Abstract
Harmful algal blooms (HABs) and eutrophication cause billions of dollars in damages and kill a large percentage of our aquatic ecosystem. According to Bernard et al. (2014), the costs of monitoring is on the order of 1 billion USD annually and Kim (2006) reported that due to HABs, Japan lost over 1 billion dollars in aquaculture.To monitor and predict such blooms, we need to cost-effectively and remotely monitor pollutants in our water bodies. One prominent pollutant that is a factor in causing harmful algal bloom is dissolved ammonia from agriculture and sewage treatment plants. To that end, we propose a novel and cost-effective sensing solution that can detect ammonia in water. Using selective membrane separation and electrochemical stripping, we are able to selectively isolate ammonium ions from dissolved river water and wastewater in our small-scale 3-chamber system. Through capacitive sensing, we can then measure with high sensitivity (28-bit resolution) and precision the concentration of ammonium ions present in our final chamber.Between the first and second chamber, there is a cation exchange membrane that allows only cations to pass through. Amongst the cations, only ammonium and hydrogen ions form gases and diffuse through the gas-permeable membrane between the second and third chamber. The ammonia gas is then dissolved into the sulfuric acid present in the third chamber. The dissolved ammonium ions change the permittivity of the overall solution and this change can be measured using highly sensitive capacitive-based sensors.Presently, we have higher than 99% selectivity in isolating ammonium ions from wastewater and river water. Our capacitive sensor has a noise floor of 0.3 femtofarad. The capacitive measurements show a linear response under 0.4 mg N/L. The combination of high selectivity and high sensitivity unlocks the ability to detect small traces of dissolved ammonia at low power draw and low costs (under $29) thus enabling the use of such methods for remote immersible ammonia sensors for environmental sensing applications along with IoT nodes.
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