Tensile and conductivity properties of epoxy composites containing carbon black and graphene nanoplatelets
Bibliographic record
Abstract
Adding conductive fillers to an insulating polymer matrix produces composites with unique properties. Varying amounts of carbon black (0.33, 0.67, and 1 wt%) and graphene nanoplatelets (5, 10, 15, and 20 wt%) were added to epoxy. In addition, a few carbon black/graphene nanoplatelet/epoxy formulations were also fabricated. The conductivity and tensile properties were determined and analyzed. The single filler composites containing 5 and 10 wt% graphene nanoplatelet and 0.33 wt% carbon black could be used for electrically insulating applications. Composites containing 15 and 20 wt% graphene nanoplatelet could be used for static dissipative applications. The following composites could be used for semi-conductive applications: 0.67 wt% carbon black/epoxy, 1 wt% carbon black/epoxy, 0.33 wt% carbon black/5 wt% graphene nanoplatelet/epoxy, and 0.33 wt% carbon black/10 wt% graphene nanoplatelet/epoxy. At the 95% confidence level, the combination of 0.33 wt% carbon black with 5 wt% graphene nanoplatelet caused the composite electrical resistivity (1/electrical conductivity) to significantly decrease from ∼10 15 ohm-cm to ∼10 4 ohm-cm. It is likely that the highly branched, high surface area carbon black is forming an electrically conductive network with graphene nanoplatelets. Concerning single filler composites, adding ≤1 wt% carbon black did not significantly lower the composite tensile strain; however, adding graphene nanoplatelet did decrease tensile strain and increase modulus. One possible application for the 10 wt% graphene nanoplatelet/epoxy composite is in Polymer Core Composite Conductors for power transmission lines, which need to be electrically insulating, have improved thermal conductivity (increased from 0.2 to 0.3 W/m-K), increased tensile modulus (increased from 2.7 to 3.3 GPa), and good tensile strength (70 MPa) and strain (3.3%).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".