Browsing by Author "Liu, Yuchu"
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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.