Terahertz photoconductivity and photocarrier dynamics in graphene–mesoporous silicon nanocomposites
Bibliographic record
Abstract
We investigate charge transport and photocarrier dynamics in graphene--mesoporous silicon nanocomposites using optical-pump terahertz-probe measurements. The nanocomposite material consists of a free-standing mesoporous silicon membrane whose specific surface is coated with a few-layer graphene shell. Temporal decays of the differential transmission measurements are reproduced using a biexponential function with an initial decay time of 5 ps and a longer decay time of about 25 ps. These decay times are significantly reduced compared to the values of ${\ensuremath{\tau}}_{1}\ensuremath{\sim}74$ ps and ${\ensuremath{\tau}}_{2}\ensuremath{\sim}730$ ps obtained for the uncoated mesoporous silicon membrane and this is attributed to the introduction of additional surface defects formed during the graphene deposition process. Based on the influence of the laser fluence on the time-resolved differential transmission curves, a capture/recombination model is proposed to describe the photocarrier dynamics in these nanocomposite materials. Frequency-dependent complex photoconductivity data curves are extracted from the terahertz waveforms taken at different optical-pump THz-probe delays. These data curves are well reproduced using a modified Drude-Smith model taking into account diffusive-restoring currents. The $c$ parameter of this model, which describes the degree of carrier localization, is about $\ensuremath{-}0.73$ for the uncoated porous Si membrane and is approaching $\ensuremath{-}1$ for graphene--mesoporous Si nanocomposites formed at temperatures above $800{\phantom{\rule{0.16em}{0ex}}}^{\ensuremath{\circ}}\mathrm{C}$. For all the nanocomposites, the characteristics of the photoconductive material, in terms of photocarrier capture/recombination time and effective mobility, are of interest for the fabrication of pulsed terahertz devices.
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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) | 0.000 | 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 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".