Subnanometer Scale Mapping of Hydrogen Doping in Vanadium Dioxide.

Hydrogen donor doping of correlated electron systems such as vanadium dioxide (VO2) profoundly modifies the ground state properties. The electrical behavior of HxVO2 is strongly dependent on the hydrogen concentration; hence, atomic scale control of the doping process is necessary. It is however a nontrivial problem to quantitatively probe the hydrogen distribution in a solid matrix. As hydrogen transfers its sole electron to the material, the ionization mechanism is suppressed. In this study, a methodology mapping the doping distribution at subnanometer length scale is demonstrated across a HxVO2 thin film focusing on the oxygen-hydrogen bonds using electron energy loss spectroscopy (EELS) coupled with first-principles EELS calculations. The hydrogen distribution was revealed to be nonuniform along the growth direction and between different VO2 grains, calling for intricate hydrogenation mechanisms. Our study points to a powerful approach to quantitatively map dopant distribution in quantum materials relevant to energy and information sciences.

Ab Initio-Based Bond Order Potential for Arsenene Polymorphs Developed via Hierarchical Reinforcement Learning.

Arsenene, a less-explored two-dimensional material, holds the potential for applications in wearable electronics, memory devices, and quantum systems. This study introduces a bond-order potential model with Tersoff formalism, the ML-Tersoff, which leverages multireward hierarchical reinforcement learning (RL), trained on an ab initio data set. This data set covers a spectrum of properties for arsenene polymorphs, enhancing our understanding of its mechanical and thermal behaviors without the complexities of traditional models requiring multiple parameter sets. Our RL strategy utilizes decision trees coupled with a hierarchical reward strategy to accelerate convergence in high-dimensional continuous search spaces. Unlike the Stillinger-Weber approach, which demands separate formalisms for buckled and puckered forms, the ML-Tersoff model concurrently captures multiple properties of the two polymorphs by effectively representing the local environment, thereby avoiding the need for different atomic types. We apply the ML model to understand the mechanical and thermal properties of the arsenene polymorphs and nanostructures. We observe an inverse relationship between the critical strain and temperature in arsenene. Thermal conductivity calculations in nanosheets show good agreement with ab initio data, reflecting a decrease in thermal conductivity attributable to increased anharmonic effects at higher temperatures. We also apply the model to predict the thermal behavior of arsenene nanotubes.

Perovskite nickelates as electric-field sensors in salt water.

Designing materials to function in harsh environments, such as conductive aqueous media, is a problem of broad interest to a range of technologies, including energy, ocean monitoring and biological applications. The main challenge is to retain the stability and morphology of the material as it interacts dynamically with the surrounding environment. Materials that respond to mild stimuli through collective phase transitions and amplify signals could open up new avenues for sensing. Here we present the discovery of an electric-field-driven, water-mediated reversible phase change in a perovskite-structured nickelate, SmNiO3. This prototypical strongly correlated quantum material is stable in salt water, does not corrode, and allows exchange of protons with the surrounding water at ambient temperature, with the concurrent modification in electrical resistance and optical properties being capable of multi-modal readout. Besides operating both as thermistors and pH sensors, devices made of this material can detect sub-volt electric potentials in salt water. We postulate that such devices could be used in oceanic environments for monitoring electrical signals from various maritime vessels and sea creatures.

Reinforcement learning based hybrid bond-order coarse-grained interatomic potentials for exploring mesoscale aggregation in liquid-liquid mixtures.

