From Phenylsiloxane Polymer Composition to Size-Controlled Silicon Carbide Nanocrystals
Bibliographic record
Abstract
Silicon carbide (SiC) has become a very important material for many high-performance applications as a result of its exceptional material properties. The emergence of size-dependent properties in SiC nanocrystals (SiC-NCs), together with the increased surface area intrinsic to nanocrystals, has led to a variety of new possible applications, including optoelectronics and hybrid materials. Here we report the straightforward preparation of size-controlled oxide-embedded and freestanding SiC-NCs from the reductive thermal processing of compositionally controlled phenylsiloxane polymers. Compositional tuning of the polymers is achieved by varying the relative amounts of phenyl trichlorosilane (C(6)H(5)SiCl(3)) and silicon tetrachloride (SiCl(4)) during hydrolysis and cocondensation. Thermal processing of the resulting compositionally controlled condensation copolymers yields oxide-embedded SiC-NCs whose average diameter is dependent on the relative C(6)H(5)SiCl(3) concentration in the initial precursor mixture. A liberation procedure for preparing size-controlled freestanding SiC-NCs that involves oxidation of matrix carbon and subsequent chemical etching of the matrix is also presented.
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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) | 0.001 | 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".