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Record W3023069569 · doi:10.1039/d0cp01317j

Irreproducibility in the triboelectric charging of insulators: evidence of a non-monotonic charge <i>versus</i> contact time relationship

2020· article· en· W3023069569 on OpenAlexaff
Jinyang Zhang, Chao Su, Fergus J. M. Rogers, Nadim Darwish, Michelle L. Coote, Simone Ciampi

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsBentley (Canada)
FundersAustralian Research Council
KeywordsTriboelectric effectCharge (physics)Monotonic functionCondensed matter physicsMaterials scienceMechanicsPhysicsComposite materialMathematicsQuantum mechanicsMathematical analysis

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score1.000
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.034
GPT teacher head0.261
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designBench or experimental
DomainReproducibility
GenreEmpirical

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".

Quick stats

Citations18
Published2020
Admission routes1
Has abstractyes

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