Exploring mesoscopic physical phenomena has always been a challenge for brute-force all-atom molecular dynamics simulations. Although recent advances in computing hardware have improved the accessible length scales, reaching mesoscopic timescales is still a significant bottleneck. Coarse-graining of all-atom models allows robust investigation of mesoscale physics with a reduced spatial and temporal resolution but preserves desired structural features of molecules, unlike continuum-based methods. Here, we present a hybrid bond-order coarse-grained forcefield (HyCG) for modeling mesoscale aggregation phenomena in liquid-liquid mixtures. The intuitive hybrid functional form of the potential offers interpretability to our model, unlike many machine learning based interatomic potentials. We parameterize the potential with the continuous action Monte Carlo Tree Search (cMCTS) algorithm, a reinforcement learning (RL) based global optimizing scheme, using training data from all-atom simulations. The resulting RL-HyCG correctly describes mesoscale critical fluctuations in binary liquid-liquid extraction systems. cMCTS, the RL algorithm, accurately captures the mean behavior of various geometrical properties of the molecule of interest, which were excluded from the training set. The developed potential model along with the RL-based training workflow could be applied to explore a variety of other mesoscale physical phenomena that are typically inaccessible to all-atom molecular dynamics simulations.

Atomic Cross-Talk at the Interface: Enhanced Lubricity and Wear and Corrosion Resistance in Sub 2 nm Hybrid Overcoats via Strengthened Interface Chemistry.

Friction, wear, and corrosion remain the major causes of premature failure of diverse systems including hard-disk drives (HDDs). To enhance the areal density of HDDs beyond 1 Tb/in2, the necessary low friction and high wear and corrosion resistance characteristics with sub 2 nm overcoats remain unachievable. Here we demonstrate that atom cross-talk not only manipulates the interface chemistry but also strengthens the tribological and corrosion properties of sub 2 nm overcoats. High-affinity (HA) atomically thin (∼0.4 nm) interlayers (ATIs, XHA), namely Ti, Si, and SiNx, are sandwiched between the hard-disk media and 1.5 nm thick carbon (C) overlayer to develop interface-enhanced sub 2 nm hybrid overcoats that consistently outperform a thicker conventional commercial overcoat (≥2.7 nm), with the C/SiNx bilayer overcoat bettering all other <2 nm thick overcoats. These hybrid overcoats can enable the development of futuristic 2-4 Tb/in2 areal density HDDs and can transform various moving-mechanical-system based technologies.

Efficient Probabilistic Computing with Stochastic Perovskite Nickelates.

Probabilistic computing has emerged as a viable approach to solve hard optimization problems. Devices with inherent stochasticity can greatly simplify their implementation in electronic hardware. Here, we demonstrate intrinsic stochastic resistance switching controlled via electric fields in perovskite nickelates doped with hydrogen. The ability of hydrogen ions to reside in various metastable configurations in the lattice leads to a distribution of transport gaps. With experimentally characterized p-bits, a shared-synapse p-bit architecture demonstrates highly parallelized and energy-efficient solutions to optimization problems such as integer factorization and Boolean satisfiability. The results introduce perovskite nickelates as scalable potential candidates for probabilistic computing and showcase the potential of light-element dopants in next-generation correlated semiconductors.

Carbon-based tribofilms from lubricating oils.

Moving mechanical interfaces are commonly lubricated and separated by a combination of fluid films and solid 'tribofilms', which together ensure easy slippage and long wear life. The efficacy of the fluid film is governed by the viscosity of the base oil in the lubricant; the efficacy of the solid tribofilm, which is produced as a result of sliding contact between moving parts, relies upon the effectiveness of the lubricant's anti-wear additive (typically zinc dialkyldithiophosphate). Minimizing friction and wear continues to be a challenge, and recent efforts have focused on enhancing the anti-friction and anti-wear properties of lubricants by incorporating inorganic nanoparticles and ionic liquids. Here, we describe the in operando formation of carbon-based tribofilms via dissociative extraction from base-oil molecules on catalytically active, sliding nanometre-scale crystalline surfaces, enabling base oils to provide not only the fluid but also the solid tribofilm. We study nanocrystalline catalytic coatings composed of nitrides of either molybdenum or vanadium, containing either copper or nickel catalysts, respectively. Structurally, the resulting tribofilms are similar to diamond-like carbon. Ball-on-disk tests at contact pressures of 1.3 gigapascals reveal that these tribofilms nearly eliminate wear, and provide lower friction than tribofilms formed with zinc dialkyldithiophosphate. Reactive and ab initio molecular-dynamics simulations show that the catalytic action of the coatings facilitates dehydrogenation of linear olefins in the lubricating oil and random scission of their carbon-carbon backbones; the products recombine to nucleate and grow a compact, amorphous lubricating tribofilm.

