Temperature-Dependent Frictional Behavior of MoS2 in Humid Environments: Insights from Water Molecule Adsorption and DFT Analyses.

This study investigates the temperature-dependent frictional behavior of MoS2 in humid environments within the context of a long-standing debate over increased friction due to oxidation processes or molecular adsorption. By combining sliding friction experiments and density functional theory (DFT)-based first-principles simulations, it aims to clarify the role of water molecule adsorption in influencing frictional properties of MoS2, addressing the challenge of identifying interfacial bonding behavior responsible for friction in such conditions. Sliding experiments revealed that magnetron-sputtered MoS2 exhibits a reduction in the coefficient of friction (COF) with an increase in temperature from 25 to 100 °C under 20 and 40% relative humidity. This change in the COF obeys the Arrhenius law, presenting an energy barrier of 0.165 eV, indicative of the temperature-dependent nature of these frictional changes and suggests a consistent frictional mechanism. DFT simulations showed that H2O molecules are adsorbed at MoS2 vacancy defects with adsorption energies ranging from -0.56 to -0.17 eV, which align with the experimentally determined energy barrier. Adsorptive interactions, particularly the formation of stable H···S interfacial hydrogen bonds at defect sites, increase the interlayer adhesion and impede layer shearing. TEM analysis confirms that although MoS2 layers align parallel to the sliding direction in humid conditions, the COF remains at 0.12, as opposed to approximately 0.02 in dry air. This demonstrates that parallel layer alignment does not notably decrease the COF, underscoring humidity's significant role in MoS2's COF values, a result also supported by the Arrhenius analysis. The reversibility of the physisorption process, demonstrated by the recovery of the COF in high-temperature humid environments, suggests its dynamic nature. This study yields fundamental insights into MoS2 interfaces for environments with variable humidity and temperature, crucial for demanding tribological applications.

Aluminum alloy compositions and properties extracted from a corpus of scientific manuscripts and US patents.

Researchers continue to explore and develop aluminum alloys with new compositions and improved performance characteristics. An understanding of the current design space can help accelerate the discovery of new alloys. We present two datasets: 1) chemical composition, and 2) mechanical properties for predominantly wrought aluminum alloys. The first dataset contains 14,884 entries on aluminum alloy compositions extracted from academic literature and US patents using text processing techniques, including 550 wrought aluminum alloys which are already registered with the Aluminum Association. The second dataset contains 1,278 entries on mechanical properties for aluminum alloys, where each entry is associated with a particular wrought series designation, extracted from tables in academic literature.

Stabilization of a monolayer tellurene phase at CdTe interfaces.

Two-dimensional (2D) materials provide a plethora of novel condensed matter physics and are the new playground in materials science, offering potentially vast applications. One of the critical hurdles for many 2D systems is the synthesis of these low-dimensional systems as well as the prediction and identification of new candidates. Herein, a self-assembly of a monolayer tellurene by bonding CdTe wafers is demonstrated for the first time. The conventional applications of wafer-bonding range from the production of microelectromechanical systems to the synthesis of lattice-mismatched multi-junction photovoltaics. Due to the heterogeneous materials that are typically employed, the bond-interface usually contains a thin amorphous layer or arrays of dislocations. Such an interface is thus itself inactive and in many cases has detrimental effects on the device. The new material phase stabilized in this work consists of an undulating monolayer of tellurium atoms covalently bonded to {111} Cd-terminated CdTe wafer surfaces. First-principles calculations and experimentally observed changes in the localized plasmon excitation energy indicate the clear rearrangement of the underlying band-structure suggesting a metallic character, bands showing linear dispersion, and a significant asymmetric spin-band splitting. The I-V characteristics show the presence of a highly conductive pathway that lowers the resistivity by three orders of magnitude, as compared to bulk CdTe, which can be attributed to the tellurium monolayer. The findings indicate that suitably chosen crystallographic wafer surfaces can act as structural templates allowing the production of exotic phases. The presently stabilized monolayer is an addition to the family of tellurene variants, providing new insights into the fundamental properties of this and other emerging 2D materials, while attracting attention to the unusual side of the wafer-bonding technology exemplified in this study.

Roles of sliding-induced defects and dissociated water molecules on low friction of graphene.

