Project description:Metallic glasses (MGs) typically have high yield strength while low ductility, and the latter is commonly considered as the Achilles' heel of MGs. Elucidate the mechanism for such low ductility becomes the research focus of this field. With molecular level simulations, we show the degree of short-range order (SRO) of atomic structure for brittle Fe-based glass decreases dramatically during the stretch, while mild change occurs in ductile Zr-based glass. The reformation capability for SRO and their medium-range connections is found to be the primary characteristics to differentiate the deformability between the two metallic glasses. We suspect that, in addition to the strength of networks formed by SRO structure, the reformation capability to reform SRO networks also plays the key role in regulating the ductility in metallic glasses. Our study provides important insights into the understanding about the mechanisms accounting for ductility or brittleness of bulk metallic glasses.
Project description:Glasses formed by physical vapor deposition (PVD) are an interesting new class of materials, exhibiting properties thought to be equivalent to those of glasses aged for thousands of years. Exerting control over the structure and properties of PVD glasses formed with different types of glass-forming molecules is now an emerging challenge. In this work, we study coarse-grained models of organic glass formers containing fluorocarbon tails of increasing length, corresponding to an increased tendency to form microstructures. We use simulated PVD to examine how the presence of the microphase-separated domains in the supercooled liquid influences the ability to form stable glasses. This model suggests that increasing molecule tail length results in decreased thermodynamic stability of the molecules in PVD films. The reduced stability is further linked to the reduced ability of these molecules to equilibrate at the free surface during PVD. We find that, as the tail length is increased, the relaxation times near the surface of the supercooled equilibrium liquid films of these molecules are slowed and become essentially bulk-like, due to the segregation of the fluorocarbon tails to the free surface. Surface diffusion is also markedly reduced due to clustering of the molecules at the surface. Based on these results, we propose a trapping mechanism where tails are unable to move between local phase-separated domains on the relevant deposition time scales.
Project description:Physical vapor deposition is commonly used to prepare organic glasses that serve as the active layers in light-emitting diodes, photovoltaics, and other devices. Recent work has shown that orienting the molecules in such organic semiconductors can significantly enhance device performance. We apply a high-throughput characterization scheme to investigate the effect of the substrate temperature (Tsubstrate) on glasses of three organic molecules used as semiconductors. The optical and material properties are evaluated with spectroscopic ellipsometry. We find that molecular orientation in these glasses is continuously tunable and controlled by Tsubstrate/Tg, where Tg is the glass transition temperature. All three molecules can produce highly anisotropic glasses; the dependence of molecular orientation upon substrate temperature is remarkably similar and nearly independent of molecular length. All three compounds form "stable glasses" with high density and thermal stability, and have properties similar to stable glasses prepared from model glass formers. Simulations reproduce the experimental trends and explain molecular orientation in the deposited glasses in terms of the surface properties of the equilibrium liquid. By showing that organic semiconductors form stable glasses, these results provide an avenue for systematic performance optimization of active layers in organic electronics.
Project description:The van der Waals heterostructures of two-dimensional (2D) atomic crystals constitute a new paradigm in nanoscience. Hybrid devices of graphene with insulating 2D hexagonal boron nitride (h-BN) have emerged as promising nanoelectronic architectures through demonstrations of ultrahigh electron mobilities and charge-based tunnel transistors. Here, we expand the functional horizon of such 2D materials demonstrating the quantum tunneling of spin polarized electrons through atomic planes of CVD grown h-BN. We report excellent tunneling behavior of h-BN layers together with tunnel spin injection and transport in graphene using ferromagnet/h-BN contacts. Employing h-BN tunnel contacts, we observe enhancements in both spin signal amplitude and lifetime by an order of magnitude. We demonstrate spin transport and precession over micrometer-scale distances with spin lifetime up to 0.46 nanosecond. Our results and complementary magnetoresistance calculations illustrate that CVD h-BN tunnel barrier provides a reliable, reproducible and alternative approach to address the conductivity mismatch problem for spin injection into graphene.
Project description:In this work, we report development of hybrid nanostructures of metal nanoparticles (NP) and carbon nanostructures with strong potential for catalysis, sensing, and energy applications. First, the etched silicon wafer substrates were passivated for subsequent electrochemical (EC) processing through grafting of nitro phenyl groups using para-nitrobenzene diazonium (PNBT). The X-ray photoelectron spectroscope (XPS) and atomic force microscope (AFM) studies confirmed presence of few layers. Cobalt-based nanoparticles were produced over dip or spin coated Nafion films under different EC reduction conditions, namely CoSO₄ salt concentration (0.1 M, 1 mM), reduction time (5, 20 s), and indirect or direct EC reduction route. Extensive AFM examination revealed NP formation with different attributes (size, distribution) depending on electrochemistry conditions. While relatively large NP with >100 nm size and bimodal distribution were obtained after 20 s EC reduction in H₃BO₃ following Co2+ ion uptake, ultrafine NP (<10 nm) could be produced from EC reduction in CoSO₄ and H₃BO₃ mixed solution with some tendency to form oxides. Different carbon nanostructures including few-walled or multiwalled carbon nanotubes (CNT) and carbon nanosheets were grown in a C₂H₂/NH₃ plasma using the plasma-enhanced chemical vapor deposition technique. The devised processing routes enable size controlled synthesis of cobalt nanoparticles and metal/carbon hybrid nanostructures with unique microstructural features.