Removal of Non-Specifically Bound Proteins Using Rayleigh Waves Generated on ST-Quartz Substrates.

Label-free biosensors are plagued by the issue of non-specific protein binding which negatively affects sensing parameters such as sensitivity, selectivity, and limit-of-detection. In the current work, we explore the possibility of using the Rayleigh waves in ST-Quartz devices to efficiently remove non-specifically bound proteins via acoustic streaming. A coupled-field finite element (FE) fluid structure interaction (FSI) model of a surface acoustic wave (SAW) device based on ST-Quartz substrate in contact with a liquid loading was first used to predict trends in forces related to SAW-induced acoustic streaming. Based on model predictions, it is found that the computed SAW body force is sufficient to overcome adhesive forces between particles and a surface while lift and drag forces prevent reattachment for a range of SAW frequencies. We further performed experiments to validate the model predictions and observe that the excitation of Rayleigh SAWs removed non-specifically bound (NSB) antigens and antibodies from sensing and non-sensing regions, while rinsing and blocking agents were ineffective. An amplified RF signal applied to the device input disrupted the specific interactions between antigens and their capture antibody as well. ST-quartz allows propagation of Rayleigh and leaky SH-SAW waves in orthogonal directions. Thus, the results reported here could allow integration of three important biosensor functions on a single chip, i.e., removal of non-specific binding, mixing, and sensing in the liquid phase.

One-Dimensional Lateral Force Anisotropy at the Atomic Scale in Sliding Single Molecules on a Surface.

Using a q+ atomic force microscopy at low temperature, a sexiphenyl molecule is slid across an atomically flat Ag(111) surface along the direction parallel to its molecular axis and sideways to the axis. Despite identical contact area and underlying surface geometry, the lateral force required to move the molecule in the direction parallel to its molecular axis is found to be about half of that required to move it sideways. The origin of the lateral force anisotropy observed here is traced to the one-dimensional shape of the molecule, which is further confirmed by molecular dynamics simulations. We also demonstrate that scanning tunneling microscopy can be used to determine the comparative lateral force qualitatively. The observed one-dimensional lateral force anisotropy may have important implications in atomic scale frictional phenomena on materials surfaces.

Angstrom-Scale Transparent Overcoats: Interfacial Nitrogen-Driven Atomic Intermingling Promotes Lubricity and Surface Protection of Ultrathin Carbon.

Lubricity, a phenomenon which enables the ease of motion of objects, and wear resistance, which minimizes material damage or degradation, are important fundamental characteristics for sustainable technology developments. Ultrathin coatings that promote lubricity and wear resistance are of huge importance for a number of applications, including magnetic storage and micro-/nanoelectromechanical systems. Conventional ultrathin coatings have, however, reached their limit. Graphene-based materials that have shown promise to reduce friction and wear have many intrinsic limitations such as high temperature and substrate-specific growth. To address these concerns, a great deal of research is currently ongoing to optimize graphene-based materials. Here we discover that angstrom-thick carbon (8 Å) significantly reduces interfacial friction and wear. This lubricant shows ultrahigh optical transparency and can be directly deposited on a wide range of surfaces at room temperature. Experiments combined with molecular dynamics simulations reveal that the lubricating efficacy of 8 Å carbon is further improved via interfacial nitrogen.

Strongly correlated perovskite lithium ion shuttles.

Solid-state ion shuttles are of broad interest in electrochemical devices, nonvolatile memory, neuromorphic computing, and biomimicry utilizing synthetic membranes. Traditional design approaches are primarily based on substitutional doping of dissimilar valent cations in a solid lattice, which has inherent limits on dopant concentration and thereby ionic conductivity. Here, we demonstrate perovskite nickelates as Li-ion shuttles with simultaneous suppression of electronic transport via Mott transition. Electrochemically lithiated SmNiO3 (Li-SNO) contains a large amount of mobile Li+ located in interstitial sites of the perovskite approaching one dopant ion per unit cell. A significant lattice expansion associated with interstitial doping allows for fast Li+ conduction with reduced activation energy. We further present a generalization of this approach with results on other rare-earth perovskite nickelates as well as dopants such as Na+ The results highlight the potential of quantum materials and emergent physics in design of ion conductors.

