Electrochemical Screening Spot Test Method for Detection of Nickel and Cobalt Ion Release From Metal Surfaces
Bibliographic record
Abstract
BACKGROUND: Present screening methods to rapidly detect release of nickel and cobalt ions from metallic surfaces involve colorimetric dimethylglyoxime (DMG)- and disodium-1-nitroso-2-naphthol-3,6-disulfonate-based spot tests with a cotton bud. There is a risk of false-negative test reactions because test outcomes are dependent on the pressure, area, and duration of surface wiping. OBJECTIVE: The aim of the study was to develop a miniaturized electrochemical device that uses a voltage to accelerate nickel and cobalt release from the tested item and perform an initial validation. METHODS AND RESULTS: A device was built in plastic, and its performance was investigated using 0.5 mL of test solutions of, respectively, DMG and disodium-1-nitroso-2-naphthol-3,6-disulfonate. Cotton buds that had been wetted in test solution were pressed against different metal surfaces at various voltages (0-9 V) and a range of test durations (0-120 seconds). Duplicate testing for nickel and cobalt release was also performed on a sample of 163 jewelry items. CONCLUSIONS: This novel electrochemical device makes it possible to perform nickel and cobalt ion release testing without rubbing, thereby reducing interindividual differences in testing technique. The nickel testing with the device seemed to be superior to conventional DMG spot testing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".