Irreproducibility in the triboelectric charging of insulators: evidence of a non-monotonic charge <i>versus</i> contact time relationship
Bibliographic record
Abstract
Here, we investigate the development and relaxation of static charges on the surface of plastic materials that are first brought in contact, and then macroscopically separated. Experimentalists dealing with the static electrification of insulators are aware of difficulties predictably attaining, and precisely reproducing, a given charging magnitude. Here we have observed for the first time that in homo-systems (e.g. PTFE rubbed against PTFE) charge densities reach the maximum value after a material-specific contact time. Attempts to charge a sample beyond its peak value leads to a progressive drop in charge. We propose this drop to result both from the electrostatically driven segregation of polymer ionic fragments, as well as from the discharge of unstable fragments by dielectric breakdown when a sufficiently high surface charge density is reached. We therefore highlight the general existence of two branches in the charging versus charging time curve: the assumption of a monotonous charging slope holds only left or right of the charging maxima and to achieve a specific charge density, care has to be taken to remain within one branch. Differences between materials in the tribocharging peak time are shown to reflect difference in material transfer rates and water adsorption, rather than differences in electronic factors such as the relative stability of cationic and anionic fragments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".