Detection of the corrosion degree of beverage cans using a novel electrochemical sensor
Bibliographic record
Abstract
Purpose The purpose of this paper is to report a novel electrochemical sensor designed to detect the corrosion of metal cans used for beverage packaging. Design/methodology/approach Electrochemical impedance spectroscopy (EIS) and electrochemical noise (EN) were performed to detect the corrosion degree of beverage cans that had been stored for 1 month (named s1), 3 months (named s2), 27 months (named s3) and 43 months (named s4). Findings The EIS results showed that the EIS plot of s1 samples had not developed to a characteristic of two time‐constants, indicating that the coating showed good protective performance. The EIS plots of s2, s3 and s4 showed characteristics of two time‐constants, indicating that the organic coatings of s2, s3, and s4 had lost their protective performance. EN results showed that quantities and amplitudes of transient peaks increased with the increasing storage time, indicating that an increasing degree of local corrosion occurred within the cans. A corrosion process for beverage cans is discussed and can be considered in three stages. Originality/value The designed electrochemical sensor was successfully applied to detect the performance of beverage cans and, further, provided scientific proof to evaluate the shelf life of metal cans for packaging.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".