Pseudogap and weak multifractality in disordered Mott charge-density-wave insulator

The competition, coexistence and cooperation of various orders in low-dimensional materials like spin, charge, topological orders and charge-density-wave has been one of the most intriguing issues in condensed matter physics. In particular, layered transition metal dichalcogenides provide an ideal platform for studying such an interplay with a notable case of 1${T}$-TaS$_{2}$ featuring Mott-insulating ground state, charge-density-wave, spin frustration and emerging superconductivity together. We investigated local electronic states of Se-substituted 1${T}$-TaS$_{2}$ by scanning tunneling microscopy/spectroscopy (STM/STS), where superconductivity emerges from the unique Mott-CDW state. Spatially resolved STS measurements reveal that an apparent V-shape pseudogap forms at the Fermi Level (E$_{F}$), with the origin of the electronic states splitting and transformation from the Mott states, and the CDW gaps are largely preserved. The formation of the pseudogap has little correlation to the variation of local Se concentration, but appears to be a global characteristics. Furthermore, the correlation length of local density of states (LDOS) diverges at the Fermi energy and decays rapidly at high energies. The spatial correlation shows a power-law decay close to the Fermi energy. Our statistics analysis of the LDOS indicates that our system exhibits weak multifractal behavior of the wave functions. These findings strongly support a correlated metallic state induced by disorder in our system, which provides an new insight into the novel mechanism of emerging superconductivity in the two-dimensional correlated electronic systems

Low-resolution Prior Equilibrium Network for CT Reconstruction

The unrolling method has been investigated for learning variational models in X-ray computed tomography. However, it has been observed that directly unrolling the regularization model through gradient descent does not produce satisfactory results. In this paper, we present a novel deep learning-based CT reconstruction model, where the low-resolution image is introduced to obtain an effective regularization term for improving the network`s robustness. Our approach involves constructing the backbone network architecture by algorithm unrolling that is realized using the deep equilibrium architecture. We theoretically discuss the convergence of the proposed low-resolution prior equilibrium model and provide the conditions to guarantee convergence. Experimental results on both sparse-view and limited-angle reconstruction problems are provided, demonstrating that our end-to-end low-resolution prior equilibrium model outperforms other state-of-the-art methods in terms of noise reduction, contrast-to-noise ratio, and preservation of edge details

Flow Boiling Heat Transfer And Pressure Drop Characteristics Of R1234yf In A Dimpled Flat Duct

Among various heat transfer enhancement technologies, the dimpled surface, which is inspired by the resistance reduction characteristics of the specific concaves on golf balls, has the potential to improve heat transfer with a relatively low pressure-drop penalty. More and more applications of dimpled surfaces in heat exchangers have shown up in industries. However, the lack of experimental data, especially the heat transfer and pressure drop data for liquidand-vapor two-phase flow, inside the dimpled flow channels prevents the good design of the dimpled heat exchangers. In this study, a facility has been designed and built to investigate the heat transfer and pressure drop of flow-boiling R1234yf in a dimpled flat duct. The details of the facility, especially the test section, are presented. A microscope is used to measure the geometrical dimensions of the dimpled flat tube. The heat loss is tested and the heat balance is -2 -1 checked before the experiments. The experiments are performed at mass flux from 100 to 200 kg m s , heat flux of 5 kW m-2, saturation temperature of 15 oC, and vapor quality from 0.1 to 0.95. The experimental results are presented and discussed in detail

An Updated Quantification Method for Liquid Refrigerant Distribution in Microchannel Evaporators Using Infrared Thermography

Refrigerant distribution in the parallel tubes of a microchannel evaporator significantly affects its heat transfer performance, which can further affect the coefficient of performance of the whole air-conditioning or refrigeration system. This paper proposes an easy-to-implement quantification method using infrared thermography for the liquid refrigerant distribution in microchannel evaporators to update the original method developed by Li and Hrnjak (2015). Before the detailed discussion of the new quantification method, the effect of surface emissivity on the infrared thermography is investigated, and the calibration process of the infrared thermography is presented for a microchannel heat exchanger sample. Then, the updated quantification method is introduced in detail. The ε-NTU approach is clarified for the formula derivation. A new mathematical method is introduced for the determination of the transition between the two-phase region and the single-phase region. A facility with pump-driven two-phase refrigerant R134a has been built to demonstrate the updated quantification method for the liquid refrigerant distribution in a microchannel evaporator with vertical parallel tubes. The tests have been run at the conditions of 41.7 g/s refrigerant flow rate and 5 oC evaporation temperature with the evaporator inlet vapor quality of 0.15 and 0.25, respectively. The infrared images and the reduced liquid refrigerant mass flow rate distributions are presented to demonstrate the effectiveness of the updated quantification method

A novel porcine reproductive and respiratory syndrome virus vector system that stably expresses enhanced green fluorescent protein as a separate transcription unit

Abstract Here we report the rescue of a recombinant porcine reproductive and respiratory syndrome virus (PRRSV) carrying an enhanced green fluorescent protein (EGFP) reporter gene as a separate transcription unit. A copy of the transcription regulatory sequence for ORF6 (TRS6) was inserted between the N protein and 3′-UTR to drive the transcription of the EGFP gene and yield a general purpose expression vector. Successful recovery of PRRSV was obtained using an RNA polymerase II promoter to drive transcription of the full-length virus genome, which was assembled in a bacterial artificial chromosome (BAC). The recombinant virus showed growth replication characteristics similar to those of the wild-type virus in the infected cells. In addition, the recombinant virus stably expressed EGFP for at least 10 passages. EGFP expression was detected at approximately 10 h post infection by live-cell imaging to follow the virus spread in real time and the infection of neighbouring cells occurred predominantly through cell-to-cell-contact. Finally, the recombinant virus generated was found to be an excellent tool for neutralising antibodies and antiviral compound screening. The newly established reverse genetics system for PRRSV could be a useful tool not only to monitor virus spread and screen for neutralising antibodies and antiviral compounds, but also for fundamental research on the biology of the virus.This study was funded by grants from the National Natural Science Foundation of China (U0931003/L01) and the National High-Tech R&D Program of China (2011AA10A208) to EMZ, the National Natural Science Foundation of China (31302103) to WCB, the European Community’s Seventh Frame-work Programme (PoRRSCon, FP7-KBBE-2009-3-245141) and the Ministry of Science and Innovation of Spain (MCINN) (BIO2010-16075) to FA and LE.Peer Reviewe

Curvature regularization for Non-line-of-sight Imaging from Under-sampled Data

Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes from the data measured in the line-of-sight, which uses photon time-of-flight information encoded in light after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is of high possibility to be degraded due to noises and distortions. In this paper, we propose two novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (i.e., signal and object)-domain curvature regularization model. Fast numerical optimization algorithms are developed relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, which are further accelerated by GPU implementation. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. All our codes and data are available at https://github.com/Duanlab123/CurvNLOS