Fermions, Gauge Theories, and the Sinc Function Representation for Feynman Diagrams

We extend our new approach for numeric evaluation of Feynman diagrams to integrals that include fermionic and vector propagators. In this initial discussion we begin by deriving the Sinc function representation for the propagators of spin-1/2 and spin-1 fields and exploring their properties. We show that the attributes of the spin-0 propagator which allowed us to derive the Sinc function representation for scalar field Feynman integrals are shared by fields with non-zero spin. We then investigate the application of the Sinc function representation to simple QED diagrams, including first order corrections to the propagators and the vertex.Comment: 10 pages, Latex, 9 figure

Phase Separation and Magnetic Order in K-doped Iron Selenide Superconductor

Alkali-doped iron selenide is the latest member of high Tc superconductor family, and its peculiar characters have immediately attracted extensive attention. We prepared high-quality potassium-doped iron selenide (KxFe2-ySe2) thin films by molecular beam epitaxy and unambiguously demonstrated the existence of phase separation, which is currently under debate, in this material using scanning tunneling microscopy and spectroscopy. The stoichiometric superconducting phase KFe2Se2 contains no iron vacancies, while the insulating phase has a \surd5\times\surd5 vacancy order. The iron vacancies are shown always destructive to superconductivity in KFe2Se2. Our study on the subgap bound states induced by the iron vacancies further reveals a magnetically-related bipartite order in the superconducting phase. These findings not only solve the existing controversies in the atomic and electronic structures in KxFe2-ySe2, but also provide valuable information on understanding the superconductivity and its interplay with magnetism in iron-based superconductors

Time-Constrained Ensemble Sensing With Heterogeneous IoT Devices in Intelligent Transportation Systems

Recently we have witnessed the rise of Artificial Intelligence of Things (AIoT) and the shift of sensing paradigm from cloud-centric to the edge-centric, which effectively improves the sensing capability of intelligence transportation systems. To improve the real-time sensing performance, in this work we propose an ensemble sensing based scheme to solve the time-constraint synchronized inference problem and achieve robust inference with heterogeneous IoT devices in intelligence transportation systems. We design and implement Ensen, which incorporates various novel techniques such as customized DNN model design, KD-based model training, and dynamic deep ensemble management, etc., to achieve improved accuracy and maximize the computational resource usage of the whole sensing group. Extensive evaluations on different types of common IoT devices have shown that Ensen achieves a robust performance and can be easily extended to different types of convolutional neural networks

Stretchable Self-Healable Semiconducting Polymer Film for Active-Matrix Strain-Sensing Array

Skin-like sensory devidces shoud be stretchable and self-healable to meet the demands for future electronic skin applications. Despite recent notable advances in skin-inspired electronic materials, it remains challenging to confer these desired functionalities to an active semiconductor. Here, we report a strain-sensitive, stretchable, and autonomously self-healable semiconducting film achieved through blending of a polymer semiconductor and a self-healable elastomer, both of which are dynamically cross-linked by metal coordination. We observed that by controlling the percolation threshold of the polymer semiconductor, the blend film became strain sensitive, with a gauge factor of 5.75 x 105 at 100% strain in a stretchable transistor. The blend film is also highly stretchable (fracture strain, \u3e1300%) and autonomously self-healable at room temperature. We proceed to demonstrate a fully integrated 5 x 5 stretchable active-matrix transistor sensor array capable of detecting strain distribution through surface deformation

Improvement in NO2 Sensing Properties of Semiconductor-Type Gas Sensors by Loading of Au Into Porous In2O3 Powders

Porous (pr-) In2O3 powders loaded with and without noble metals (Au, Pd, or Pt) were prepared by ultrasonic spray pyrolysis employing the PMMA microspheres as a template (typical particle size (ps): 28 or 70 nm with a diameter), and their NO2 sensing properties were examined. The Au loading on the pr-In2O3 was effective to increase the NO2 response at lower operating temperature (?200°C), while the metal loading of Pd or Pt were hardly effective. In addition, a decrease in the PMMA microspheres (from 70 to 28 nm in ps) largely increased the NO2 response,and an optimized amount of Au loaded on the pr-In2O3 sensor was 1.0 wt%. The decrease in the thickness of the sensing layer improved the NO2 response and response speed. It was suggested that the Au loading enhanced the amount of the negatively adsorbed NO2 on the bottom part of the sensing layer, leading to the increase in the NO2 response. Furthermore, the introduction of additional macropores (ps: 150 nm) to the 1.0 wt% Au loaded pr-In2O3 sensor increased the response to a low concentration of NO2 (0.025 ppm) at 30°C. Therefore, it was found that easy gas diffusion from the surface to the bottom part of the sensing layer increased the effective concentration of NO2, and thus the NO2 response was increased

Searching for Better Plasmonic Materials

Plasmonics is a research area merging the fields of optics and nanoelectronics by confining light with relatively large free-space wavelength to the nanometer scale - thereby enabling a family of novel devices. Current plasmonic devices at telecommunication and optical frequencies face significant challenges due to losses encountered in the constituent plasmonic materials. These large losses seriously limit the practicality of these metals for many novel applications. This paper provides an overview of alternative plasmonic materials along with motivation for each material choice and important aspects of fabrication. A comparative study of various materials including metals, metal alloys and heavily doped semiconductors is presented. The performance of each material is evaluated based on quality factors defined for each class of plasmonic devices. Most importantly, this paper outlines an approach for realizing optimal plasmonic material properties for specific frequencies and applications, thereby providing a reference for those searching for better plasmonic materials.Comment: 27 pages, 6 figures, 2 table

Pairing symmetry and properties of iron-based high temperature superconductors

Pairing symmetry is important to indentify the pairing mechanism. The analysis becomes particularly timely and important for the newly discovered iron-based multi-orbital superconductors. From group theory point of view we classified all pairing matrices (in the orbital space) that carry irreducible representations of the system. The quasiparticle gap falls into three categories: full, nodal and gapless. The nodal-gap states show conventional Volovik effect even for on-site pairing. The gapless states are odd in orbital space, have a negative superfluid density and are therefore unstable. In connection to experiments we proposed possible pairing states and implications for the pairing mechanism.Comment: 4 pages, 1 table, 2 figures, polished versio