Project description:We performed scanning thermal microscopy measurements on single layers of chemical-vapor-deposited (CVD) graphene supported by different substrates, namely, SiO2, Al2O3, and PET using a double-scan technique to remove the contribution to the heat flux through the air and the cantilever. Then, by adopting a simple lumped-elements model, we developed a new method that allows determining, through a multistep numerical analysis, the equivalent thermal properties of thermally conductive coatings of nanometric thickness. In this specific case we found that our CVD graphene is "thermally equivalent", for heat injection perpendicular to the graphene planes, to a coating material of conductivity k eff = 2.5 ± 0.3 W/m K and thickness t eff = 3.5 ± 0.3 nm in perfect contact with the substrate. For the SiO2 substrate, we also measured stacks made of 2- and 4-CVD monolayers, and we found that the effective thermal conductivity increases with increasing number of layers and, with a technologically achievable number of layers, is expected to be comparable to that of 1 order of magnitude-thicker metallic thin films. This study provides a powerful method for characterizing the thermal properties of graphene in view of several thermal management applications.
Project description:Establishing ultimate spin current efficiency in graphene over industry-standard substrates can facilitate research and development exploration of spin current functions and spin sensing. At the same time, it can resolve core issues in spin relaxation physics while addressing the skepticism of graphene's practicality for planar spintronic applications. In this work, we reveal an exceptionally long spin communication capability of 45 μm and highest to date spin diffusion length of 13.6 μm in graphene on SiO2/Si at room temperature. Employing commercial chemical vapor deposited (CVD) graphene, we show how contact-induced surface charge transfer doping and device doping contributions, as well as spin relaxation, can be quenched in extremely long spin channels and thereby enable unexpectedly long spin diffusion lengths in polycrystalline CVD graphene. Extensive experiments show enhanced spin transport and precession in multiple longest channels (36 and 45 μm) that reveal the highest spin lifetime of ∼2.5-3.5 ns in graphene over SiO2/Si, even under ambient conditions. Such performance, made possible due to our devices approaching the intrinsic spin-orbit coupling of ∼20 μeV in graphene, reveals the role of the D'yakonov-Perel' spin relaxation mechanism in graphene channels as well as contact regions. Our record demonstration, fresh device engineering, and spin relaxation insights unlock the ultimate spin current capabilities of graphene on SiO2/Si, while the robust high performance of commercial CVD graphene can proliferate research and development of innovative spin sensors and spin computing circuits.
Project description:In this article, we used a two-step chemical vapor deposition (CVD) method to synthesize methylammonium lead-tin triiodide perovskite films, MAPb1-xSnxI3, with x varying from 0 to 1. We successfully controlled the concentration of Sn in the perovskite films and used Rutherford backscattering spectroscopy (RBS) to quantify the composition of the precursor films for conversion into perovskite films. According to the RBS results, increasing the SnCl2 source amount in the reaction chamber translate into an increase in Sn concentration in the films. The crystal structure and the optical properties of perovskite films were examined by X-ray diffraction (XRD) and UV-Vis spectrometry. All the perovskite films depicted similar XRD patterns corresponding to a tetragonal structure with I4cm space group despite the precursor films having different crystal structures. The increasing concentration of Sn in the perovskite films linearly decreased the unit volume from about 988.4 Å3 for MAPbI3 to about 983.3 Å3 for MAPb0.39Sn0.61I3, which consequently influenced the optical properties of the films manifested by the decrease in energy bandgap (Eg) and an increase in the disorder in the band gap. The SEM micrographs depicted improvements in the grain size (0.3-1 µm) and surface coverage of the perovskite films compared with the precursor films.
Project description:Amorphous films and coatings are rapidly growing in importance. Yet, there is a dearth of high-quality structural data on sub-micron films. Not understanding how these materials assemble at atomic scale limits fundamental insights needed to improve their performance. Here, we use grazing-incidence x-ray total scattering measurements to examine the atomic structure of the top 50-100?nm of Ta2O5 films; mirror coatings that show high promise to significantly improve the sensitivity of the next generation of gravitational-wave detectors. Our measurements show noticeable changes well into medium range, not only between crystalline and amorphous, but also between as-deposited, annealed and doped amorphous films. It is a further challenge to quickly translate the structural information into insights into mechanisms of packing and disorder. Here, we illustrate a modeling approach that allows translation of observed structural features to a physically intuitive packing of a primary structural unit based on a kinked Ta-O-Ta backbone. Our modeling illustrates how Ta-O-Ta units link to form longer 1D chains and even 2D ribbons, and how doping and annealing influences formation of 2D order. We also find that all the amorphousTa2O5 films studied in here are not just poorly crystalline but appear to lack true 3D order.
Project description:Traditional fully-deterministic algorithms, which rely on physical equations and mathematical models, are the backbone of many scientific disciplines for decades. These algorithms are based on well-established principles and laws of physics, enabling a systematic and predictable approach to problem-solving. On the other hand, AI-based strategies emerge as a powerful tool for handling vast amounts of data and extracting patterns and relationships that might be challenging to identify through traditional algorithms. Here, we bridge these two realms by using AI to find an optimal mapping of meteorological features predicted two days ahead by the state-of-the-art numerical weather prediction model by the European Centre for Medium-range Weather Forecasts (ECMWF) into lightning flash occurrence. The prediction capability of the resulting AI-enhanced algorithm turns out to be significantly higher than that of the fully-deterministic algorithm employed in the ECMWF model. A remarkable Recall peak of about 95% within the 0-24 h forecast interval is obtained. This performance surpasses the 85% achieved by the ECMWF model at the same Precision of the AI algorithm.