In Situ Monitoring of Corrosion under Insulation Using Electrochemical and Mass Loss Measurements
Bibliographic record
Abstract
Corrosion under insulation (CUI) refers to the external corrosion of piping and vessels when they are encapsulated in thermal insulation. To date, very limited information (especially electrochemical data) is available for these “difficult-to-test” CUI conditions. This study was aimed at developing a novel electrochemical sensing method for in situ CUI monitoring and analysis. Pt-coated Ti wires were used to assemble a three-electrode electrochemical cell over a pipe surface covered by thermal insulation. The CUI behavior of X70 carbon steel (CS) and 304 stainless steel (SS) under various operating conditions was investigated using mass loss, linear polarization resistance (LPR), and electrochemical impedance spectroscopy (EIS) measurements. It was found that both the consecutive wet and dry cycles and cyclic temperatures accelerated the progression of CUI. LPR and EIS measurements revealed that the accelerated CUI by thermal cycling was due to the reduced polarization resistance and deteriorated corrosion film. Enhanced pitting corrosion was observed on all tested samples after thermal cycling conditions, especially for CS samples. The proposed electrochemical technique demonstrated the ability to obtain comparable corrosion rates to conventional mass loss data. In addition to its potential for in situ CUI monitoring, this design could be further applied to rank alloys, coatings, and inhibitors under more complex exposure conditions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".