Browsing by Author "Tang,Yifan"
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Item Nanomaterials Based Gas Sensor for ISRU Process Contaminants(2024 International Conference on Environmnetal Systems, 2024-07-21) Mushfiq, Mohammad; Zhang, Liyue; Ishihara, Kristi; Paul, Jeffery; Sampathkumaran, Uma; Zhang, Sen; Tang,YifanNASA is seeking sensing technologies for In Situ Resource Utilization (ISRU) process gases. One particular need is monitoring impurities such as hydrogen sulfide (H2S), hydrogen chloride (HCl) and hydrogen fluoride (HF) in the ISRU oxygen stream. Such sensors serve as both safety and process monitoring devices and are critical for the successful operation of ISRU systems. InnoSense, in collaboration with the University of Virginia (UVA), is developing an innovative process gas monitoring (PROMON�) device. PROMON builds on InnoSense�s proprietary single walled carbon nanotube � based sensor platform. It is further enabled by novel intermetallic nanoparticles (NPs) with precise size and composition control, serving as the key sensing element. Additionally, PROMON will include a ruggedized in-line design, providing real-time monitoring under harsh environment without reactant loss from slipstream. A similar sensor using different NPs has been developed by the team for regenerative fuel cell oxygen stream, demonstrating its capability of monitoring H2 in the concentration range of 12.5 � 12500 ppm under both ambient and high pressure (up to 300 PSI), at high temperature (up to 85�C) and high humidity (near condensing). This sets up the basis for PROMON development. In this STTR project, the initial focus is to validate the PROMON proof-of-principle, demonstrating sensor resolution, sensitivity, selectivity and durability, targeting H2S. This includes the development of (1) core sensing materials, (2) sensor units, (3) sensor test platform, and (4) benchtop prototype. In the future, we will expand targets to HCl and HF, optimize the sensor and prototype design, recognition chemistry and algorithm, and perform rigorous characterization. During future moon and mars missions, PROMON will serve as a robust sensor capable of monitoring contaminants in ISRU process gas. With its versatility, PROMON can also be adapted as a general gas detector or monitor for other analytes, toward meeting NASA needs.Item Nanomaterials Based In-Line Sensor for Ionic Silver in Spacecraft Potable Water Systems(2024 International Conference on Environmnetal Systems, 2024-07-21) Ishihara, Kristi; Liu, Yuchu; Mushfiq, Mohammad; Wang, Sijian; Zhang, Xiaowei; Paul, Jeffery; Sampathkumaran, Uma; Zhong, Mingjiang; Tang,YifanNASA is seeking sensing technologies for the in-line measurement of ionic silver (Ag+) as the biocide in spacecraft potable water systems. For human exploration missions, it is critical to monitor Ag+ concentration to maintain a sufficient yet safe level of Ag+ in the water. To address this need, InnoSense and its Small Business Technology Transfer (STTR) partner, Yale University, are developing an innovative nanomaterial-enabled Silver Monitor (SilMon�) building on InnoSense�s proprietary nanomaterials-based sensing platform and customized recognition molecules (RMs) synthesized at Yale. In the progress to date, the team has developed an interdigitated electrode (IDE)-based sensor functionalized with single-walled carbon nanotubes (SWNTs) and unique recognition molecules (RMs) for Ag+ detection. Resistance (R) measurement and scanning electron microscopy are used for IDE characterization and quality control. Currently, device yield is at least 82% for the targeted R values. A SilMon prototype hardware has been developed for systematic sensor evaluation. The prototype contains in-line flow cells for measuring IDEs. Additionally, a flow channel and control hardware were also developed to mimic both flowing and static conditions in the water processor assembly (WPA). The IDEs were evaluated using the SilMon flow cell prototype. Both deionized (DI) water and 200 ppb Ag+ were used as the background and Ag+ solutions of various concentrations served as the analyte. SilMon has demonstrated a wide response ranging from single ppb up to 4000 ppb. More efforts were focused in the targeted 100 � 600 ppb Ag+ concentration range, demonstrating good sensitivity, reversibility and baseline stability. Thorough evaluation of the sensor response, stability, recovery, and cross sensitivity towards interfering species is ongoing. Additionally, an artificial intelligence (AI) based recognition algorithm using deep neural network (DNN) is being developed to further enhance the sensitivity, selectivity and accuracy.