Porous NIR Photoluminescent Silicon Nanocrystals‐POSS Composites
Bibliographic record
Abstract
Near infrared photoluminescent porous silicon nanocrystals(ncSi)‐polyhedral oligomeric silsesquioxanes (POSS) polymer composites are synthesized using a combination of thermal hydrosilylation and polymerization between Vinyl‐POSS and hydrogen‐terminated silicon nanocrystals (ncSi:H). The synthesized materials are characterized by IR, powder X‐ray diffraction and solid‐state nuclear magnetic resonance (NMR) (13C and 29Si). The results demonstrate that the hydrosilylation–polymerization reaction proceeded to create chemically crosslinked Vinyl‐POSS‐ncSi composites in which the integrity of the POSS cages is maintained intact. Scanning electron microscope (SEM) results demonstrate that morphology of these materials depends on the weight ratio of ncSi:H to Vinyl‐POSS. Brunauer–Emmett–Teller surface area analyses establish that the composites have high surface areas ranging from 290.5 to 1047.2 m2 g−1 and pore volumes from 0.64 to 1.17 cm3 g−1. The pore sizes range from 6.08 to 3.54 nm and are dependent on the weight ratio of Vinyl‐POSS to ncSi:H. Photoluminescence spectroscopy shows that the absolute quantum yield of the nanocomposites is not affected by the weight ratio of ncSi:H to Vinyl‐POSS. Thermal gravimetric analysis results show that the POSS polymer composites with ncSi have lower thermal stability in nitrogen atmosphere as compared with the pure Vinyl‐POSS polymer. It is envisioned that future applications for these composites will likely be found in the fields of advanced materials, gas adsorption media, and biomedicine.
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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".