Effect of Mixing Methods and Black Conductive Fillers on Properties of Natural Rubber Composites
Bibliographic record
Abstract
Carbon black, graphite, carbon nanofibers, carbon nanotubes, and metal fillers increase composite conductivity in natural rubber, which is electrically insulating. Depending on dispersion, conductive filler lowers insulating material resistivity. These materials are frequently used for electromagnetic/radio frequency interference (EMI/RFI) shielding, conductive flexible seals gaskets, and conductive mats used to prevent electrostatic damage to electronic devices. These elastomers could be used to make flexible solar cells or mechanical-to-electricity devices. Temperature, mixing time, shear rate, and cross-linking during vulcanization affect rubber electrical conductivity of composite. To study shear rate effects, vulcanizate of Natural Rubber-based composites filled with carbon black, millable carbon fiber powder, and synthetic graphite powder was prepared by open mixing (two roll mill) and close mixing (internal mixer). We compared how shear rate affects cure, stress-strain, and volume resistivity of conductive filler-based Natural Rubber composites. Increment in clearance of two roll mill during addition of rubber additives along with rubber of reduced the shearing force resulted in less dispersive and distributive mixing and stagnant points due to band formation on roll surface compared to intermix where compound movement had no stagnation point and long wings pushed material axially and two nogs pushed material in other chamber. Compared to two roll mill samples, the compound reached every point of the mixing chamber for best homogeneity, reducing cure time and improving stress-strain and volume resistivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".