<p dir="ltr">We propose a new way to improve the efficiency of the quality review process using the image-based learning approach. This is achievable because we have learned that there is a correlation between the sensor’s surface texture and its performance. Also, the fabrication parameters directly affect the thickness of the ion-selective membrane (ISM) of the nitrate sensors and therefore affect the texture on the sensor’s surface. Given that information, we can quickly predict the sensor performance and make rapid modifications to the fabrication parameters using our image-based prediction model.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/24294997 |
| Date | 12 October 2023 |
| Creators | Xihui Wang (5930924) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Thin-film_Nitrate_Sensor_Performance_Prediction_Based_on_Pre-processed_Sensor_Images/24294997 |
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