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At least 1,675 records · Page 93

On the solid phase crystallization of In{sub 2}O{sub 3}:H transparent conductive oxide films prepared by atomic layer deposition

Hydrogen-doped indium oxide (In{sub 2}O{sub 3}:H) has emerged as a highly transparent and conductive oxide, finding its application in a multitude of optoelectronic devices. Recently, we have reported on an atomic layer deposition (ALD) process to prepare high quality In{sub 2}O{sub 3}:H. This process consists of ALD of In{sub 2}O{sub 3}:H films at 100 °C, followed by a solid phase crystallization step at 150–200 °C. In this work, we report on a detailed electron microscopy study of this crystallization process which reveals new insights into the crucial aspects for achieving the large grain size and associated excellent properties of the material. The key finding is that the best optoelectronic properties are obtained by preparing the films at the lowest possible temperature prior to post-deposition annealing. Electron microscopy imaging shows that such films are mostly amorphous, but feature a very low density of embedded crystallites. Upon post-deposition annealing, crystallization proceeds merely from isotropic crystal grain growth of these embedded crystallites rather than by the formation of additional crystallites. The relatively high hydrogen content of 4.2 at. % in these films is thought to cause the absence of additional nucleation, thereby rendering the final grain size and optoelectronic properties solely dependent on the density of embedded crystallites. The temperature-dependent grain growth rate has been determined, from which an activation energy of (1.39 ± 0.04) eV has been extracted. Finally, on the basis of the observed crystallization mechanism, a simple model to fully describe the crystallization process has been developed. This model has been validated with a numerical implementation thereof, which accurately predicts the observed temperature-dependent crystallization behaviour.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Polysulfone shock compressed above the decomposition threshold: Velocimetry and modeling of two-wave structures

Polysulfone was shock loaded to pressures of 14.6–26.2 GPa in a series of gas gun-driven plate-impact experiments measuring material response with embedded electro-magnetic particle velocity gauges and optical velocimetry. The embedded electro-magnetic particle velocity gauges did not show a distinct two-wave structure but did show rounding that suggested a reaction but not a distinct separation of the reactants and product waves. In contrast, the transmission experiments fielded with optical velocimetry, with product pressures ranging from 21.0 to 26.2 GPa, showed well-defined two-wave structures due to shock-driven chemical decomposition of the polymer to products at a higher density. Distinct two-wave structures have not previously been published in literature during polymer compression; here, we observed these two-wave structures at both the polymer/lithium fluoride and polymer/polymethylmethacrylate interfaces. Hydrodynamic simulations of the experiments were performed using a newly constructed SESAME equation of state (EOS) for the polymer, a thermochemical EOS for the decomposition products, and an Arrhenius reaction rate model for the kinetics of the reaction. Finally, simulation results also demonstrated two-wave structures but were unable to quantitatively reproduce either the embedded gauge or optical velocimetry data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Normal stability of slow manifolds in nearly periodic Hamiltonian systems

Kruskal [J. Math. Phys. 3, 806 (1962)] showed that each nearly periodic dynamical system admits a formal U(1) symmetry, generated by the so-called roto-rate. We prove that such systems also admit nearly invariant manifolds of each order, near which rapid oscillations are suppressed. We study the nonlinear normal stability of these slow manifolds for nearly periodic Hamiltonian systems on barely symplectic manifolds—manifolds equipped with closed, non-degenerate 2-forms that may be degenerate to leading order. In particular, we establish a sufficient condition for long-term normal stability based on second derivatives of the well-known adiabatic invariant. We use these results to investigate the problem of embedding guiding center dynamics of a magnetized charged particle as a slow manifold in a nearly periodic system. Here, we prove that one previous embedding and two new embeddings enjoy long-term normal stability and thereby strengthen the theoretical justification for these models.

97 MATHEMATICS AND COMPUTING↗

Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics

We address a three-tier numerical framework based on nonlinear manifold learning for the forecasting of high-dimensional time series, relaxing the “curse of dimensionality” related to the training phase of surrogate/machine learning models. At the first step, we embed the high-dimensional time series into a reduced low-dimensional space using nonlinear manifold learning (local linear embedding and parsimonious diffusion maps). Then, we construct reduced-order surrogate models on the manifold (here, for our illustrations, we used multivariate autoregressive and Gaussian process regression models) to forecast the embedded dynamics. Finally, we solve the pre-image problem, thus lifting the embedded time series back to the original high-dimensional space using radial basis function interpolation and geometric harmonics. The proposed numerical data-driven scheme can also be applied as a reduced-order model procedure for the numerical solution/propagation of the (transient) dynamics of partial differential equations (PDEs). In conclusion, we assess the performance of the proposed scheme via three different families of problems: (a) the forecasting of synthetic time series generated by three simplistic linear and weakly nonlinear stochastic models resembling electroencephalography signals, (b) the prediction/propagation of the solution profiles of a linear parabolic PDE and the Brusselator model (a set of two nonlinear parabolic PDEs), and (c) the forecasting of a real-world data set containing daily time series of ten key foreign exchange rates spanning the time period 3 September 2001–29 October 2020.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Quantum computing for fusion energy science applications