Creation of Single-Photon Emitters in WSe2 Monolayers Using Nanometer-Sized Gold Tips.

Due to their tunable bandgaps and strong spin-valley locking, transition metal dichalcogenides constitute a unique platform for hosting single-photon emitters. Here, we present a versatile approach for creating bright single-photon emitters in WSe2 monolayers by the deposition of gold nanostars. Our molecular dynamics simulations reveal that the formation of the quantum emitters is likely caused by the highly localized strain fields created by the sharp tips of the gold nanostars. The surface plasmon modes supported by the gold nanostars can change the local electromagnetic fields in the vicinity of the quantum emitters, leading to their enhanced emission intensities. Moreover, by correlating the emission energies and intensities of the quantum emitters, we are able to associate them with two types of strain fields and derive the existence of a low-lying dark state in their electronic structures. Our findings are highly relevant for the development and understanding of single-photon emitters in transition metal dichalcogenide materials.

Slippery and Wear-Resistant Surfaces Enabled by Interface Engineered Graphene.

Friction and wear remain the primary cause of mechanical energy dissipation and system failure. Recent studies reveal graphene as a powerful solid lubricant to combat friction and wear. Most of these studies have focused on nanoscale tribology and have been limited to a few specific surfaces. Here, we uncover many unknown aspects of graphene's contact-sliding at micro- and macroscopic tribo-scales over a broader range of surfaces. We discover that graphene's performance reduces for surfaces with increasing roughness. To overcome this, we introduce a new type of graphene/silicon nitride (SiNx, 3 nm) bilayer overcoats that exhibit superior performance compared to native graphene sheets (mono and bilayer), that is, display the lowest microscale friction and wear on a range of tribologically poor flat surfaces. More importantly, two-layer graphene/SiNx bilayer lubricant (<4 nm in total thickness) shows the highest macroscale wear durability on tape-head (topologically variant surface) that exceeds most previous thicker (∼7-100 nm) overcoats. Detailed nanoscale characterization and atomistic simulations explain the origin of the reduced friction and wear arising from these nanoscale coatings. Overall, this study demonstrates that engineered graphene-based coatings can outperform conventional coatings in a number of technologies.

"Teamwork Makes the Dream Work": Tribal Competition Evolutionary Search as a Surrogate for Free-Energy-Based Structural Predictions.

Crystal structure prediction has been a grand challenge in material science owing to the large configurational space that one must explore. Evolutionary (genetic) algorithms coupled with first principles calculations are commonly used in crystal structure prediction to sample the ground and metastable states of materials based on configurational energies. However, crystal structure predictions at finite temperature ( T), pressure ( P), and composition ( X) require a free-energy-based search that is often computationally expensive and tedious. Here, we introduce a new machine-learning workflow for structure prediction that is based on a concept inspired by the evolution of human tribes in primitive society. Our tribal genetic algorithm (GA) combines configurational sampling with evolutionary optimization to accurately predict entropically stabilized phases at finite ( T, P, X), at a computational cost that is an order of magnitude smaller than that required for a free-energy-based search. In a departure from standard GA techniques, the populations of individuals are divided into multiple tribes based on a bond-order fingerprint, and genetic operations are modified to ensure that cluster configurations are sampled adequately to capture entropic contributions. Team competition introduced into the evolutionary process allows winning teams (representing a better set of individuals) to expand their sizes; this translates into a more expanded search of the phase space allowing us to explore solutions near possible global minimum. Each team explores a specific section of the structural phase space and avoids bias on solutions arising from the use of individual populations in a purely energy-based search. We demonstrate the efficacy of our approach by performing the structural prediction of a representative two-dimensional two-body system as well as Lennard-Jones clusters over a range of temperatures up to its melting point. Our approach outperforms the standard GA approaches and enables structural search under "real nonambient conditions" on both bulk systems and finite-sized clusters.

