The past few years have witnessed the great success of graphene in controlling the electromagnetic (EM) wave. As an important topic in both the physics and engineering fields, wavefront control has attracted more and more attention from the researchers. So far, most graphene-based wavefront control is studied in terahertz or higher frequencies. In the microwave band, relevant work is rarely reported, which is limited by the nearly purely resistive property of graphene, the lack of reactance makes phase control a difficult problem. In this paper, we present and experimentally realize the microwave programmable graphene metasurface (MPGM) for the first time. By analyzing the equivalent impedance, the necessary condition of achieving a binary element using resistive material is first derived. Inspired by which, the proposed structure can realize uniform reflection amplitude and opposite phase simultaneously through changing the voltage applied to graphene. Meanwhile, the patterned configuration makes it possible to control different elements independently. As a result, both simulated and measured results indicate that our MPGM can realize multiple functions such as beam redirecting and radar cross section reduction, paving the way for graphene in the application of designing tunable phase-based devices in the microwave band.
To develop a mechanistic-empirical pavement design system for Norwegian conditions, this paper evaluates the influence of the adoption of different models and shifting techniques on the determination of dynamic modulus master curves of asphalt mixtures. Two asphalt mixture types commonly used in Norway, namely Asphalt Concrete (AC) and Stone Mastic Asphalt (SMA) containing neat bitumen and polymer-modified bitumen, were prepared by the roller compactor, and their dynamic moduli were determined by the cyclic indirect tensile test. The dynamic modulus master curves were constructed using the standard logistic sigmoidal model, a generalized logistic sigmoidal model and the Christensen–Anderson–Marasteanu model. The shifting techniques consisted of log-linear, quadratic polynomial function, Arrhenius, William–Landel–Ferry and Kaelble methods. The absolute error, normalised square error and goodness-of-fit statistics encompassing standard error ratio and coefficient of determination were used to appraise the models and shifting methods. The results showed that the standard logistic sigmoidal model and the Williams–Landel–Ferry equation had the most suitable fits for the specimens tested. The asphalt mixtures containing neat bitumen had a better fit than the ones containing polymer-modified bitumen. The Kaelble equation and log-linear equation led to similar results. These findings provide a relevant recommendation for the mechanistic-empirical pavement design system.
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