This is a review of recent research exploring and extending present-day quantum computing capabilities for fusion energy science applications. We begin with a brief tutorial on both ideal and open quantum dynamics, universal quantum computation, and quantum algorithms. Then, we explore the topic of using quantum computers to simulate both linear and nonlinear dynamics in greater detail. Because quantum computers can only efficiently perform linear operations on the quantum state, it is challenging to perform nonlinear operations that are generically required to describe the nonlinear differential equations of interest. In this work, we extend previous results on embedding nonlinear systems within linear systems by explicitly deriving the connection between the Koopman evolution operator, the Perron–Frobenius evolution operator, and the Koopman–von Neumann evolution (KvN) operator. We also explicitly derive the connection between the Koopman and Carleman approaches to embedding. Extension of the KvN framework to the complex-analytic setting relevant to Carleman embedding, and the proof that different choices of complex analytic reproducing kernel Hilbert spaces depend on the choice of Hilbert space metric are covered in the appendixes. Finally, we conclude with a review of recent quantum hardware implementations of algorithms on present-day quantum hardware platforms that may one day be accelerated through Hamiltonian simulation. We discuss the simulation of toy models of wave–particle interactions through the simulation of quantum maps and of wave–wave interactions important in nonlinear plasma dynamics.

Joseph, I. (ORCID:0000000255400840)↗

Improving nanoparticle superlattice stability with deformable polymer gels

The self-assembly of colloidal nanoparticles into ordered superlattices typically uses dynamic interactions to govern particle crystallization, as these non-permanent bonds prevent the formation of kinetically trapped, disordered aggregates. However, while the use of reversible bonding is critical in the formation of highly ordered particle arrangements, dynamic interactions also inherently make the structures more prone to disassembly or disruption when subjected to different environmental stimuli. Thus, there is typically a trade-off between the ability to initially form an ordered colloidal material and the ability of that material to retain its order under different conditions. Here, we present a method for embedding colloidal nanoparticle superlattices into a polymer gel matrix. This encapsulation strategy physically prevents the nanoparticles from dissociating upon heating, drying, or the introduction of chemicals that would normally disrupt the lattice. However, the use of a gel as the embedding medium still permits further modification of the colloidal nanoparticle lattice by introducing stimuli that deform the gel network (as this deformation in turn alters the nanoparticle lattice structure in a predictable manner). Moreover, encapsulation of the lattice within a gel permits further stabilization into fully solid materials by removing the solvent from the gel or by replacing the solvent with a liquid monomer that can be photopolymerized. This embedding method therefore makes it possible to incorporate ordered colloidal arrays into a polymer matrix as either dynamic or static structures, expanding their potential for use in responsive materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Sculpting crystals one Burgers vector at a time: Toward colloidal lattice robot swarms

Plastic deformation of crystalline materials with isotropic particle attractions proceeds by the creation and migration of dislocations under the influence of external forces. If dislocations are produced and migrated under the action of local forces, then material shape change can occur without the application of surface forces. We investigate how particles with variable diameters can be embedded in colloidal monolayers to produce dislocations on demand. Additionally, we find in simulation that when embedded clusters of variable diameter particles are taken through multiple cycles of swelling and shrinking, large cumulative plastic slip is produced by the creation and biased motion of dislocation pairs in the solid for embedded clusters of particular geometries. In this way, dislocations emitted by these clusters (biased “dislocation emitters”) can be used to reshape colloidal matter. Our results are also applicable to larger-scale swarms of robotic particles that organize into dense ordered two-dimensional (2D) arrangements. We conclude with a discussion of how dislocations fulfill for colloids the role sought by “metamodules” in lattice robotics research and show how successive applications of shear as a unit operation can produce shape change through slicing and swirling.

