Small Cluster Sizes Research Articles

Fast Collective Hydrogen-Bond Dynamics in Hexafluoroisopropanol Related to its Chemical Activity.

Using fluorinated mono-alcohols, in particular hexafluoro-isopropanol (HFIP), as a solvent can enhance chemical reaction rates in a spectacular manner. Previous work has shown evidence that this enhancement is related to the hydrogen-bond structure of these liquids. Here, we investigate the hydrogen-bond dynamics of HFIP and compare it to that of its non-fluorinated analog, isopropanol. Ultrafast infrared spectroscopy experiments show that the dynamics of individual hydrogen-bonds is about twice as slow in HFIP as in isopropanol. Surprisingly, from dielectric spectroscopy we find the opposite behavior for the dynamics of hydrogen-bonded clusters: collective rearrangements are 3 times faster in HFIP than in isopropanol. This difference indicates that the hydrogen-bonded clusters in HFIP are smaller than in isopropanol. The differences in cluster size can be traced to changes in the hydrogen-bond donor and acceptor strengths upon fluorination. The smaller cluster size can boost reaction rates in HFIP by increasing the concentration of reactive, terminal OH-groups of the clusters, whereas the fast collective dynamics can increase the rate of formation of hydrogen-bonds with the reactants. The longer lifetime of the individual hydrogen-bonds in HFIP can enhance the stability of the hydrogen-bonded clusters, and so increase the probability of reactant-solvent hydrogen-bonding.

Estimating an adjusted risk difference in a cluster randomized trial with individual-level analyses.

In cluster randomized trials (CRTs) with a binary outcome, intervention effects are usually reported as odds ratios, but the CONSORT statement advocates reporting both a relative and an absolute intervention effect. With a simulation study, we assessed several methods to estimate a risk difference (RD) in the framework of a CRT with adjustment on both individual- and cluster-level covariates. We considered both a conditional approach (with the generalized linear mixed model [GLMM]) and a marginal approach (with the generalized estimating equation [GEE]). For both approaches, we considered the Gaussian, binomial, and Poisson distributions. When considering the binomial or Poisson distribution, we used the g-computation method to estimate the RD. Convergence problems were observed with the GEE approach, especially with low intra-cluster coefficient correlation values, small number of clusters, small mean cluster size, high number of covariates, and prevalences close to 0. All methods reported no bias. The Gaussian distribution with both approaches and binomial and Poisson distributions with the GEE approach had satisfactory results in estimating the standard error. Results for type I error and coverage rates were better with the GEE than GLMM approach. We recommend using the Gaussian distribution because of its ease of use (the RD is estimated in one step only). The GEE approach should be preferred and replaced with the GLMM approach in cases of convergence problems.

In situ Construction of Single-Atom Electronic Bridge on COF to Enhance Photocatalytic H2 Production.

Photocatalytic hydrogen production is one of the most valuable technologies in the future energy system. Here, we designed a metal-covalent organic frameworks (MCOFs) with both small-sized metal clusters and nitrogen-rich ligands, named COF-Cu3TG. Based on our design, small-sized metal clusters were selected to increase the density of active sites and shorten the distance of electron transport to active sites. While another building block containing nitrogen-rich organic ligands acted as a node that could in situ anchor metal atoms during photocatalysis and form interlayer single-atom electron bridges (SAEB) to accelerate electron transport. Together, they promoted photocatalytic performance. This represented the further utilization of Ru atoms and was an additional application of the photosensitizer. N2-Ru-N2 electron bridge (Ru-SAEB) was created in situ between the layers, resulting in a considerable enhancement in the hydrogen production rate of the photocatalyst to 10.47 mmol g-1 h-1. Through theoretical calculation and EXAFS, the existence position and action mechanism of Ru-SAEB were reasonably inferred, further confirming the rationality of the Ru-SAEB configuration. A sufficiently proximity between the small-sized Cu3 cluster and the Ru-SAEB was found to expedite electron transfer. This work demonstrated the synergistic impact of small molecular clusters with Ru-SAEB for efficient photocatalytic hydrogen production.

