Graphene–Mesoporous Si Nanocomposite as a Compliant Substrate for Heteroepitaxy
Bibliographic record
Abstract
The ultimate performance of a solid state device is limited by the restricted number of crystalline substrates that are available for epitaxial growth. As a result, only a small fraction of semiconductors are usable. This study describes a novel concept for a tunable compliant substrate for epitaxy, based on a graphene-porous silicon nanocomposite, which extends the range of available lattice constants for epitaxial semiconductor alloys. The presence of graphene and its effect on the strain of the porous layer lattice parameter are discussed in detail and new remarkable properties are demonstrated. These include thermal stability up to 900 °C, lattice tuning up to 0.9 % mismatch, and compliance under stress for virtual substrate thicknesses of several micrometers. A theoretical model is proposed to define the compliant substrate design rules. These advances lay the foundation for the fabrication of a compliant substrate that could unlock the lattice constant restrictions for defect-free new epitaxial semiconductor alloys and 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".