations of (PtNi)₃₈ clusters could be classified as the truncated octahedral and disordered structures. In contrast, all the optimal (PtNi)₅₅ clusters were approximately icosahedral. Our work fully demonstrates the high efficiency of the improved algorithm and advances the development of global optimization algorithms and the structural prediction of multicomponent clusters.

Integration Path Analysis of Traditional Media and New Media Based on Internet of Things Data Mining

With the development of information technology, the influence of traditional media is weakening day by day. In view of this, based on the Internet of things data mining technology, this study improves the k-means algorithm, and designs a new media precision marketing system, which combines new media with traditional media and provides a new marketing model for traditional media. The results show that the accuracy of the improved k-means algorithm finally reaches about 93%, which is much higher than that of similar algorithms. It can be seen that the improved k-means algorithm has better performance. In the application experiment, this study can effectively find the new media activities with the highest user preference, and the impact of the two websites' application of precision marketing system on users has also increased. It can be seen that the precision marketing system designed this time is more effective.