Effect of Nanographene Coating on the Seebeck Coefficient of Mesoporous Silicon
Bibliographic record
Abstract
Nanographene-mesoporous silicon (G-PSi) composites have recently emerged as a promising class of nanomaterials with tuneable physical properties. In this study, we investigated the impact of nanographene coating on the Seebeck coefficient of mesoporous silicon (PSi) obtained by varying two parameters: porosity and thickness. To achieve this, an electrochemical etching process on p + doped Si is presented for the control of the parameters (thicknesses varying from 20 to 160 µm, and a porosity close to 50%), and for nanographene incorporation through chemical vapor deposition. Raman and XPS spectroscopies confirmed the presence of nanographene on PSi. Using a homemade ZT meter, the Seebeck coefficient of the p + doped Si matrix was evaluated at close to 100 ± 15 µV/K and confirmed by UPS spectroscopy analysis. Our findings suggest that the Seebeck coefficient of the porous Si can be measured independently from that of the substrate by fitting measurements on samples with a different thickness of the porous layer. The value of the Seebeck coefficient for the porous Si is of the order of 750 ± 40 µV/K. Furthermore, the incorporation of nanographene induced a drastic decrease to approximately 120 ± 15 µV/K, a value similar to that of its silicon substrate.
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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 | 0.003 | 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 | 0.000 |
| 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".