Three-Dimensional Integrated X-ray Diffraction Imaging of a Native Strain in Multi-Layered WSe2.

Emerging two-dimensional (2-D) materials such as transition-metal dichalcogenides show great promise as viable alternatives for semiconductor and optoelectronic devices that progress beyond silicon. Performance variability, reliability, and stochasticity in the measured transport properties represent some of the major challenges in such devices. Native strain arising from interfacial effects due to the presence of a substrate is believed to be a major contributing factor. A full three-dimensional (3-D) mapping of such native nanoscopic strain over micron length scales is highly desirable for gaining a fundamental understanding of interfacial effects but has largely remained elusive. Here, we employ coherent X-ray diffraction imaging to directly image and visualize in 3-D the native strain along the (002) direction in a typical multilayered (∼100-350 layers) 2-D dichalcogenide material (WSe2) on silicon substrate. We observe significant localized strains of ∼0.2% along the out-of-plane direction. Experimentally informed continuum models built from X-ray reconstructions trace the origin of these strains to localized nonuniform contact with the substrate (accentuated by nanometer scale asperities, i.e., surface roughness or contaminants); the mechanically exfoliated stresses and strains are localized to the contact region with the maximum strain near surface asperities being more or less independent of the number of layers. Machine-learned multimillion atomistic models show that the strain effects gain in prominence as we approach a few- to single-monolayer limit. First-principles calculations show a significant band gap shift of up to 125 meV per percent of strain. Finally, we measure the performance of multiple WSe2 transistors fabricated on the same flake; a significant variability in threshold voltage and the "off" current setting is observed among the various devices, which is attributed in part to substrate-induced localized strain. Our integrated approach has broad implications for the direct imaging and quantification of interfacial effects in devices based on layered materials or heterostructures.

Ultrafast Three-Dimensional X-ray Imaging of Deformation Modes in ZnO Nanocrystals.

Imaging the dynamical response of materials following ultrafast excitation can reveal energy transduction mechanisms and their dissipation pathways, as well as material stability under conditions far from equilibrium. Such dynamical behavior is challenging to characterize, especially operando at nanoscopic spatiotemporal scales. In this letter, we use X-ray coherent diffractive imaging to show that ultrafast laser excitation of a ZnO nanocrystal induces a rich set of deformation dynamics including characteristic "hard" or inhomogeneous and "soft" or homogeneous modes at different time scales, corresponding respectively to the propagation of acoustic phonons and resonant oscillation of the crystal. By integrating the 3D nanocrystal structure obtained from the ultrafast X-ray measurements with a continuum thermo-electro-mechanical finite element model, we elucidate the deformation mechanisms following laser excitation, in particular, a torsional mode that generates a 50% greater electric potential gradient than that resulting from the flexural mode. Understanding of the time-dependence of these mechanisms on ultrafast scales has significant implications for development of new materials for nanoscale power generation.

Ultrafast Three-Dimensional Integrated Imaging of Strain in Core/Shell Semiconductor/Metal Nanostructures.

Visualizing the dynamical response of material heterointerfaces is increasingly important for the design of hybrid materials and structures with tailored properties for use in functional devices. In situ characterization of nanoscale heterointerfaces such as metal-semiconductor interfaces, which exhibit a complex interplay between lattice strain, electric potential, and heat transport at subnanosecond time scales, is particularly challenging. In this work, we use a laser pump/X-ray probe form of Bragg coherent diffraction imaging (BCDI) to visualize in three-dimension the deformation of the core of a model core/shell semiconductor-metal (ZnO/Ni) nanorod following laser heating of the shell. We observe a rich interplay of radial, axial, and shear deformation modes acting at different time scales that are induced by the strain from the Ni shell. We construct experimentally informed models by directly importing the reconstructed crystal from the ultrafast experiment into a thermo-electromechanical continuum model. The model elucidates the origin of the deformation modes observed experimentally. Our integrated imaging approach represents an invaluable tool to probe strain dynamics across mixed interfaces under operando conditions.

