Influence of HSiO<sub>1.5</sub> Sol−Gel Polymer Structure and Composition on the Size and Luminescent Properties of Silicon Nanocrystals
Bibliographic record
Abstract
We report the preparation of SiO 2 -embedded silicon nanocrystals (Si-NCs) from the thermal processing of sol−gel polymers derived from trichlorosilane (HSiCl 3 ). Straightforward addition of water to HSiCl 3 generates a cross-linked (HSiO 1.5 ) n sol−gel polymer suitable for the generation of bulk quantities of SiO 2 -embedded Si-NCs. It is shown that structural differences between the present (HSiO 1.5 ) n polymer and hydrogen silsesquioxane (HSQ) result in controllable differences in the resulting oxide-embedded Si-NCs produced from these precursors. A polymer structure/NC size relationship is further delineated through the preparation and evaluation of methyl-modified (HSiO 1.5 ) n (CH 3 SiO 1.5 ) m ( m ≪ n, m + n = 1) sol−gel copolymers, in which a low concentration of methyl groups acts as a polymer network modifier and influences the formation of Si-NCs during thermal processing. Si-NC size is readily tailored by controlled variations to peak processing temperature for (HSiO 1.5 ) n and composition ( n and m ) for (HSiO 1.5 ) n (CH 3 SiO 1.5 ) m . Furthermore, the present Si-NCs exhibit size-dependent photoluminescence (PL) in accordance with the principles of quantum confinement. Freestanding Si-NCs are obtained through chemical etching of the oxide matrix and exhibit tunable PL throughout the visible spectrum.
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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".