Sliding contact experiments and first-principles calculations were performed to elucidate the roles of structural defects and water dissociative adsorption process on the tribo-chemical mechanisms responsible for low friction of graphene. Sliding friction tests conducted in ambient air and under a dry N2 atmosphere showed that in both cases a high running-in coefficient of friction (COF) occurred initially but a low steady-state COF was reached only when the sliding was continued in air with moisture. Density functional theory (DFT) calculations indicated that the energy barrier (E b ) for dissociative adsorption of H2O was significantly lower in case of reconstructed graphene with a monovacancy compared to pristine graphene. Cross-sectional transmission electron microscopy of graphene transferred to the counterface revealed a partly amorphous structure incorporating damaged graphene layers with d-spacings larger than that of the original layers. DFT calculations on the reconstructed bilayer AB graphene systems revealed an increase of d-spacing due to the chemisorption of H, O, and OH at the vacancy sites and a reduction in the interlayer binding energy (E B ) between the bilayer graphene interfaces compared to pristine graphene. Thus, sliding induced defects facilitated dissociative adsorption of water molecules and reduced COF of graphene for sliding tests under ambient and humid environments but not under an inert atmosphere.

Ultralow-loading platinum-cobalt fuel cell catalysts derived from imidazolate frameworks.

Achieving high catalytic performance with the lowest possible amount of platinum is critical for fuel cell cost reduction. Here we describe a method of preparing highly active yet stable electrocatalysts containing ultralow-loading platinum content by using cobalt or bimetallic cobalt and zinc zeolitic imidazolate frameworks as precursors. Synergistic catalysis between strained platinum-cobalt core-shell nanoparticles over a platinum-group metal (PGM)-free catalytic substrate led to excellent fuel cell performance under 1 atmosphere of O2 or air at both high-voltage and high-current domains. Two catalysts achieved oxygen reduction reaction (ORR) mass activities of 1.08 amperes per milligram of platinum (A mgPt -1) and 1.77 A mgPt -1 and retained 64% and 15% of initial values after 30,000 voltage cycles in a fuel cell. Computational modeling reveals that the interaction between platinum-cobalt nanoparticles and PGM-free sites improves ORR activity and durability.

Machine Learning Force Field Parameters from Ab Initio Data.

Machine learning (ML) techniques with the genetic algorithm (GA) have been applied to determine a polarizable force field parameters using only ab initio data from quantum mechanics (QM) calculations of molecular clusters at the MP2/6-31G(d,p), DFMP2(fc)/jul-cc-pVDZ, and DFMP2(fc)/jul-cc-pVTZ levels to predict experimental condensed phase properties (i.e., density and heat of vaporization). The performance of this ML/GA approach is demonstrated on 4943 dimer electrostatic potentials and 1250 cluster interaction energies for methanol. Excellent agreement between the training data set from QM calculations and the optimized force field model was achieved. The results were further improved by introducing an offset factor during the machine learning process to compensate for the discrepancy between the QM calculated energy and the energy reproduced by optimized force field, while maintaining the local "shape" of the QM energy surface. Throughout the machine learning process, experimental observables were not involved in the objective function, but were only used for model validation. The best model, optimized from the QM data at the DFMP2(fc)/jul-cc-pVTZ level, appears to perform even better than the original AMOEBA force field (amoeba09.prm), which was optimized empirically to match liquid properties. The present effort shows the possibility of using machine learning techniques to develop descriptive polarizable force field using only QM data. The ML/GA strategy to optimize force fields parameters described here could easily be extended to other molecular systems.

Machine learnt bond order potential to model metal-organic (Co-C) heterostructures.

