Miniaturization and Fabrication of a Novel Cross-Fractal Biosensor and Sensor for Characterizing 3D Printing Electromagnetic Properties in Polylactic Acid

Abstract

This study presents a new approach for characterizing the 3D printing electromagnetic properties in Polylactic Acid (PLA) using a novel cross-fractal sensor. The CST Studio Suite simulation, employing the finite element method (FEM), and the ADS simulation software, based on the equivalent circuit of this novel sensor, are utilized to enhance confidence in the convergence of simulation and measurement results. The analysis confirms that the real sensor, based on a cross-sectional fractal resonator, exhibits excellent sensitivity, selectivity, and linearity. Sensor performance is evaluated through two methods: the frequency-based approach and the S11 parameter analysis. In the frequency-based method, the sensor demonstrates a typical sensitivity of 0.0302 GHz/cm³, while in the S11 parameter analysis, it exhibits a typical sensitivity of 0.3065 dB/cm³. These results underscore the high sensitivity and linearity of this innovative sensor. The using of this novel cross fractal sensor, it promises to show how to properly adjust 3D printer parameters, potentially setting the "infill percentage" of the 3D print, this is because the proportion of air present changes when the printing percentage is changed, which can lead to (εr) and loss-tangent (tan (δ)) with varying the infill percentage, by the resonance frequency (fr) approach demonstrates a characteristic sensitivity of 0.1822 GHz/RIU. The experimental study confirmed that the fabricated sensor exhibits miniaturization (with an electrical size of λ0/8), high sensitivity, and excellent linearity in the frequency approach, with a typical sensitivity of 0.004615 GHz/%. The suggested biosensor demonstrated its capability to detect low concentrations of ethanol. These aqueous solutions, containing known alcohols like ethanol in low concentrations, enables identification of Halal and alcohol-free products in food sensing applications.