Extreme temperature stability of thermally insulating graphene-mesoporous-silicon nanocomposite
Bibliographic record
Abstract
Abstract We demonstrate the thermal stability and thermal insulation of graphene-mesoporous-silicon nanocomposites (GPSNC). By comparing the morphology of GPSNC carbonized at 650 °C as-formed to that after annealing, we show that this nanocomposite remains stable at temperatures as high as 1050 °C due to the presence of a few monolayers of graphene coating on the pore walls. This does not only make this material compatible with most thermal processes but also suggests applications in harsh high temperature environments. The thermal conductivity of GPSNCs carbonized at temperatures in the 500 °C–800 °C range is determined through Raman spectroscopy measurements. They indicate that the thermal conductivity of the composite is lower than that of silicon, with a value of 13 ± 1 W mK −1 at room temperature, and not affected by the thin graphene layer, suggesting a role of the high concentration of carbon related-defects as indicated by the high intensity of the D-band compared to G-band of the Raman spectra. This morphological stability at high temperature combined with a high thermal insulation make GPSNC a promising candidate for a broad range of applications including microelectromechanical systems and thermal effect microsystems such as flow sensors or IR detectors. Finally, at 120 °C, the thermal conductivity remains equal to that at room temperature, attesting to the potential of using our nanocomposite in devices that operate at high temperatures such as microreactors for distributed chemical conversion, solid oxide fuel cells, thermoelectric devices or thermal micromotors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".