Applying machine learning techniques to enhance the sensitivity and Selectivity of Multifunctional Sensor Platform

The goal of this project was to develop machine learning algorithms for the purpose of enhancing the sensitivity and performance of scientific instruments. As an example, we used various experimental datasets from an ultra-compact in situ chemical analysis tool called the Multifunctional Nanosensor Platform (MNP).

Enhancing the sensitivity of Multifunctional Nanosensor Platforms (MNP) allows us to reach an unprecedented sensitivity of sub-parts per billion for this type of miniaturized instruments. The NASA Nanotechnology Roadmap activity has identified sensors that can detect minute concentrations of gases and vapors in the parts per billion (ppb) level as one of the key technologies required for missions over the next several decades for Planetary Science, Heliophysics and Human Exploration. Applications of MNP range from detecting key species for the origin of life in and habitability assessment of other planetary bodies, to understanding chemical processes on Earth and other bodies, to monitoring enclosed areas in human exploration missions to ensure both the safety of crew and proper operation of space assets. In addition, the methodologies developed in this work can be applied to other similar instruments to enhance their performance.

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