Cluster spin glass behaviour of Co2+−Al3+ layered double hydroxides

Frequency-dependent magnetic behaviour of cobalt-aluminium layered double hydroxides (LDH) with the Co/Al ratio of 2 and 3, was studied between 2 and 300 K. It was found from analysis of the temperature-dependent ac magnetic susceptibility that these LDH exhibit a magnetic cluster spin glass behaviour regardless of their basal spacing value and the Co/Al ratio as evidenced by EA/kBT0 > 1 relation obtained using Vogel-Fulcher law. The obtained values of Mydosh parameters in the range of 0.017–0.052 suggest that some of these materials are not far from the canonical spin glasses, implying a rather small size of the magnetic clusters.

PNNGS, a multi-convolutional parallel neural network for genomic selection.

Genomic selection (GS) can accomplish breeding faster than phenotypic selection. Improving prediction accuracy is the key to promoting GS. To improve the GS prediction accuracy and stability, we introduce parallel convolution to deep learning for GS and call it a parallel neural network for genomic selection (PNNGS). In PNNGS, information passes through convolutions of different kernel sizes in parallel. The convolutions in each branch are connected with residuals. Four different Lp loss functions train PNNGS. Through experiments, the optimal number of parallel paths for rice, sunflower, wheat, and maize is found to be 4, 6, 4, and 3, respectively. Phenotype prediction is performed on 24 cases through ridge-regression best linear unbiased prediction (RRBLUP), random forests (RF), support vector regression (SVR), deep neural network genomic prediction (DNNGP), and PNNGS. Serial DNNGP and parallel PNNGS outperform the other three algorithms. On average, PNNGS prediction accuracy is 0.031 larger than DNNGP prediction accuracy, indicating that parallelism can improve the GS model. Plants are divided into clusters through principal component analysis (PCA) and K-means clustering algorithms. The sample sizes of different clusters vary greatly, indicating that this is unbalanced data. Through stratified sampling, the prediction stability and accuracy of PNNGS are improved. When the training samples are reduced in small clusters, the prediction accuracy of PNNGS decreases significantly. Increasing the sample size of small clusters is critical to improving the prediction accuracy of GS.

The association of sociodemographic factors with total and item-level semantic fluency metrics.

We aimed to estimate the association of age, education, and sex/gender with semantic fluency performance as measured by the standard total number of words as well as novel item-level metrics and to descriptively compare associations across cohorts with different recruitment strategies and sample compositions. Cross-sectional data from 2,391 individuals from three cohorts were used: Washington Heights/Inwood Columbia Aging Project, a community-based cohort; Second Manifestations of ARTerial disease-Magnetic Resonance, a clinic-based cohort; and African American Alzheimer's Disease Genetics Study, a volunteer-based cohort. Total number of correct words and six item-level semantic fluency metrics were included as main outcomes: average cluster size, number of cluster switches, lexical/Zipf frequency, age of acquisition, and lexical decision response time. General linear models were run separately in each cohort to model the association between sociodemographic variables and semantic fluency metrics. Across cohorts, older age was associated with a lower total score and fewer cluster switches. Higher level of education was associated with naming more words, performing more cluster switches, and naming words with a longer lexical decision response time, lower frequency of occurrence, or later age of acquisition. Being female compared to male was associated with naming fewer words, smaller cluster sizes, naming words with a longer lexical decision response time, and lower age of acquisition. The effects varied in strength but were in a similar direction across cohorts. Item-level semantic fluency metrics-similar to the standard total score-are sensitive to the effects of age, education, and sex/gender. The results suggest geographical, cultural, and cross-linguistic generalizability of these sociodemographic effects on semantic fluency performance. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