A fundamental understanding of the inter-relationships between structure, morphology, atomic scale dynamics, chemistry, and physical properties of mixed metallic-covalent systems is essential to design novel functional materials for applications in flexible nano-electronics, energy storage and catalysis. To achieve such knowledge, it is imperative to develop robust and computationally efficient atomistic models that describe atomic interactions accurately within a single framework. Here, we present a unified Tersoff-Brenner type bond order potential (BOP) for a Co-C system, trained against lattice parameters, cohesive energies, equation of state, and elastic constants of different crystalline phases of cobalt as well as orthorhombic Co2C derived from density functional theory (DFT) calculations. The independent BOP parameters are determined using a combination of supervised machine learning (genetic algorithms) and local minimization via the simplex method. Our newly developed BOP accurately describes the structural, thermodynamic, mechanical, and surface properties of both the elemental components as well as the carbide phases, in excellent accordance with DFT calculations and experiments. Using our machine-learnt BOP potential, we performed large-scale molecular dynamics simulations to investigate the effect of metal/carbon concentration on the structure and mechanical properties of porous architectures obtained via self-assembly of cobalt nanoparticles and fullerene molecules. Such porous structures have implications in flexible electronics, where materials with high electrical conductivity and low elastic stiffness are desired. Using unsupervised machine learning (clustering), we identify the pore structure, pore-distribution, and metallic conduction pathways in self-assembled structures at different C/Co ratios. We find that as the C/Co ratio increases, the connectivity between the Co nanoparticles becomes limited, likely resulting in low electrical conductivity; on the other hand, such C-rich hybrid structures are highly flexible (i.e., low stiffness). The BOP model developed in this work is a valuable tool to investigate atomic scale processes, structure-property relationships, and temperature/pressure response of Co-C systems, as well as design organic-inorganic hybrid structures with a desired set of properties.

Atomic and electronic structure of Lomer dislocations at CdTe bicrystal interface.

Extended defects are of considerable importance in determining the electronic properties of semiconductors, especially in photovoltaics (PVs), due to their effects on electron-hole recombination. We employ model systems to study the effects of dislocations in CdTe by constructing grain boundaries using wafer bonding. Atomic-resolution scanning transmission electron microscopy (STEM) of a [1-10]/(110) 4.8° tilt grain boundary reveals that the interface is composed of three distinct types of Lomer dislocations. Geometrical phase analysis is used to map strain fields, while STEM and density functional theory (DFT) modeling determine the atomic structure at the interface. The electronic structure of the dislocation cores calculated using DFT shows significant mid-gap states and different charge-channeling tendencies. Cl-doping is shown to reduce the midgap states, while maintaining the charge separation effects. This report offers novel avenues for exploring grain boundary effects in CdTe-based solar cells by fabricating controlled bicrystal interfaces and systematic atomic-scale analysis.

Unraveling the Planar-Globular Transition in Gold Nanoclusters through Evolutionary Search.

Au nanoclusters are of technological relevance for catalysis, photonics, sensors, and of fundamental scientific interest owing to planar to globular structural transformation at an anomalously high number of atoms i.e. in the range 12-14. The nature and causes of this transition remain a mystery. In order to unravel this conundrum, high throughput density functional theory (DFT) calculations, coupled with a global structural optimization scheme based on a modified genetic algorithm (GA) are conducted. More than 20,000 Au12, Au13, and Au14 nanoclusters are evaluated. With any DFT functional, globular and planar structures coexist across the size range of interest. The planar-globular transition is gradual at room temperature rather than a sharp transition as previously believed. The effects of anionicity, s-d band hybridization and long range interactions on the dimensional transition are quantified by using the structures adjacent to the minima. Anionicity marginally changes the relative stability of the clusters. The degree of s-d hybridization is varied via changing the Hubbard U value which corroborate that s-d hybridization alone does not stabilize planar structures. van der Waals interactions, on the other hand, stabilize globular structures. These results elucidate the balance between the different reasons of the dimensional transition in gold nanoclusters.

Oxidation-assisted ductility of aluminium nanowires.

Oxidation can drastically change mechanical properties of nanostructures that typically have large surface-to-volume ratios. However, the underlying mechanisms describing the effect oxidation has on the mechanical properties of nanostructures have yet to be characterized. Here we use reactive molecular dynamics and show that the oxidation enhances the aluminium nanowire ductility, and the oxide shell exhibits superplastic behaviour. The oxide shell decreases the aluminium dislocation nucleation stress by increasing the activation volume and the number of nucleation sites. Superplasticity of the amorphous oxide shell is due to viscous flow as a result of healing of the broken aluminium-oxygen bonds by oxygen diffusion, below a critical strain rate. The interplay between the strain rate and oxidation rate is not only essential for designing nanodevices in ambient environments, but also controls interface properties in large-scale deformation processes.