Publications by authors named "Mahdi Nooshyar"

This study presents a novel pseudospin-polarized waveguide with closed boundaries, designed using complementary metasurfaces with dual surface impedances. By enforcing electromagnetic duality, the proposed structure establishes mirror reflection symmetry, significantly reducing backscattering and ensuring robust one-way wave propagation. The waveguide effectively suppresses backward-propagating modes, even in the presence of bends and structural discontinuities, making it a highly stable and efficient platform for guided-wave applications.

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Quasi-line waves represent a distinct class of propagation modes along non-complementary impedance surfaces, offering an alternative to the conventional line waves typically formed by complementary impedance surfaces. In this study, we introduce a novel design for quasi-line waves utilizing non-dual, purely inductive impedance structures. By incorporating multilayer graphene, our design achieves wide bandwidth and extended propagation lengths in the terahertz range, with field concentration localized at the edges of the inductive surfaces.

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Artificial neural networks (ANNs) have been successfully trained to model and predict the acidity constants (pK(a)) of 128 various phenols with diverse chemical structures using a quantitative structure-activity relationship. An ANN with 6-14-1 architecture was generated using six molecular descriptors that appear in the multi-parameter linear regression (MLR) model. The polarizability term (pi (I)), most positive charge of acidic hydrogen atom (q+), molecular weight (MW), most negative charge of the phenolic oxygen atom (q-), the hydrogen-bond accepting ability (epsilon(B)) and partial-charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pK(a).

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