An insight into annealing mechanism of graphitized structures after irradiation

Graphite components are constantly subjected to a combined actions of annealing and irradiation due to high temperatures and thermal spike caused by irradiation when reactors are operating, resulting in complex microstructures along with matching changes in the material properties and dimensions. This study investigates the evolution process of initial irradiation defects at different annealing temperatures, which is difficult to captured by experiment. The findings indicate that 923K reactor-temperature annealing can also quickly restore isolated self-interstitial atoms and small-sized point defect clusters to intralayer locations on the nanosecond scale. However, multi-interlayer penetration damages and localized point defect aggregation from high-energy irradiation can lead to the disappearance of layered structural information, which allows these damage structures to develop into interlayer dislocations during annealing process. These interlayer dislocations further exacerbate the interlayer expansion of graphite crystal and may elucidate the mechanism behind volume expansion in nuclear graphite within reactors. Fully amorphized regions can also regain a layered structure approximately when guided by residual layered structures at 2000K, while chaotic regions or disordered layered structures form in the absence of guidance. These chaotic regions significantly exacerbate the volume expansion of graphite model, which may be related to the rapid volume increase observed in nuclear graphite components at the end of reactor life. The study provides insights into the transformation of initial irradiation defects into different types of defects under different temperatures and damage states, which serve as a critical foundation for assessing the evolution of irradiation defects in nuclear graphite for reactor applications and annealing studies.

Impact of NPSR1 gene variation on the neural correlates of phasic and sustained fear in spider phobia-an imaging genetics and independent replication approach.

The functional neuropeptide S receptor 1 (NPSR1) gene A/T variant (rs324981) is associated with fear processing. We investigated the impact of NPSR1 genotype on fear processing and on symptom reduction following treatment in individuals with spider phobia. A replication approach was applied [discovery sample: Münster (MS) nMS = 104; replication sample Würzburg (WZ) nWZ = 81]. Participants were genotyped for NPSR1 rs324981 [T-allele carriers (risk) versus AA homozygotes (no-risk)]. A sustained and phasic fear paradigm was applied during functional magnetic resonance imaging. A one-session virtual reality exposure treatment was conducted. Change of symptom severity from pre to post treatment and within session fear reduction were assessed. T-allele carriers in the discovery sample displayed lower anterior cingulate cortex (ACC) activation compared to AA homozygotes independent of condition. For sustained fear, this effect was replicated within a small cluster and medium effect size. No association with symptom reduction was found. Within-session fear reduction was negatively associated with ACC activation in T-allele carriers in the discovery sample. NPSR1 rs324981 genotype might be associated with fear processing in the ACC in spider phobia. Interpretation as potential risk-increasing function of the NPSR1 rs324981 T-allele via impaired top-down control of limbic structures remains speculative. Potential association with symptom reduction warrants further research.

Lightweight safflower cluster detection based on YOLOv5

The effective detection of safflower in the field is crucial for implementing automated visual navigation and harvesting systems. Due to the small physical size of safflower clusters, their dense spatial distribution, and the complexity of field scenes, current target detection technologies face several challenges in safflower detection, such as insufficient accuracy and high computational demands. Therefore, this paper introduces an improved safflower target detection model based on YOLOv5, termed Safflower-YOLO (SF-YOLO). This model employs Ghost_conv to replace traditional convolution blocks in the backbone network, significantly enhancing computational efficiency. Furthermore, the CBAM attention mechanism is integrated into the backbone network, and a combined LCIOU+NWD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L_{CIOU + NWD}$$\\end{document} loss function is introduced to allow for more precise feature extraction, enhanced adaptive fusion capabilities, and accelerated loss convergence. Anchor boxes, updated through K-means clustering, are used to replace the original anchors, enabling the model to better adapt to the multi-scale information of safflowers in the field. Data augmentation techniques such as Gaussian blur, noise addition, sharpening, and channel shuffling are applied to the dataset to maintain robustness against variations in lighting, noise, and visual angles. Experimental results demonstrate that SF-YOLO surpasses the original YOLOv5s model, with reductions in GFlops and Params from 15.8 to 13.2 G and 7.013 to 5.34 M, respectively, representing decreases of 16.6% and 23.9%. Concurrently, SF-YOLO’s mAP0.5 increases by 1.3%, reaching 95.3%. This work enhances the accuracy of safflower detection in complex agricultural environments, providing a reference for subsequent autonomous visual navigation and automated non-destructive harvesting technologies in safflower operations.

