<p dir="ltr">In today’s age of information, we are constantly informed about our surroundings by the network of distributed sensors to decide the next action. One major class of distributed sensors is wearable, implantable, and environmental (WIE) electrochemical sensors, widely used for analyte concentration measurement in personalized healthcare, environmental monitoring, smart agriculture, food, and chemical industries. Although WIE sensors offer an opportunity for prompt and prudent decisions, reliable sensing with such sensors is a big challenge. Among them, one is uncontrolled outside environment. Rapidly varying temperature, humidity, and target concentration increase noise and decrease the data reliability of the sensors. Second, because they are closely coupled to the physical world, they are subject to biofouling, radiation exposure, and water ingress which causes physical degradation. Moreover, to correct the drift due to degradation, frequent calibration is not possible once the sensor is deployed in the field. Another challenge is the energy supply needed to support the autonomous WIE sensors. If the sensor is wireless, it must be powered by a battery or an energy harvester. Unfortunately, batteries have limited lifetime and energy harvesters cannot supply power on-demand limiting their overall operation.</p><p dir="ltr">The objective of this thesis is to achieve reliable sensing with WIE sensors by overcoming the challenges of uncontrolled environment, drift or degradation, and calibration subject to limited power supplies. First, we have developed a concept of “Nernst thermometry” for potentiometric ion-selective electrodes (ISE) with which we have self-corrected concentration fluctuation due to uncontrolled temperature. Next, by using “Nernst thermometry,” we have developed a physics-guided data analysis method for drift detection and self-calibration of WIE ISE. For WIE sensor, wireless data transmission is an energy-intensive operation. To reduce unreliable data transmission, we have developed a statistical approach to monitor the credibility of the sensor continuously and transmit only credible sensor data. To understand and monitor the cause of ISE degradation, we have proposed a novel on-the-fly equivalent circuit extraction method that does not require any external power supply or complex measurements. To ensure an on-demand power supply, we have presented the concept of “signal as a source of energy.” By circuit simulation and long-term experimental analysis, we have shown that ISE can indefinitely sense and harvest energy from the analyte. We have theoretically calculated the maximum achievable power with such systems and presented ways to achieve it practically. Overall, the thesis presents a holistic approach to developing a self-sustainable WIE sensor with environmental variation correction, self-calibration, reliable data transmission, and lifelong self-powering capabilities, bringing smart agriculture and environmental sensing one step closer to reality.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/27199617 |
| Date | 10 October 2024 |
| Creators | Ajanta Saha (19827849) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/RELIABLE_SENSING_WITH_UNRELIABLE_SENSORS_FROM_PHYSICAL_MODELING_TO_DATA_ANALYSIS_TO_APPLICATIONS/27199617 |
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