Superradiant Emission from Coherent Excitons in van Der Waals Heterostructures

Recent advancements in isolation and stacking of layered van der Waals materials have created an unprecedented paradigm for demonstrating varieties of 2D quantum materials. Rationally designed van der Waals heterostructures composed of monolayer transition-metal dichalcogenides (TMDs) and few-layer hBN show several unique optoelectronic features driven by correlations. However, entangled superradiant excitonic species in such systems have not been observed before. In this report, it is demonstrated that strong suppression of phonon population at low temperature results in a formation of a coherent excitonic-dipoles ensemble in the heterostructure, and the collective oscillation of those dipoles stimulates a robust phase synchronized ultra-narrow band superradiant emission even at extremely low pumping intensity. Such emitters are in high demand for a multitude of applications, including fundamental research on many-body correlations and other state-of-the-art technologies. This timely demonstration paves the way for further exploration of ultralow-threshold quantum-emitting devices with unmatched design freedom and spectral tunability.     

Blockchain for Collaborative Businesses

Mobile Networks and Applications - Blockchain applications have continuously improved ever since its first debut on cryptocurrency. From then on, its uses have branched out from the financial...     

Synthesis,characterization and charge transport properties of Pr–Ni Co-doped SrFe2O4 spinel for high frequency devices applications

Ferrites are materials of interest due to their broad applications in high technological devices and a lot of research has been focused to synthesize new ferrites. In this regard, an effort has been devoted to synthesize spinel Pr–Ni co-substituted strontium ferrites with a nominal formula of Sr_1-xPr_xFe_2-yNi_yO₄ (0.0 ≤ x ≤ 0.1, 0.0 ≤ y ≤ 1.0). The cubic structure of pure and Pr–Ni co-substituted strontium ferrite samples calcinated at 1073 K for 3 h has been confirmed through X-ray diffraction (XRD). Average sizes of crystallites (18–25 nm) have been estimated from XRD analysis and nanometer particle sizes of synthesized ferrites have been further verified by scanning electron microscopy (SEM). SEM results have also shown that particles are mostly agglomerated and all the samples possess porosity. It has been observed that at 298 K, the values of resistivity (ρ) increase, while that of AC conductivity, dielectric loss, and dielectric constants decrease with increasing amounts of Pr³⁺ and Ni²⁺ ions. The values of dielectric parameters initially decrease with frequency and later become constant and can be explained on the basis of dielectric polarization. Electrochemical impedance spectroscopy (EIS) studies show that the charge transport phenomenon in ferrite materials is mainly controlled via grain boundaries. Overall, synthesized ferrite materials own enhanced resistivity values in the range of 1.38 × 10⁹–1.94 × 10⁹ Ω cm and minimum dielectric losses, which makes them suitable candidates for high frequency devices applications.     

Knowledge-Based Virtual Modeling and Simulation of Manufacturing Processes for Industry 4.0

Abstract

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing.     

Optimization of waste quail eggshells as biocomposites for polyaniline in ammonia gas detection

A simple, cost-effective, and novel chemical sensor for ammonia (NH₃) gas detection was developed from polyaniline (PANI)/quail eggshell (QES) composites. QES is a natural waste enriched in calcium carbonate. In this work, pure PANI was synthesized from chemical oxidation method and PANI/QES composites were prepared from physical mixing of QES with the synthesized PANI at different mass ratio. A series of complementary techniques including Fourier transform infrared and ultraviolet-visible spectrometers, scanning electron microscope with energy dispersive detection coupled with mapping, thermogravimetric analysis, and X-ray diffractometer were used to characterize the physicochemical and textural properties of the biocomposites. From the results, PANI/QES composite with a mass ratio of 1 exhibited the lowest NH₃ detection limit of 5.24 ppm with a linear correlation coefficient (R²) of close to unity (0.9932) between the signal and NH₃ gas concentration. As a whole, the PANI/QES biocomposites synthesized from this work exhibited excellent selectivity toward NH₃ gas even in the presence of other gas impurities, such as acetone, ethanol, and hexane. For the sensor reusability, the PANI/QES biocomposites can be reused in the application of NH₃ gas detection for at least 4 cycles.     

An optimized skin texture model using gray-level co-occurrence matrix

Neural Computing and Applications - Texture analysis is devised to address the weakness of color-based image segmentation models by considering the statistical and spatial relations among the group...     

ACTNET: End-to-End Learning of Feature Activations and Multi-stream Aggregation for Effective Instance Image Retrieval

International Journal of Computer Vision - We propose a novel CNN architecture called ACTNET for robust instance image retrieval from large-scale datasets. Our key innovation is a learnable...