simulation-early-earth-hydrothermal-chimneys-thermal-gradient

Simulation of Early Earth Hydrothermal Chimneys in a Thermal Gradient Environment

Deep sea hydrothermal vents are self-organizing precipitates generated from geochemical disequilibria and have been proposed as a possible setting for the ...

California Institute of Technology

In an early earth or planetary chimney systems, hydrothermal fluid chemistry and flow durations play a large role in the chimney's ability to drive electrochemical reactions for the origin of life. We performed continuous electrochemical impedance spectroscopy (EIS) characterization on inorganic membranes representing prebiotic hydrothermal chimney vents in natural seafloor systems, by incorporating an electrode array into a chimney growth experiment. Localized potential and capacitances profiles in the chimney reveal a dynamic system where redox processes are driven by transport phenomena, increasing rapidly due to disequilibrium until achieving equilibrium at about 100 mV and 1000 μF/cm . The impedance in the chimney interior is three orders of magnitude lower (100 Ohms/cm vs 100 KOhms/cm ) than at the ocean or the ocean/chimney interface. The calculated peak dissipation factor (DF) values are more than ten times higher (40.0 vs 3.0) and also confirm the elevated chemical reactivity in the chimney interior.

Chemical Gardens as Electrochemical Systems: In Situ Characterization of Simulated Prebiotic Hydrothermal Vents by Impedance Spectroscopy.

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.

Article	Year
Chemical Gardens as Electrochemical Systems: In Situ Characterization of Simulated Prebiotic Hydrothermal Vents by Impedance Spectroscopy. ChemPlusChem\| PubMed ID: 33270995	2020
Machine Learning Analysis of the Thermodynamic Responses of In Situ Dielectric Spectroscopy Data in Amino Acids and Inorganic Electrolytes. The journal of physical chemistry. B\| PubMed ID: 33284009	2020

Ninos Hermis

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