Molecular dynamics simulation of the microstructural evolution in FeCuNi metallic glass under neutron irradiation

Investigating the microstructural characteristics of metallic glasses (MGs) under high-energy irradiation is of great importance for their industrial applications. In this study, molecular dynamics simulations of FeCuNi MG under neutron irradiation have been carried out, and the microstructural evolution has been systematically investigated by the largest standard cluster analysis (LaSCA). It is found that both topologically close-packed (TCP) and icosahedral structures in MG exhibit significant self-healing ability under irradiation. However, the TCP structures are quantitatively more dominant and thus can more rationally explain the structural self-healing mechanism of irradiated MG. Furthermore, during the irradiation process, the large-sized TCP clusters in the MG split into many small-sized clusters. However, due to the inherent self-healing ability of the TCP structures, these split clusters recombine into large-sized clusters after the irradiation ends. In summary, the results of this study are expected to provide theoretical guidance for the development of new high-performance reactor materials.

Electronic Structures of Small Stoichiometric ZnxOx Clusters.

Stoichiometric ZnxOx clusters in the subnanometer size regime have been the topic of several computational and mass spectrometry studies that showed the particular stability of the stoichiometric species relative to nonstoichiometric species (ZnxOy, x ≠ y). In the current study, we present the angle-resolved anion photoelectron (PE) spectra of stoichiometric ZnxOx-clusters (2 ≤ x ≤ 5), which are interpreted with supporting computational studies that include natural orbital ionization calculations on detachment transition cross sections. All spectra show evidence of Dxh ring structures, which had been predicted to be the most stable structures in previous computational studies. However, a new lowest energy isomer is reported for the Zn2O2 anion and neutral, a bent chain, which is readily reconciled with the most intense feature in the Zn2O2- PE spectrum. The computed PE angular distributions (PADs) associated with the lowest energy cluster structures identified computationally agree with the experimental results, with the exception of Zn5O5-, the experimental PAD of which suggests that strong vibronic coupling may be introducing anomalies. While the lowest lying electronic state of the Zn2O2 chain structure is a triplet state, all neutral ring structures (including Zn2O2, the anion of which also populates the ion beam), favor a singlet electronic state. The computed singlet-triplet splitting of the Dxh structures increases monotonically with x. Overall, we find that the properties of the ring structures evolve smoothly, rather than in the punctuated manner typically seen in the small cluster size regime.

Learning a reactive potential for silica-water through uncertainty attribution

The reactivity of silicates in aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite catalysts, or highly porous precipitated silica. While simulations of chemical reactions can provide insight at the molecular level, balancing accuracy and scale in reactive simulations in the condensed phase is a challenge. Here, we demonstrate how a machine-learning reactive interatomic potential trained on PaiNN architecture can accurately capture silicate-water reactivity. The model was trained on a dataset comprising 400,000 energies and forces of molecular clusters at the ωB97X-D3/def2-TZVP level. To ensure the robustness of the model, we introduce a general active learning strategy based on the attribution of the model uncertainty, that automatically isolates uncertain regions of bulk simulations to be calculated as small-sized clusters. The potential reproduces static and dynamic properties of liquid water and solid crystalline silicates, despite having been trained exclusively on cluster data. Furthermore, we utilize enhanced sampling simulations to recover the self-ionization reactivity of water accurately, and the acidity of silicate oligomers, and lastly study the silicate dimerization reaction in a water solution at neutral conditions and find that the reaction occurs through a flanking mechanism.

