Effective conductivity of 2D isotropic two-phase systems in magnetic field

Using the linear fractional transformation, connecting effective conductivities sigma_{e} of isotropic two-phase systems with and without magnetic field, explicit approximate expressions for sigma_{e} in a magnetic field are obtained. They allow to describe sigma_{e} of various inhomogeneous media at arbitrary phase concentrations x and magnetic fields. the x-dependence plots of sigma_e at some values of inhomogeneity and magnetic field are constructed. Their behaviour is qualitatively compatible with the existing experimental data. The obtained results are applicable for different two-phase systems (regular and nonregular as well as random), satisfying the symmetry and self-duality conditions, and admit a direct experimental checking.Comment: 9 pages, 2 figures, Latex2e, small corrections and new figure

Three-dimensionally Ordered Macroporous Structure Enabled Nanothermite Membrane of Mn2O3/Al

Mn2O3 has been selected to realize nanothermite membrane for the first time in the literature. Mn2O3/Al nanothermite has been synthesized by magnetron sputtering a layer of Al film onto three-dimensionally ordered macroporous (3DOM) Mn2O3 skeleton. The energy release is significantly enhanced owing to the unusual 3DOM structure, which ensures Al and Mn2O3 to integrate compactly in nanoscale and greatly increase effective contact area. The morphology and DSC curve of the nanothermite membrane have been investigated at various aluminizing times. At the optimized aluminizing time of 30 min, energy release reaches a maximum of 2.09 kJ∙g−1, where the Al layer thickness plays a decisive role in the total energy release. This method possesses advantages of high compatibility with MEMS and can be applied to other nanothermite systems easily, which will make great contribution to little-known nanothermite research

Passive scalars, random flux, and chiral phase fluids

We study the two-dimensional localization problem for (i) a classical diffusing particle advected by a quenched random mean-zero vorticity field, and (ii) a quantum particle in a quenched random mean-zero magnetic field. Through a combination of numerical and analytic techniques we argue that both systems have extended eigenstates at a special point in the spectrum, $E_c$, where a sublattice decomposition obtains. In a neighborhood of this point, the Lyapunov exponents of the transfer-matrices acquire ratios characteristic of conformal invariance allowing an indirect determination of $1/r$ for the typical spatial decay of eigenstates.Comment: use revtex, two-column, 4 pages, 5 postscript figures, submitted to PR

Nonequilibrium phenomena in high Landau levels

Developments in the physics of 2D electron systems during the last decade have revealed a new class of nonequilibrium phenomena in the presence of a moderately strong magnetic field. The hallmark of these phenomena is magnetoresistance oscillations generated by the external forces that drive the electron system out of equilibrium. The rich set of dramatic phenomena of this kind, discovered in high mobility semiconductor nanostructures, includes, in particular, microwave radiation-induced resistance oscillations and zero-resistance states, as well as Hall field-induced resistance oscillations and associated zero-differential resistance states. We review the experimental manifestations of these phenomena and the unified theoretical framework for describing them in terms of a quantum kinetic equation. The survey contains also a thorough discussion of the magnetotransport properties of 2D electrons in the linear response regime, as well as an outlook on future directions, including related nonequilibrium phenomena in other 2D electron systems.Comment: 60 pages, 41 figure

Interfacial Chemistry in Al/CuO Reactive Nanomaterial and Its Role in Exothermic Reaction.

Interface layers between reactive and energetic materials in nanolaminates or nanoenergetic materials are believed to play a crucial role in the properties of nanoenergetic systems. Typically, in the case of Metastable Interstitial Composite nanolaminates, the interface layer between the metal and oxide controls the onset reaction temperature, reaction kinetics, and stability at low temperature. So far, the formation of these interfacial layers is not well understood for lack of in situ characterization, leading to a poor control of important properties. We have combined in situ infrared spectroscopy and ex situ X-ray photoelectron spectroscopy, differential scanning calorimetry, and high resolution transmission electron microscopy, in conjunction with firstprinciples calculations to identify the stable configurations that can occur at the interface and determine the kinetic barriers for their formation. We find that (i) an interface layer formed during physical deposition of aluminum is composed of a mixture of Cu, O, and Al through Al penetration into CuO and constitutes a poor diffusion barrier (i.e., with spurious exothermic reactions at lower temperature), and in contrast, (ii) atomic layer deposition (ALD) of alumina layers using trimethylaluminum (TMA)produces a conformal coating that effectively prevents Al diffusion even for ultrathin layer thicknesses (∼0.5 nm), resulting in better stability at low temperature and reduced reactivity. Importantly, the initial reaction of TMA with CuO leads to the extraction of oxygen from CuO to form an amorphous interfacial layer that is an important component for superior protection properties of the interface and is responsible for the high system stability. Thus, while Al e-beam evaporation and ALD growth of an alumina layer on CuO both lead to CuO reduction, the mechanism for oxygen removal is different, directly affecting the resistance to Al diffusion. This work reveals that it is the nature of the monolayer interface between CuO and alumina/Al rather than the thickness of the alumina layer that controls the kinetics of Al diffusion, underscoring the importance of the chemical bonding at the interface in these energetic materials

