Two-Electrode Electrochemical Impedance Sensor: Part 2—Impedance Measurement and Simulation of Coatings on Nonmetal Substrates
Bibliographic record
Abstract
Electrochemical impedance spectroscopy (EIS) has been studied extensively as a quantitative technique for evaluating protective coatings, which degrade unavoidably, regardless of their substrate type. However, most EIS studies focus on coatings on metal substrates. Consequently, on nonmetal substrates, coating evaluation is still lacking a quantitative method. Examples of the coatings on nonmetal substrates include not only those on nonconductive substrates such as plastics and composites but also the top layer of multilayer coatings on metal substrate. Here, with a two-cell EIS (TCEIS) configuration, EIS techniques are made available for evaluating coatings on nonconductive substrates, which is a progress toward future quantitative evaluation of these coatings. To test the feasibility of the TCEIS, impedance measurements were carried out on two different coatings on various non-metal and metal (for comparison) substrates. Measurements were successful on several nonmetal substrates and all the metal substrates. A simplified two-dimensional finite element analysis (FEA) model was used to assist the interpretation of the results and study the effects on the TCEIS measurements from the relative size and position of a coating defect and the two cells of TCEIS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".