Formation dynamics of branching structure in the slippery DLCA model.

We numerically investigated the aggregation dynamics and resulting network structures of colloidal gels using the slippery diffusion-limited cluster aggregation (DLCA) model. In this model, bonds are irreversibly formed upon the particle contacts, but the angles among them are not fixed, unlike the conventional DLCA. This allows clusters to be deformed in the process of aggregation. By characterizing the aggregation dynamics and using a reduced network scheme, our simulation revealed two distinct branching structure formation routes depending on the particle volume fraction ϕ. In lower volume fraction systems (ϕ ≤ 8%), the deformations of small-size clusters proceed prior to the percolation. When the Maxwell criterion is satisfied and the clusters become mechanically stable, the formation of the branching structure is nearly completed. After forming the branching structures, they aggregate and form a larger percolating network. Then, the aggregation proceeds through the elongation and straightening of the chain parts of the network. In higher volume fraction systems (ϕ > 8%), on the other hand, the clusters percolate, and a fine and homogeneous branching structure is formed at the early stage of the aggregation. In the aging stage, it collapses into a denser and more heterogeneous structure and becomes more stable. Our quantitative analyses of the branching structure will shed light on a new strategy for describing the network formation and elasticity of colloidal gels.

Revealing the critical role of vanadium in radiation damage of tungsten-based alloys

Refractory tungsten-based medium- and high-entropy alloys form a promising class of strong, heat- and radiation-resistant materials for nuclear applications. Here, we systematically explore the radiation tolerance of Mo–Nb–Ta–V–W alloys using molecular dynamics simulations and a machine-learned interatomic potential. Going from pure W to W-based equiatomic binaries, ternaries, quaternary, and the quinary high-entropy alloy, we simulate the radiation damage accumulation up to 0.4–0.8 dpa through cumulative collision cascades. We find that the radiation damage and evolution are controlled by a combination of cascade-induced clustering, the balance in migration of vacancies and interstitials, and defect cluster binding energies. These mechanisms are strongly influenced by atom size, among which vanadium as the smallest atom is decisive. In pure W and alloys without V, the microstructure is characterised by large and growing interstitial dislocation loops. In stark contrast, in alloys containing V, vacancy dislocation loops are formed directly in collision cascades and the balanced migration rate of vacancies and interstitials promotes recombination and prevents growth of interstitial loops. The atom size differences also lead to clear segregation, with small atoms (V) in interstitial clusters and larger atoms (Ta, Nb) segregating around vacancy clusters. Furthermore, the small cluster sizes, formation of vacancy loops, segregation, and balanced migration in V-containing alloys lead to low swelling and an exceptionally efficient defect annealing compared to pure W and V-free alloys. Our results highlight WTaV as a promising low-activation material, where almost 90% of defects are annealed at 2000 K during 5 ns compared to <20% in pure W.

Controlling Interfacial Hydrogen Bonding at a Gold Surface: The Effect of Organic Cosolvents.

Water often serves as both a reactant and solvent in electrocatalytic reactions. Interfacial water networks can affect the transport and kinetics of these reactions, e.g., hydrogen evolution reaction and CO2 reduction reaction. Adding cosolvents that influence the hydrogen-bonding (H-bonding) environment, such as dimethyl sulfoxide (DMSO), has the potential to tune the reactivity of these important electrocatalytic reactions by regulating the interfacial local environment and water network. We investigate interfacial H-bonding networks in water-DMSO cosolvent mixtures on gold surfaces by using surface-enhanced infrared absorption spectroscopy and molecular dynamics simulations. Experiments and simulations show that the gold surface is enriched with dehydrated DMSO molecules and the mixture phase-separates to form water clusters. Simulations show a "buckled" water conformation at the surface, further constraining interfacial H-bonding. The small size of these water clusters and the energetically unfavorable H-bond conformations might inhibit H-bonding with bulk water, suppressing the proton diffusion required for efficient hydrogen evolution reaction processes.