FluoroSAM: A Language-aligned Foundation Model for X-ray Image Segmentation

Automated X-ray image segmentation would accelerate research and development in diagnostic and interventional precision medicine. Prior efforts have contributed task-specific models capable of solving specific image analysis problems, but the utility of these models is restricted to their particular task domain, and expanding to broader use requires additional data, labels, and retraining efforts. Recently, foundation models (FMs) -- machine learning models trained on large amounts of highly variable data thus enabling broad applicability -- have emerged as promising tools for automated image analysis. Existing FMs for medical image analysis focus on scenarios and modalities where objects are clearly defined by visually apparent boundaries, such as surgical tool segmentation in endoscopy. X-ray imaging, by contrast, does not generally offer such clearly delineated boundaries or structure priors. During X-ray image formation, complex 3D structures are projected in transmission onto the imaging plane, resulting in overlapping features of varying opacity and shape. To pave the way toward an FM for comprehensive and automated analysis of arbitrary medical X-ray images, we develop FluoroSAM, a language-aligned variant of the Segment-Anything Model, trained from scratch on 1.6M synthetic X-ray images. FluoroSAM is trained on data including masks for 128 organ types and 464 non-anatomical objects, such as tools and implants. In real X-ray images of cadaveric specimens, FluoroSAM is able to segment bony anatomical structures based on text-only prompting with 0.51 and 0.79 DICE with point-based refinement, outperforming competing SAM variants for all structures. FluoroSAM is also capable of zero-shot generalization to segmenting classes beyond the training set thanks to its language alignment, which we demonstrate for full lung segmentation on real chest X-rays

Artificial Intelligence CAD Tools in Trauma Imaging: A Scoping Review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel

BACKGROUND: AI/ML CAD tools can potentially improve outcomes in the high-stakes, high-volume model of trauma radiology. No prior scoping review has been undertaken to comprehensively assess tools in this subspecialty. PURPOSE: To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness. METHODS: Following a search of databases, abstract screening, and full-text document review, CAD tool maturity was charted using elements of data curation, performance validation, outcomes research, explainability, user acceptance, and funding patterns. Descriptive statistics were used to illustrate key trends. RESULTS: A total of 4052 records were screened, and 233 full-text articles were selected for content analysis. Twenty-one papers described FDA-approved commercial tools, and 212 reported algorithm prototypes. Works ranged from foundational research to multi-reader multi-case trials with heterogeneous external data. Scalable convolutional neural network-based implementations increased steeply after 2016 and were used in all commercial products; however, options for explainability were narrow. Of FDA-approved tools, 9/10 performed detection tasks. Dataset sizes ranged from \u3c 100 to \u3e 500,000 patients, and commercialization coincided with public dataset availability. Cross-sectional torso datasets were uniformly small. Data curation methods with ground truth labeling by independent readers were uncommon. No papers assessed user acceptance, and no method included human-computer interaction. The USA and China had the highest research output and frequency of research funding. CONCLUSIONS: Trauma imaging CAD tools are likely to improve patient care but are currently in an early stage of maturity, with few FDA-approved products for a limited number of uses. The scarcity of high-quality annotated data remains a major barrier

Thermal-Chemical Characteristics of Al-Cu Alloy Nanoparticles

This work investigated the oxidation, ignition, and thermal reactivity of alloy nanoparticles of aluminum and copper (nAlCu) using simultaneous thermogravimetric analysis (TGA) and differential scanning calorimeter (DSC) method. The microstructure of the particles was characterized with a scanning electron microscope (SEM) and transmission electron microscope (TEM), and the elemental composition of the particles before and after the oxidation was investigated with energy dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD). The particles were heated from room temperature to 1200 °C under different heating rates from 2 to 30 K/min in the presence of air. The complete oxidation process of the nAlCu was characterized by two exothermic and two endothermic reactions, and the reaction paths up to 1200 °C were proposed. An early ignition of nAlCu, in the temperature around 565 °C, was found at heating rates ≥ 8 K/min. The eutectic melting temperature of nAlCu was identified at ∼546 °C, which played a critical role in the early ignition. The comparison of the reactivity with that of pure Al nanoparticles showed that the nAlCu was more reactive through alloying