Browsing by Subject "Gas sensor"

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Analysis and classification of drift susceptible chemosensory responses
(2014-12) Bansal, Puneet, active 21st century; Ghosh, Joydeep
This report presents machine learning models that can accurately classify gases by analyzing data from an array of 16 sensors. More specifically, the report presents basic decision tree models and advanced ensemble versions. The contribution of this report is to show that basic decision trees perform reasonably well on the gas sensor data, however their accuracy can be drastically improved by employing ensemble decision tree classifiers. The report presents bagged trees, Adaboost trees and Random Forest models in addition to basic entropy and Gini based trees. It is shown that ensemble classifiers achieve a very high degree of accuracy of 99% in classifying gases even when the sensor data is drift ridden. Finally, the report compares the accuracy of all the models developed.
In-situ real-time spectroscopy platform for monitoring gas adsorption and reactions on 2D materials
(2019-12-05) Holt, Milo; Akinwande, Deji; Banerjee, Sanjay; Bank, Seth; Cullinan, Michael; Dodabalapur, Ananth
Thin film gas sensors are at the center of critical areas of research and innovation, with applications in a wide range of important and fast-evolving fields. From environmental monitoring and medical diagnosis to early warning systems and safety, gas sensors are a vital front end technology already ubiquitous in industry. With development of the Internet of Things (IoT), the need for untethered platforms is accelerating the search for gas sensing materials suitable in low-power applications. 2D materials like graphene and 40+ combinations of the transition metal dichalcogenides (TMDs) show particular promise here, owing to a suite of exceptional dimensional, structural and electronic properties. Specifically with respect to gas sensing, 2D thin films provide the ultimate material dimensions and properties for low-power fast-response devices, at the same time providing an excellent interface for analyte-surface observations. This dissertation concerns the sensing properties and stability of the four prototypical semiconducting TMD combinations of molybdenum (Mo), Tungsten (W), Sulfur (S) and Selenium (Se), with a focus on molybdenum disulfide (MoS₂). This work presents a platform solution for the in-situ real-time (dynamic) capture and study of ammonia (NH₃), water vapor (H₂O) and oxygen (O₂) interactions with exfoliated and synthesiszed TMDs in a controlled environment, at both ambient and elevated temperatures