Enhancing the mechanical properties of fluororubber through the formation of crosslinked networks with aminated multi-walled carbon nanotubes and reduced graphene oxides
Bibliographic record
Abstract
To manipulate the mechanical properties of fluororubber (FKM) composites, aminated multi-walled carbon nanotubes (MWCNT-A) and reduced graphene oxide (RGO) were introduced to synergistically create additional crosslinking between the FKM networks. Effects of the improved crosslinking on the mechanical, electrical and thermal properties of the FKM composites were systematically investigated in this research. The results showed that additional linkages were created due to the interactions between FKM and MWCNT-A and between MWCNT-A and RGO, resulting the thus-prepared FKM/MWCNT-A/RGO composites with excellent mechanical, electrical and thermal properties. Compared with the pristine FKM composite, the tensile strength, modulus at 100% strain, hardness, thermal and electrical conductivity of the FKM/MWCNT-A/RGO composite exhibited great increment of 43.8%, 656.3%, 34.5%, 30.3% and eight orders of magnitude, respectively. This research demonstrates that the construction of additional crosslinking with nanofillers represents an effective and promising route for a more broad range of industrial applications of FKM.
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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 | 0.000 |
| 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 | 0.000 |
| 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".