36 MATERIALS SCIENCE↗

Active doping controls the mode of failure in dense colloidal gels

Mechanical properties of disordered materials are governed by their underlying free energy landscape. In contrast to external fields, embedding a small fraction of active particles within a disordered material generates nonequilibrium internal fields, which can help to circumvent kinetic barriers and modulate the free energy landscape. In this work, we investigate through computer simulations how the activity of active particles alters the mechanical response of deeply annealed polydisperse colloidal gels. We show that the “swim force” generated by the embedded active particles is responsible for determining the mode of mechanical failure, i.e., brittle vs. ductile. We find, and theoretically justify, that at a critical swim force the mechanical properties of the gel decrease abruptly, signaling a change in the mode of mechanical failure. The weakening of the elastic modulus above the critical swim force results from the change in gel porosity and distribution of attractive forces among gel particles, while below the critical swim force, the ductility enhancement is caused by an increase of gel structural disorder. Above the critical swim force, the gel develops a pronounced heterogeneous structure characterized by multiple pore spaces, and the mechanical response is controlled by dynamical heterogeneities. We contrast these results with those of a simulated monodisperse gel that exhibits a nonmonotonic trend of ductility modulation with increasing swim force, revealing a complex interplay between the gel energy landscape and embedded activity.

Zhou, Tingtao (ORCID:000000021766719X)↗

Towards POI-based large-scale land use modeling: spatial scale, semantic granularity, and geographic context

The combination of spatial distribution, semantic characteristics, and sometimes temporal dynamics of POIs inside a geographic region can capture its unique land use characteristics. Most previous studies on POI-based land use modeling research focused on one geographic region and select one spatial scale and semantic granularity for land use characterization. There is a lack of understanding on the impact of spatial scale, semantic granularity, and geographic context on POI-based land use modeling, particularly large-scale land use modeling. In this study, we developed a scalable POI-based land use modeling framework and examined the impact of these three factors on POI-based land use characterization using data from three geographic regions. We developed a unified semantic representation framework for POI semantics that can help fuse heterogeneous POI data sources. Then, by combining POIs with a neural network language model, we developed a spatially explicit approach to learn the embedding representation of POIs and AOIs. We trained multiple supervised classifiers using AOI embeddings as input features to predict AOI land use at different semantic granularities. The classification performance of different land use classes was analyzed and compared across three geographic regions to identify the semantic representativeness of POI-based AOI embedding and the impact of geographic context.

58 GEOSCIENCES↗

Anchor selection study for ocean current turbines

This paper compares anchoring systems suitable for ocean current turbines, specifically those proposed to be installed off the coast of Southeast Florida near latitude 26° 5’ N. This location boasts one of the most energy dense ocean current resources, with a mean kinetic energy flux of approximately 2.34 kW/m2. Seafloor types ranging from unconstrained sediment to high relief ledges were observed during regional benthic surveys for which applicable anchor types would include deadweight, plate, pile, and drag embedment. Numerical simulations of single point moored marine hydrokinetic devices were used to extract anchor loading at a likely deployment location for mooring scopes from 1.25 to 2.0 and turbine rotor diameters between 3–50m. These anchor loading data were used for preliminary sizing of deadweight and driven plate anchors on both cohesionless and cohesive soils. Finally, the capabilities of drag embedment and pile anchors relevant to ocean current turbines are discussed. Multiple types of drag embedment anchors can support all of the predicted loads if adequate sediment exists and the loading is horizontal, while pile and H-type anchors can support all of the evaluated loading scenarios and chain-in-hole anchors can support turbines with rotor diameters up to 30m.

Florida Current↗

A YOUNG STELLAR CLUSTER WITHIN THE RCW41 H II REGION: DEEP NIR PHOTOMETRY AND OPTICAL/NIR POLARIMETRY

The RCW41 star-forming region is embedded within the Vela Molecular Ridge, hosting a massive stellar cluster surrounded by a conspicuous H II region. Understanding the role of interstellar magnetic fields and studying the newborn stellar population is crucial to building a consistent picture of the physical processes acting on this kind of environment. We carried out a detailed study of the interstellar polarization toward RCW41 with data from an optical and near-infrared polarimetric survey. Additionally, deep near-infrared images from the 3.5 meter New Technology Telescope were used to study the photometric properties of the embedded young stellar cluster, revealing several YSO candidates. By using a set of pre-main-sequence isochrones, a mean cluster age in the range 2.5-5.0 million years was determined, and evidence of sequential star formation was revealed. An abrupt decrease in R-band polarization degree was noticed toward the central ionized area, probably due to low grain alignment efficiency caused by the turbulent environment and/or the weak intensity of magnetic fields. The distortion of magnetic field lines exhibits dual behavior, with the mean orientation outside the area approximately following the borders of the star-forming region and directed radially toward the cluster inside the ionized area, in agreement with simulations of expanding H II regions. The spectral dependence of polarization allowed a meaningful determination of the total-to-selective extinction ratio by fittings of the Serkowski relation. Furthermore, a large rotation of polarization angle as a function of wavelength was detected toward several embedded stars.

