Surface Potential Decay and DC Conductivity of TiO2-based Polyimide Nanocomposite Films

Polymer nanocomposites have attracted wide interest as a method of enhancing polymer properties and extending their applications. Surface potential decay has been used widely as a tool to monitor charge transport and trapping characteristics of insulating materials. Polyimide (PI) as an engineering material has been paid more attention due to high thermal and chemical stability, good mechanical property and excellent insulating property in a wide range of temperature. There has been a lot of work over last few years on optical, thermal and mechanical properties of polyimide nanocomposites. However, little attention has been given to the effect of nano-fillers on charge transport and trapping in polyimide nanocomposites. In the present paper, pure, 1%, 3%, 5% and 7% polyimide nanocomposites was examined by using surface potential decay in conjunction with dc conductivity measurement and both experiments showed that 3% is the optimal value for electrical insulation

Topological semimetals with Riemann surface states

Riemann surfaces are geometric constructions in complex analysis that may represent multi-valued holomorphic functions using multiple sheets of the complex plane. We show that the energy dispersion of surface states in topological semimetals can be represented by Riemann surfaces generated by holomorphic functions in the two-dimensional momentum space, whose constant height contours correspond to Fermi arcs. This correspondence is demonstrated in the recently discovered Weyl semimetals and leads us to predict new types of topological semimetals, whose surface states are represented by double- and quad-helicoid Riemann surfaces. The intersection of multiple helicoids, or the branch cut of the generating function, appears on high-symmetry lines in the surface Brillouin zone, where surface states are guaranteed to be doubly degenerate by a glide reflection symmetry. We predict the heterostructure superlattice [(SrIrO$_3$)$_2$(CaIrO$_3$)$_2$] to be a topological semimetal with double-helicoid Riemann surface states.Comment: Four pages, four figures and two pages of appendice

Novel Approaches for Regional Multifocus Image Fusion

Image fusion is a research topic about combining information from multiple images into one fused image. Although a large number of methods have been proposed, many challenges remain in obtaining clearer resulting images with higher quality. This chapter addresses the multifocus image fusion problem about extending the depth of field by fusing several images of the same scene with different focuses. Existing research in multifocus image fusion tends to emphasis on the pixel-level image fusion using transform domain methods. The region-level image fusion methods, especially the ones using new coding techniques, are still limited. In this chapter, we provide an overview of regional multi-focus image fusion, and two different orthogonal matching pursuit-based sparse representation methods are adopted for regional multi-focus image fusion. Experiment results show that the regional image fusion using sparse representation can achieve a comparable even better performance for multifocus image fusion problems

Pressure from data-driven estimation of velocity fields using snapshot PIV and fast probes

The most explored path to obtain pressure fields from Particle Image Velocimetry (PIV) data roots its basis on accurate measurement of instantaneous velocity fields and their corresponding time derivatives. This requires time-resolved measurements, which are often difficult to achieve due to hardware limitations and expensive to implement. In alternative, snapshot PIV experiments are more affordable but require enforcing physical constraints (e.g. Taylor’s hypothesis) to extract the time derivative of the velocity field. In this work, we propose the use of data-driven techniques to retrieve time resolution from the combination of snapshot PIV and high-repetition-rate sensors measuring flow quantities in a limited set of spatial points. The instantaneous pressure fields can thus be computed by leveraging the Navier–Stokes equations as if the measurement were time-resolved. Extended Proper Orthogonal Decomposition, which can be regarded as one of the simplest algorithm for estimating velocity fields from a finite number of sensors, is used in this paper to prove the feasibility of this concept. The method is fully data-driven and, after training, it requires only probe data to obtain field information of velocity and pressure in the entire flow domain. This is certainly an advantage since model-based methods can retrieve pressure in an observed snapshot, but show increasing error as the field information is propagated over time. The performances of the proposed method are tested on datasets of increasing complexity, including synthetic test cases of the wake of a fluidic pinball and a channel flow, and experimental measurements in the wake of a wing. The results show that the data-driven pressure estimation is effective in flows with compact POD spectrum. In the cases where Taylor’s hypothesis holds well, the in-sample pressure field estimation can be more accurate for model-based methods; nonetheless, the proposed data-driven approach reaches a better accuracy for out-of-sample estimation after less than 0.20 convective times in all tested cases.This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085). Funding for APC: Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2022)

Symmetry Enforced Self-Learning Monte Carlo Method Applied to the Holstein Model

Self-learning Monte Carlo method (SLMC), using a trained effective model to guide Monte Carlo sampling processes, is a powerful general-purpose numerical method recently introduced to speed up simulations in (quantum) many-body systems. In this work, we further improve the efficiency of SLMC by enforcing physical symmetries on the effective model. We demonstrate its effectiveness in the Holstein Hamiltonian, one of the most fundamental many-body descriptions of electron-phonon coupling. Simulations of the Holstein model are notoriously difficult due to the combination of the typical cubic scaling of fermionic Monte Carlo and the presence of extremely long autocorrelation times. Our method addresses both bottlenecks. This enables simulations on large lattices in the most difficult parameter regions, and evaluation of the critical point for the charge density wave transition at half-filling with high precision. We argue that our work opens a new research area of quantum Monte Carlo (QMC), providing a general procedure to deal with ergodicity in situations involving Hamiltonians with multiple, distinct low energy states.Comment: 4 pages, 3 figures with 2 pages supplemental materia