Small Cluster Sizes Research Articles

Contamination effects in cluster randomised trials of TB interventions.

<sec><title>BACKGROUND</title>Cluster randomised trials (CRTs) of TB interventions have achieved mixed results, with many lacking significant reductions in outcomes. Contamination in CRTs, resulting from short and long-term movement between clusters and the general population, may dilute the impact of measured intervention.</sec><sec><title>METHODS</title>We systematically reviewed the literature to identify CRTs that aimed to capture the population-level effects of the intervention on TB. Details of trial designs, interventions, outcomes, populations, cluster configurations, and geographic data were extracted to produce text summaries, descriptive statistics, and spatial analyses.</sec><sec><title>RESULTS</title>We screened 1,039 abstracts and included 20 reports from seven CRTs. The median number of clusters was 32 (IQR 23-61), with populations ranging from 400-50,000 individuals per cluster. Four trials reported spatial data, from which the mean distance between clusters was 12.3 km (range 3.71-35.9). Several trials acknowledged design limitations, such as small cluster sizes and population mobility, which could have led to underestimations of intervention impact. Trials used various geographic, social, and pre-existing TB measures to select and allocate study clusters. Data on the potential for contamination are inconsistent.</sec><sec><title>CONCLUSION</title>Gaps remain in the reporting of methods and results, suggesting necessary improvements to standardised reporting tools. These insights can inform recommendations for improved CRT design and reporting practices.</sec>.

Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage.

The high-entropy strategy has emerged as a prevalent approach to boost capacitive energy-storage performance of relaxors for advanced electrical and electronic systems. However, exploring high-performance high-entropy systems poses challenges due to the extensive compositional space. Herein, with the assistance of machine learning screening, we demonstrated a high energy-storage density of 20.7 J cm-3 with a high efficiency of 86% in a high-entropy Pb-free relaxor ceramic. A random forest regression model with key descriptors based on limited reported experimental data were developed to predict and screen the elements and chemical compositions of high-entropy systems. Following basic experiments, a (Bi0.5Na0.5)TiO3-based high-entropy relaxor characterized by fine grains, weakly-coupled and small-sized polar clusters was identified. This resulted in a near-linear polarization behavior and an ultrahigh breakdownstrength of 95 kV mm-1. Further, this high-entropy realxor presented a high discharge energy density of 7.7 J cm-3 under discharge rate of about 27 ns, along with superior temperature and fatigue stability. Our results present the data-driven model for efficiently exploring high-performance high-entropy relaxors, demonstrating the potential of machine learning in developing relaxors.

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.

Irradiation Damage Evolution Dependence on Misorientation Angle for Σ 5 Grain Boundary of Nb: An Atomistic Simulation-Based Study

Abstract Niobium as refractory element holds ability to arrest the primary radiation damage in reactor's fissionable conditions and can withstand high-temperature applications. We have inclined the investigation of irradiated Nb Σ 5 symmetric-tilt angled grain boundary (STGB) models at two high-angled grain boundary misorientation: 53.13 deg (Σ 5(2–10)/(120)) and 36.87 deg (Σ 5(3–10)/(130)), respectively. A hybrid of Ziegler–Biersack–Littmark (ZBL) and embedded atom method (EAM) potentials were superimposed to simulate radiation damage. Statistical averaging of the displacement cascades was conducted to study the dynamic evolution of the point defects and interstitial clusters at varying magnitudes of primary knock-on atom (PKA) energies, irradiation temperatures, and PKA directions. The irradiated grain bounary (GB) models were compared with an irradiated bulk Nb specimen, and the results of the study indicate that the irradiated Nb system with greater misorientation angle, i.e., Nb Σ 5 (ɵ = 53.13 deg) survived with lower number Frenkel pair defects as well as the population small-sized interstitial clusters. The point defect cluster analysis indicated the highest population of interstitial clusters survived in Nb STGB models were irradiated along &amp;lt;1 3 5&amp;gt; PKA direction and 100 keV recoil energies respectively.

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.

A reduced cluster dynamics modeling of radiation damage in tungsten

A reduced cluster dynamics model is developed to investigate the microstructure evolution of irradiated single crystal tungsten. The model accounts for the generation and reaction of point defects, small-size defect clusters, helium clusters, as well as the nucleation and growth of large immobile defects, including the interstitial dislocation loops, voids and helium bubbles. Moreover, by incorporating an atomically informed loop punching mechanism for bubble growth, the model is able to accurately capture the evolution kinetics of radiation-induced defects with and without the helium implantation. The predicted densities and sizes of loops and voids/bubbles, the helium-to-vacancy ratio and the internal pressure of helium bubbles are all in good agreement with experimental data at different irradiation doses across a wide range of temperatures from 300 K up to 1200 K. This work aims to provide a robust tool for analyzing the microstructure of irradiated tungsten under both fission and fusion conditions.

Phase transformations of ternary copper iron sulfide Cu1.1Fe1.9S3.0 under temperature variations: thermodynamic and kinetic aspects

The article considers ternary sulfide Cu1.1Fe1.9S3 with a metal/sulfur ratio corresponding to the complete stoichiometry of cubanite CuFe2S3 as an intermediate phase of a solid solution with chemically disordered Cu and Fe cations in the ordered anionic framework. A new approach to determining the nature of the solid solution, its stability and behavior during cooled over a wide temperature and time range is suggested. To synthesize the sample, we used controlled directional solidification of a homogeneous melt with the Cu1.1Fe1.9S3 composition under quasi-equilibrium conditions and obtained a solidified zoned ingot, where the distribution of Cu, Fe, and S elements along its length was quantitatively determined. To detect small-scale structural and chemical changes, we used optical and electron microscopy methods, electron-probe X-ray spectral microanalysis, full-profile X-ray diffraction analysis, and the differential dissolution method, which allowed to determine the phase and chemical states of the samples both at the macro level and with a high spatial resolution. With this approach, we established the following: Cu1.1Fe1.9S3 is an intermediate phase of a system with end-members of cubanite CuFe2S3 and chalcopyrite CuFeS2; a homogeneous solid solution of chalcopyrite with 5 mol. % of cubanite exists near 930 °С with a chaotic distribution of Cu and Fe between the existing crystallographic positions; a solid solution of chalcopyrite with 6 mol. % of cubanite at 900 °С facilitates lattice strain relaxation through the formation of a block nanostructure; there is a solid solution of cubanite with 30 mol. % of chalcopyrite at 900–720 °С, with small-size clusters with a chalcopyrite stoichiometry evenly distributed inside the Cu0.94Fe2S3 matrix. The factors determining the evolution and stability of solid solutions are discussed taking into account the polymorphism of chalcopyrite phase. The newly obtained data is important for the synthesis of magnetic nanosized Cu-Fe sulfide materials and can also be used in the processing of sulfide ores rich in copper

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.

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.