An introductory study using impedance spectroscopy technique with polarizable microelectrode for amino acids characterization

Machine Learning Analysis of the Thermodynamic Responses of In Situ Dielectric Spectroscopy Data in Amino Acids and Inorganic Electrolytes

Dielectric spectroscopy (DS) can be a robust in situ technique for geochemical applications. In this study, we applied deep-learning techniques to DS measurement data to enable rapid science interrogation and identification of electrolyte solutions containing salts and amino acids over a wide temperature range (20 to -60 °C). For the purpose of searching for signs of life, detecting amino acids is a fundamental high priority for field and planetary instruments as amino acids are one of the building blocks for life as we know it. A convolutional neural network (CNN) with channel-wise one-dimensional filters is proposed to fulfill the task, using the DS data of amino acid and inorganic salt solutions. Experimental results show that the CNN with two convolutional layers and one fully connected layer can effectively differentiate solutions containing amino acids from those containing salts in both the liquid and solid (water ice) states. To complement the experimental measurements and CNN analysis, the diffusive behaviors of ions (K⁺, Cl^-, and OH^-) were further discussed with atomistic molecular dynamics simulations performed in this work as well as the quantum simulation published in the literature. Combining DS with machine-learning techniques and simulations will greatly facilitate more real-time decision-making of mobility systems for future exploratory endeavors in other worlds beyond Earth.

Reduced electrode polarization at electrode and analyte interface in impedance spectroscopy using carbon paste and paper

The double layer present at the interface of an electrode and an analyte causes electrode polarization (EP) in impedance spectroscopy, which hinders acquiring the actual impedance of biological samples at a lower frequency region. In this work, a novel carbon paste (CP) electrode material prepared by mixing the pencil graphite powder with transparent glue has been reported to reduce the EP by depositing its two coplanar electrodes on a chromatography paper substrate. Furthermore, two other devices having silver paste and pencil electrodes on the chromatography paper have been fabricated, analyzed for the EP, and compared with the CP electrode. The EP is quantified by fitting the impedance data to an equivalent electrical circuit having double layer capacitance as a constant phase element, and the CP electrode shows the lowest EP among the electrodes. The cyclic voltammetry analysis reveals blocking electrode property of the CP, which diminishes dc current flow at the electrode/electrolyte interface. Furthermore, the chromatography paper is found to increase the effective surface area of the deposited electrode by enhancing its surface roughness, which helps reduce the EP.