A novel fabrication method of copper–reduced graphene oxide composites with highly aligned reduced graphene oxide and highly anisotropic thermal conductivity
Why this work is in the frame
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Bibliographic record
Abstract
Recently, metals with graphene and graphene oxide have been extensively used to enhance the mechanical and anisotropic thermal properties of composites. A novel facile fabrication approach of layer by layer self-assembly followed by hot press sintering was adopted to make copper-reduced graphene oxide composites. The microstructure and heat dissipation properties of pure copper and copper-reduced graphene oxide composites were analyzed with the help of SEM and continuous laser machine analysis. Thermal diffusivity of pure copper and copper-reduced graphene oxide composites was examined in different directions to measure the anisotropic thermal properties by using different volumetric percentages of reduced graphene oxide in the composites. Extraordinarily high anisotropic thermal conductivity of the copper-reduced graphene oxide composites was obtained at a very low concentration of 0.8 vol% reduced graphene oxide, with the difference between the thermal conductivity in-plane and through-plane being a factor of 8.82. Laser test results confirmed the highly anisotropic behavior of our copper-reduced graphene oxide composite with the remarkable property of heat dissipation. The three point bending test was also performed to check the flexural strength of the composites. At 0.6 vol% rGO, the flexural strength was noted (∼127 MPa), and it is 22% higher than that of pure sintered Cu. The high value of anisotropic thermal conductivity and higher flexural strength exhibited by the copper-reduced graphene oxide composite produced using a simple two-step fabrication method give us new hope to use these materials as heat sinks in thermal packaging systems.
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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 | 0.001 |
| 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 it