No abstract

Originally developed for use in controlled laboratory settings, potentiometric ionselective electrode (ISE) sensors have recently been deployed for continuous, in situ measurement of analyte concentration in agricultural (e.g., nitrate), environmental (e.g., ocean acidification), industrial (e.g., wastewater), and health-care sectors (e.g., sweat sensors). However, due to uncontrolled temperature and lack of frequent calibration in these field applications, it has been difficult to achieve accuracy comparable to the laboratory setting. In this paper, we propose a novel temperature self-calibration method where the ISE sensors can serve as their own thermometer and therefore precisely measure the analyte concentration in the field condition by compensating for the temperature variations. We validate the method with controlled experiments using pH and nitrate ISEs, which use the Nernst principle for electrochemical sensing. We show that, using temperature self-calibration, pH and nitrate can be measured within 0.3% and 5% of the true concentration, respectively, under varying concentrations and temperature conditions. Moreover, we perform a field study to continuously monitor the nitrate concentration of an agricultural field over a period of 6 days. Our temperature self-calibration approach determines the nitrate concentration within 4% of the ground truth measured by laboratory-based high-precision nitrate sensors. Our approach is general and would allow battery-free temperature-corrected analyte measurement for all Nernst principle-based sensors being deployed as wearable or implantable sensors.

Semi-transparent photovoltaic devices for building integrated applications have the potential to provide simultaneous power generation and natural light penetration. CuIn1−xGaxSe2 has been established as a mature technology for thin-film photovoltaics; however, its potential for Semi-Transparent Photovoltaics (STPV) is yet to be explored. In this paper, we present its carrier transport physics explaining the trend seen in recently published experiments. STPV requires deposition of films of only a few hundred nanometers to make them transparent and manifests several unique properties compared to a conventional thin-film solar cell. Our analysis shows that the short-circuit current, Jsc, is dominated by carriers generated in the depletion region, making it nearly independent of bulk and back-surface recombination. The bulk recombination, which limits the open-circuit voltage Voc, appears to be higher than usual and attributable to numerous grain boundaries. When the absorber layer is reduced below 500 nm, grain size reduces, resulting in more grain boundaries and higher resistance. This produces an inverse relationship between series resistance and absorber thickness. We also present a thickness-dependent model of shunt resistance showing its impact in these ultra-thin devices. For various scenarios of bulk and interface recombinations, shunt and series resistances, AVT, and composition of CuIn1−xGaxSe2, we project the efficiency limit, which—for most practical cases—is found to be ≤10% for AVT≥25%.

In the modern industrial setting, there is an increasing demand for all types of sensors. The demand for both the quantity and quality of sensors is increasing annually. Our research focuses on thin-film nitrate sensors in particular, and it seeks to provide a robust method to monitor the quality of the sensors while reducing the cost of production. We are researching an image-based machine learning method to allow for real-time quality assessment of every sensor in the manufacturing pipeline. It opens up the possibility of real-time production parameter adjustments to enhance sensor performance. This technology has the potential to significantly reduce the cost of quality control and improve sensor quality at the same time. Previous research has proven that the texture of the topical layer (ion-selective membrane (ISM) layer) of the sensor directly correlates with the performance of the sensor. Our method seeks to use the correlation so established to train a learning-based system to predict the performance of any given sensor from a still photo of the sensor active region, i.e. the ISM. This will allow for the real-time assessment of every sensor instead of sample testing. Random sample testing is both costly in time and labor, and therefore, it does not account for all of the individual sensors. Sensor measurement is a crucial portion of the data collection process. To measure the performance of the sensors, the sensors are taken to a specialized lab to be measured for performance. During the measurement process, noise and error are unavoidable; therefore, we generated credibility data based on the performance data to show the reliability of each sensor performance signal at each sample time. In this paper, we propose a machine learning based method to predict sensor performance using image features extracted from the non-contact sensor images guided by the credibility data. This will eliminate the need to test every sensor as it is manufactured, which is not practical in a high-speed roll-to-roll setting, thus truely enabling a certify as built framework.