Aluminum Beverage Can Lid Testing Method Under Real Conditions with EIS Online Monitoring
Bibliographic record
Abstract
The coatings on aluminum beverage can lid interiors can be prone to long-term degradation due to the high impact forces during fabrication and the corrosive nature of beverages. Multi-month tests are required to assess their resistance to this degradation. The purpose of this work is to introduce an accelerated can lid testing method with online Electrochemical Impedance Spectroscopy (EIS) monitoring under real conditions and with real beverages that can imitate the lengthy pack tests typically employed. Twelve reactors were constructed and incorporated in a testing setup, EIS spectra were collected and analyzed using equivalent circuit models. The effect of test duration, pressure, temperature, and beverage on the degradation of the lids were investigated. The results showed that both temperature and pressure accelerate degradation. In addition, 10-day accelerated tests with EIS online monitoring were compared to 10-day and 6-month pack tests. Metal Exposure and aluminum concentration from the pack tests were correlated with the pore resistance, the charge transfer resistance, and the double layer capacitance of the lids extracted from the EIS spectra. The developed method has the potential to mimic the multi-month pack tests and offers a quicker, more insightful, and less laborious alternative for the lid degradation assessment. Ultimately, this method could help in improving the longevity and quality of aluminum beverage cans.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".