79 ASTRONOMY AND ASTROPHYSICS↗

On shocks driven by high-mass planets in radiatively inefficient disks. I. Two-dimensional global disk simulations

Recent observations of gaps and non-axisymmetric features in the dust distributions of transition disks have been interpreted as evidence of embedded massive protoplanets. However, comparing the predictions of planet–disk interaction models to the observed features has shown far from perfect agreement. This may be due to the strong approximations used for the predictions. For example, spiral arm fitting typically uses results that are based on low-mass planets in an isothermal gas. In this work, we describe two-dimensional, global, hydrodynamical simulations of disks with embedded protoplanets, with and without the assumption of local isothermality, for a range of planet-to-star mass ratios 1–10 M{sub J} for a 1 M{sub ⊙} star. We use the Pencil Code in polar coordinates for our models. We find that the inner and outer spiral wakes of massive protoplanets (M≳5 M{sub J}) produce significant shock heating that can trigger buoyant instabilities. These drive sustained turbulence throughout the disk when they occur. The strength of this effect depends strongly on the mass of the planet and the thermal relaxation timescale; for a 10 M{sub J} planet embedded in a thin, purely adiabatic disk, the spirals, gaps, and vortices typically associated with planet–disk interactions are disrupted. We find that the effect is only weakly dependent on the initial radial temperature profile. The spirals that form in disks heated by the effects we have described may fit the spiral structures observed in transition disks better than the spirals predicted by linear isothermal theory.

79 ASTRONOMY AND ASTROPHYSICS↗

THE BRIGHTENING OF Re50N: ACCRETION EVENT OR DUST CLEARING?

The luminous Class I protostar HBC 494, embedded in the Orion A cloud, is associated with a pair of reflection nebulae, Re50 and Re50N, which appeared sometime between 1955 and 1979. We have found that a dramatic brightening of Re50N has taken place sometime between 2006 and 2014. This could result if the embedded source is undergoing a FUor eruption. However, the near-infrared spectrum shows a featureless very red continuum, in contrast to the strong CO bandhead absorption displayed by FUors. Such heavy veiling, and the high luminosity of the protostar, is indicative of strong accretion but seemingly not in the manner of typical FUors. We favor the alternative explanation that the major brightening of Re50N and the simultaneous fading of Re50 is caused by curtains of obscuring material that cast patterns of illumination and shadows across the surface of the molecular cloud. This is likely occurring as an outflow cavity surrounding the embedded protostar breaks through to the surface of the molecular cloud. Several Herbig–Haro objects are found in the region.

79 ASTRONOMY AND ASTROPHYSICS↗

Dependency of dynamical ejections of O stars on the masses of very young star clusters

Massive stars can be efficiently ejected from their birth star clusters through encounters with other massive stars. We study how the dynamical ejection fraction of O star systems varies with the masses of very young star clusters, M{sub ecl}, by means of direct N-body calculations. We include diverse initial conditions by varying the half-mass radius, initial mass segregation, initial binary fraction, and orbital parameters of the massive binaries. The results show robustly that the ejection fraction of O star systems exhibits a maximum at a cluster mass of 10{sup 3.5} M{sub ⊙} for all models, even though the number of ejected systems increases with cluster mass. We show that lower mass clusters (M{sub ecl}≈400 M{sub ⊙}) are the dominant sources for populating the Galactic field with O stars by dynamical ejections, considering the mass function of embedded clusters. About 15% (up to ≈38%, depending on the cluster models) of O stars of which a significant fraction are binaries, and which would have formed in a ≈10 Myr epoch of star formation in a distribution of embedded clusters, will be dynamically ejected to the field. Individual clusters may eject 100% of their original O star content. A large fraction of such O stars have velocities up to only 10 km s{sup −1}. Synthesising a young star cluster mass function, it follows, given the stellar-dynamical results presented here, that the observed fractions of field and runaway O stars, and the binary fractions among them, can be well understood theoretically if all O stars form in embedded clusters.