Quantum aided efficient resource control for connected support in IRS assisted networks

Context:To achieve the vision of all connected world with uninterrupted communication support, 6G technology plays an important role. But the scarce radio spectrum and limited network resources is the main challenge in delivering its promised performance. Objectives:This paper presents an IRS-aided cell free NOMA network model that aims to provide uniform network coverage. The future 6G technology envisions for serving billions of interconnected devices with seamless communication support, data handling capabilities and computational accuracy. But the scarcity of network resources is the main limitation. Thus, the need is to design quantum enabled intelligent and dynamic networks capable of offering extended network capabilities. Proposed work is on intelligent network framework that provides uniform network coverage through efficient resource management for a 6G enabled expanded IoT network. Methods:To enable efficient resource management, a quantum enabled resource control algorithm is proposed that creates user clusters and associates each AP-IRS pair to each cluster. Each AP transmits the superimposed signals of its intended cluster against all the user clusters as in conventional NOMA system. The nodes in each cluster have been assigned unique pilots so as to avoid intracluster interference. The use of IRS enables desired NOMA beamforming such that the effect of unfavourable wireless environment is mitigated. Results:The performance of the IRS-aided cell-free NOMA network is evaluated for average sum rate with different AP transmit power, cluster sizes, IRS reflecting elements and IRS phase shifts. It is shown that at transmit power per AP of 30dBm, the average sum rate of the system improves by 7.52% with the proposed algorithm using equal power allocation scheme. Further, the comparative performance analysis of three different communication systems is carried out to validate the proposed communication model. Conclusion:It is observed that with more number of users per cluster, the average sum rate of the system initially increases for small cluster sizes and then it becomes constant for large cluster sizes. The proposed clustering method outperforms the random clustering approach achieving sum rate of 12.9 bits/s/Hz with N =300 and M= 8. The comparison of different communication scenarios reveals that the maximum sum rate of 12.2 bits/s/Hz is achieved with the proposed model incorporating proposed clustering mechanism. Further, the energy efficiency analysis suggests that energy efficiency improves with N and Pc with proposed clustering approach. The use case scenarios for the integration of quantum computing with IRS technology are also presented.

Enhanced Wear and Corrosion Resistance of AZ91 Magnesium Alloy via Adherent Si-DLC Coating with Si-Interlayer: Impact of Biasing Voltage

Magnesium alloys are the lowest-density structural metals with a wide range of applications, such as aircraft skins, engine casings and automobile hubs. However, its low surface hardness and non-corrosion resistance in natural environments limit its wide range of applications. In this work, Si-DLC coatings (Si: 15 at.%) are fabricated on AZ91 alloy using a hollow cathode discharge combined with a DC bias voltage from 0 to −300 V to increase the deposition rate and modulate the structure and properties of the coatings. The Si interlayer with a thickness of around 0.6 µm is deposited first to enhance the adhesion. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Raman spectroscopy are used to investigate the effect of DC bias on the microstructure evolution of Si-DLC coatings. Meanwhile, corrosion and wear resistance of the coatings at various bias voltages have been investigated using electrochemical workstations and pin-on-desk wear testers. It is shown that the bias-free coating has a loose structure and is less resistant to corrosion and wear. The bias coating has a compact structure, small carbon cluster size, high chloride ion corrosion resistance, and high wear resistance against Al2O3 spheres. The corrosion potential of the coating bias at −300 V is −0.98 V, the corrosion current density is 1.35 × 10−6 A·cm−2, the friction coefficient is 0.08, and the wear rate is 10−8 orders of magnitude. The formation of SiC nanocrystals and high sp3-C, as well as the formation of transfer films on the surface of their counterparts, are the main reasons for the ultra-high wear resistance of the bias coatings. The wear rate, coefficient of friction, and corrosion rate of the coating are 0.0069 times, 0.2 times, and 0.0088 times that of the AZ91 alloy, respectively. However, the bias coating has only short to medium-term protection against the magnesium alloy and no long-term protection due to cracks caused by its high internal stress.

