Percolation for Coated Conductive Paper: Electrical Conductivity as a Function of Volume Fraction of Graphite and Carbon Black
Bibliographic record
Abstract
Conductive papers were prepared via surface coating with graphite or carbon black using either carboxylated styrene butadiene latex or starch as the binder. It is of practical interest to determine the percolation threshold for the coated paper product made using a binary system consisting of conductive filler and binder. In this study, the electroconductivity threshold of various conductive papers was determined based on experimental data according to the percolation law. Results showed that the conductivity of coated, conductive paper is a function of the volume fraction of conductive filler, which can be described well by the percolation theory. The percolation thresholds of graphite/latex, graphite/starch, carbon black/latex, and carbon black/starch coatings were 17.66, 12.36, 11.71, and 8.69 vol.%, respectively. At concentrations higher than the percolation threshold, the conductivity of conductive paper using graphite as the conductive filler was much higher than that achieved using carbon black at a similar volume fraction. The present paper has significant practical implications for conductive paper technology using graphite filler based on surface coating technology.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".