79 ASTRONOMY AND ASTROPHYSICS↗

The effective field theory of dark matter direct detection

We extend and explore the general non-relativistic effective theory of dark matter (DM) direct detection. We describe the basic non-relativistic building blocks of operators and discuss their symmetry properties, writing down all Galilean-invariant operators up to quadratic order in momentum transfer arising from exchange of particles of spin 1 or less. Any DM particle theory can be translated into the coefficients of an effective operator and any effective operator can be simply related to most general description of the nuclear response. We find several operators which lead to novel nuclear responses. These responses differ significantly from the standard minimal WIMP cases in their relative coupling strengths to various elements, changing how the results from different experiments should be compared against each other. Response functions are evaluated for common DM targets — F, Na, Ge, I, and Xe — using standard shell model techniques. We point out that each of the nuclear responses is familiar from past studies of semi-leptonic electroweak interactions, and thus potentially testable in weak interaction studies. Here, we provide tables of the full set of required matrix elements at finite momentum transfer for a range of common elements, making a careful and fully model-independent analysis possible. Finally, we discuss embedding non-relativistic effective theory operators into UV models of dark matter.

dark matter detectors↗

Fermionic systems for quantum information people

The operator algebra of fermionic modes is isomorphic to that of qubits, the difference between them is twofold: the embedding of subalgebras corresponding to mode subsets and multiqubit subsystems on the one hand, and the parity superselection in the fermionic case on the other. We discuss these two fundamental differences extensively, and illustrate these through the Jordan–Wigner representation in a coherent, self-contained, pedagogical way, from the point of view of quantum information theory. Our perspective leads us to develop useful new tools for the treatment of fermionic systems, such as the fermionic (quasi-)tensor product, fermionic canonical embedding, fermionic partial trace, fermionic products of maps and fermionic embeddings of maps. We formulate these by direct, easily applicable formulas, without mode permutations, for arbitrary partitionings of the modes. It is also shown that fermionic reduced states can be calculated by the fermionic partial trace, containing the proper phase factors. We also consider variants of the notions of fermionic mode correlation and entanglement, which can be endowed with the usual, local operation based motivation, if the parity superselection rule is imposed. We also elucidate some other fundamental points, related to joint map extensions, which make the parity superselection inevitable in the description of fermionic systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Multimodal representation learning for predicting molecule–disease relations

Motivation: Predicting molecule–disease indications and side effects is important for drug development and pharmacovigilance. Comprehensively mining molecule–molecule, molecule–disease and disease–disease semantic dependencies can potentially improve prediction performance. Methods: We introduce a Multi-Modal REpresentation Mapping Approach to Predicting molecular-disease relations (M2REMAP) by incorporating clinical semantics learned from electronic health records (EHR) of 12.6 million patients. Specifically, M2REMAP first learns a multimodal molecule representation that synthesizes chemical property and clinical semantic information by mapping molecule chemicals via a deep neural network onto the clinical semantic embedding space shared by drugs, diseases and other common clinical concepts. To infer molecule–disease relations, M2REMAP combines multimodal molecule representation and disease semantic embedding to jointly infer indications and side effects. Results: We extensively evaluate M2REMAP on molecule indications, side effects and interactions. Results show that incorporating EHR embeddings improves performance significantly, for example, attaining an improvement over the baseline models by 23.6% in PRC-AUC on indications and 23.9% on side effects. Further, M2REMAP overcomes the limitation of existing methods and effectively predicts drugs for novel diseases and emerging pathogens. Availability and implementation: The code is available at https://github.com/celehs/M2REMAP, and prediction results are provided at https://shiny.parse-health.org/drugs-diseases-dev/.

59 BASIC BIOLOGICAL SCIENCES↗

Learning nuclear cross sections across the chart of nuclides with graph neural networks

We explore the use of deep learning techniques to learn how nuclear cross sections change as we add or remove protons and neutrons. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime. Our approach follows a two-stage learning framework. First, we apply representation learning to encode cross section data into a latent space using either variational autoencoders (VAEs) or implicit neural representations (INRs). Then, we train graph neural networks (GNNs) on the resulting embeddings to predict missing values across the nuclear chart by leveraging the topological structure of neighboring isotopes. We demonstrate accurate cross section predictions within a 9 × 9 block of missing nuclei. We also find that the optimal GNN training strategy depends on the type of latent representation used, with VAE embeddings performing best under end-to-end optimization in the original space, while INR embeddings achieve better results when the GNN is trained only in the latent space. Furthermore, using clustering algorithms, we map groups of latent vectors into regions of the nuclear chart and show that VAEs and INRs can discover some of the neutron magic numbers. These findings suggest that deep-learning models based on the representation encoding of cross sections combined with graph neural networks hold significant potential in augmenting nuclear theory models, e.g., by providing reliable estimates of covariances of cross sections, including cross-material covariances.

Machine learning↗