Subnanometre ligand-shell asymmetry leads to Janus-like nanoparticle membranes.

Self-assembly of nanoparticles at fluid interfaces has emerged as a simple yet efficient way to create two-dimensional membranes with tunable properties. In these membranes, inorganic nanoparticles are coated with a shell of organic ligands that interlock as spacers and provide tensile strength. Although curvature due to gradients in lipid-bilayer composition and protein scaffolding is a key feature of many biological membranes, creating gradients in nanoparticle membranes has been difficult. Here, we show by X-ray scattering that nanoparticle membranes formed at air/water interfaces exhibit a small but significant ∼6 Šdifference in average ligand-shell thickness between their two sides. This affects surface-enhanced Raman scattering and can be used to fold detached free-standing membranes into tubes by exposure to electron beams. Molecular dynamics simulations elucidate the roles of ligand coverage and mobility in producing and maintaining this asymmetry. Understanding this Janus-like membrane asymmetry opens up new avenues for designing nanoparticle superstructures.

Evaluating the Free Energies of Solvation and Electronic Structures of Lithium-Ion Battery Electrolytes.

Adaptive biasing force molecular dynamics simulations and density functional theory calculations were performed to understand the interaction of Li(+) with pure carbonates and ethylene carbonate (EC)-based binary mixtures. The most favorable Li carbonate cluster configurations obtained from molecular dynamics simulations were subjected to detailed structural and thermochemistry calculations on the basis of the M06-2X/6-311++G(d,p) level of theory. We report the ranking of these electrolytes on the basis of the free energies of Li-ion solvation in carbonates and EC-based mixtures. A strong local tetrahedral order involving four carbonates around the Li(+) was seen in the first solvation shell. Thermochemistry calculations revealed that the enthalpy of solvation and the Gibbs free energy of solvation of the Li(+) ion with carbonates are negative and suggested the ion-carbonate complexation process to be exothermic and spontaneous. Natural bond orbital analysis indicated that Li(+) interacts with the lone pairs of electrons on the carbonyl oxygen atom in the primary solvation sphere. These interactions lead to an increase in the carbonyl (C=O) bond lengths, as evidenced by a redshift in the vibrational frequencies [ν(C=O)] and a decrease in the electron density values at the C=O bond critical points in the primary solvation sphere. Quantum theory of atoms in molecules, localized molecular orbital energy decomposition analysis (LMO-EDA), and noncovalent interaction plots revealed the electrostatic nature of the Li(+) ion interactions with the carbonyl oxygen atoms in these complexes. On the basis of LMO-EDA, the strongest attractive interaction in these complexes was found to be the electrostatic interaction followed by polarization, dispersion, and exchange interactions. Overall, our calculations predicted EC and a binary mixture of EC/dimethyl carbonate to be appropriate electrolytes for Li-ion batteries, which complies with experiments and other theoretical results.

Electronic Structure Insights into the Solvation of Magnesium Ions with Cyclic and Acyclic Carbonates.

A computational framework to rank the solvation behavior of Mg(2+) in carbonates by using molecular dynamics simulations and density functional theory is reported. Based on the binding energies and enthalpies of solvation calculated at the M06-2X/6-311++G(d,p) level of theory and the free energies of solvation from ABF-MD simulations, we find that ethylene carbonate (EC) and the ethylene carbonate:propylene carbonate (EC:PC) binary mixture are the best carbonate solvents for interacting with Mg(2+) . Natural bond orbital and quantum theory of atoms in molecules analyses support the thermochemistry calculations with the highest values of charge transfer, perturbative stabilization energies, electron densities, and Wiberg bond indices being observed in the Mg(2+) (EC) and Mg(2+) (EC:PC) complexes. The plots of the noncovalent interactions indicate that those responsible for the formation of Mg(2+) carbonate complexes are strong-to-weak attractive interactions, depending on the regions that are interacting. Finally, density of state calculations indicate that the interactions between Mg(2+) and the carbonate solvents affects the HOMO and LUMO states of all carbonate solvents and moves them to more negative energy values.