PIER | PIER Journals

2024-09-18

PIER

Vol. 180, 25-53, 2024

download: 31

Alternative Plasmonic Materials for Biochemical Sensing: a Review (Invited Paper)

Leonid Yu. Beliaev , Andrei V. Lavrinenko and Osamu Takayama

Optical materials whose permittivity becomes negative for certain wavelength ranges, so-called plasmonic materials, have been widely used for biochemical sensing applications to detect a wide variety of analytes from chemical agents to protein biomarkers. Since many analytes are or contain nanoscale objects, they interact very weakly with light. Thus, light confinement is a key to improving sensitivity. Using metal or plasmonic nanostructures is a natural solution to confine light and boost light-matter interactions. As there are several different optical sensing schemes, such as refractometric sensing, fluorescence-labeled sensing, and vibrational spectroscopy, whose operating wavelength spans from ultraviolet to mid-infrared wavelength regions, some plasmonic materials are superior to others for certain wavelength regions. In this article, we review current progress on alternative plasmonic materials, other than gold, silver, and aluminum, used in biochemical sensing applications. We cover a wide variety of plasmonic material platforms, such as transparent conductive oxides, nitrides, doped semiconductors, polar materials, two-dimensional, van der Waals materials, transition metal dichalcogenides, and plasmonic materials for ultraviolet wavelengths.

Alternative Plasmonic Materials for Biochemical Sensing: A Review (Invited Paper)

2024-09-04

PIER

Vol. 180, 13-24, 2024

doi:10.2528/PIER24050403

download: 226

Measurement of Time-Range-Angle-Dependent Beam Patterns of Frequency Diverse Arrays (Invited)

Haochi Zhang , Lepeng Zhang , Shengheng Liu , Zihuan Mao , Yahui Ma , Pei Hang He , Wen Yi Cui , Yi Fei Huang , Qi Yang and Tie-Jun Cui

Frequency diverse arrays (FDAs) have drawn great attention because they can provide a time-range-angle-dependent beam pattern that has many promising potential applications in navigation and radar systems. However, due to the limitations of measurement systems, this attractive beam pattern has not been experimentally observed. Here, a far-field measurement system for the time-range-angle beam pattern of FDA is proposed by improving the existing near-field mapping system. Without loss of generality, two types of time-range-angle-dependent beam patterns for FDA systems with different frequency sets are observed using the proposed far-field measurement system. The high efficiency and accuracy of the proposed system is verified by good agreement between the measured and simulated results. This work marks significant progress toward the practical implementation and application of FDAs.

Measurement of Time-Range-Angle-Dependent Beam Patterns of Frequency Diverse Arrays (Invited)

2024-09-01

PIER

Vol. 180, 1-11, 2024

doi:10.2528/PIER24012201

download: 192

Highly Accurate and Efficient 3D Implementations Empowered by Deep Neural Network for 2DLMs -Based Metamaterials

Naixing Feng , Huan Wang , Xuan Wang , Yuxian Zhang , Chao Qian , Zhixiang Huang and Hongsheng Chen

Streamlining the on-demand design of metamaterials, both forward and inverse, is highly demanded for unearthing complex light-matter interaction. Deep learning, as a popular data-driven method, has recently found to largely alleviate the time-consuming and experience-orientated features in widely-used numerical simulations. In this work, we propose a convolution-based deep neural network to implement the inverse design and spectral prediction of a broadband absorber, and deep neural network (DNN) not only achieves highly-accurate results based on small data samples, but also converts the one-dimensional (1D) spectral sequence into a 2D picture by employing the Markov transition field method so as to enhance the variability between spectra. From the perspective of a single spectral sample, spectral samples carry not enough information for neural network due to the constraints of the number of sampling points; from the perspective of multiple spectral samples, the gap between different spectral samples is very small, which can hinder the performance of the reverse design framework. Markov transition field method can enhance the performance of the model from those two aspects. The experimental results show that the final value of the soft required accuracy of the one-dimensional fully connected neural network model and the two-dimensional residual neural network model differ by nearly 1%, the final value of the soft accuracy of the one-dimensional residual neural network model is 97.6%. The final value of the two-dimensional residual neural network model model is 98.5%. The model utilises a data enhancement approach to improve model accuracy and also provides a key reference for designing two-dimensional layered materials (2DLMs) based metamaterials with on-demand properties before they are put into manufacturing.