Development of Flexible Moisture Sensors Based on the Corrosion and Degradation of Conductive Substrates
Bibliographic record
Abstract
Water and moisture sensing are becoming essential features measured in clean energy and transportation applications. In this study, we develop sensors with the ability to detect moisture through two distinct conductive technologies: (1) a change in morphology using water-soluble polymer composite foams and (2) a rust-induced change or resistance change caused by the corrosion of a metal substrate. Five different foam sensors were successfully fabricated, tested, and determined as functional moisture sensors following their time and relative humidity responses. The sensors’ sensitivity was calculated, and a maximum sensitivity of 3.61 kΩ/RH % was achieved. The electrical properties, foam morphologies, and chemical, thermal, and mechanical properties of the foams were measured and compared. The second sensing technology encompasses a magnesium–copper galvanic system which when in contact with water for extended periods of time will corrode (i.e., convert the metal into metal oxide), causing an irreversible change through an increase in resistance, subsequently alerting the user of possible water flooding. The metallic sensors were tested at three different outdoor temperatures (0, 23, and 50 °C) in order to characterize the influence of temperature. They were also tested with a direct force where corrosion was accelerated. Chipless microwave resonators were utilized as platforms for investigation of the performance of the developed sensors. Both technologies presented act as both the substrate and sensing material.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 source (direct Gemma or distilled Codex), 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".