Field deployable impedance-based corrosion sensor
Bibliographic record
Abstract
Electrochemical impedance spectroscopy (EIS) has been used in various applications, such as metal corrosion monitoring. However, many conventional corrosion monitoring setups are bulky and inconvenient for in-situ testing. The purpose of this work is to reduce the size of the whole corrosion monitoring system. We utilized EIS to design a field deployable impedance-based corrosion sensor (FDICS), capable of performing in-situ EIS analysis. Experiments verified the sensor's accuracy, and the results showed that the sensor performed similarly to a bench-top EIS machine when we tested on circuit models. Furthermore, we used the proposed FDICS to monitor a metal corrosion experiment and performed EIS. The result showed that the proposed FDICS is able to obtain the sample's impedance spectroscopy, which could help researchers test its corrosion severity on a metallic sample in-situ. Compared to other bulky conventional setups, our device eliminates the design complexity while still showing insights into the corrosion reaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".