Quantum cluster equilibrium theory applied to liquid ammonia.

Through this paper, the authors propose using the quantum cluster equilibrium (QCE) theory to reinvestigate ammonia clusters in the liquid phase. The ammonia clusters from size monomer to hexadecamer were considered to simulate the liquid ammonia in this approach. The clusterset used to model the liquid ammonia is an ensemble of different structures of ammonia clusters. After studious research of the representative configurations of ammonia clusters through the cluster research program ABCluster, the configurations have been optimized at the MN15/6-31++G(d,p) level of theory. These optimizations lead to geometries and frequencies as inputs for the Peacemaker code. The QCE study of this molecular system permits us to get the liquid phase populations in a temperature range of 190-260 K, covering the temperatures from the melting point to the boiling point. The results show that the population of liquid ammonia comprises mainly the ammonia hexadecamer followed by pentadecamer, tetradecamer, and tridecamer. We noted that the small-sized ammonia clusters do not contribute to the population of liquid ammonia. In addition, the thermodynamic properties, such as heat of vaporization, heat capacity, entropy, enthalpy, and free energies, obtained by the QCE theory have been compared to the experiment given some relatively good agreements in the gas phase and show considerable discrepancies in liquid phase except the density. Finally, based on the predicted population, we calculated the infrared spectrum of liquid ammonia at 215 K temperature. It comes out that the calculated infrared spectrum qualitatively agrees with the experiment.

Influence of the Gas Flow Rate on the Crack Formation of AlCoCrNi High-Entropy Metallic Film Fabricated Using Magnetron Sputtering

In the present study, the AlCoCrNi high-entropy metallic film was deposited on a Si wafer using a magnetron sputtering system. To capture the effects of the sputtering parameters on the microstructure and mechanical properties of the film, the flow rate of Ar gas injected into the chamber (5, 7, and 8 sccm) was controlled. All films were identified as being of BCC phase with compositions of near equiatomic proportions, regardless of the gas flow rates. Nano-scale clusters were observed on the surfaces of all films, and nano-cracks were found in the film deposited at the Ar gas flow rate of 8 sccm, unlike the films deposited at the gas flow rates of 5 and 7 sccm. Detailed microstructural analysis of film deposition at an Ar gas flow rate of 8 sccm indicated that the void boundaries contribute to the formation of nano-cracks. The nano-indentation results indicated that the Ar gas flow rate 5 sccm specimen, with the smallest cluster size at the topmost surface, showed the highest hardness (12.21 ± 1.05 GPa) and Young’s modulus (188.1 ± 11 GPa) values.

A trinary support of Ni/NiO/C to immobilize Ir nanoclusters for alkaline hydrogen oxidation.

The hydrogen oxidation reaction is an important process in anion exchange membrane fuel cells with alkaline solutions. The pursuit of efficient catalysts for alkaline hydrogen oxidation has attracted considerable attention. In this study, we present a precursor route for the synthesis of a new Ir-based catalyst (Ir-Ni/NiO/C), in which Ir nanoclusters were immobilized on the generated Ni/NiO/C support derived from a metal-organic framework. The small size of Ir clusters facilitates the exposure of catalytically active sites. The electronic interplay between the Ir nanoclusters and the Ni/NiO/C support optimized the hydrogen binding energy (HBE) and hydroxide binding energy (OHBE) on the surface, which is unattainable on the contrasting Ir-C, Ir-Ni/C, and Ir-NiO/C products. The optimized catalyst shows excellent mass activity for alkaline hydrogen oxidation, which is 3.1 times that of the Pt/C catalyst. This study presents a promising pathway for the development of advanced HOR catalysts.