In situ IR-spectroscopy as a tool for monitoring the radical hydrosilylation process on silicon nanocrystal surfaces
Bibliographic record
Abstract
Among a variety of SiNC functionalization methods, radical initiated grafting is very promising due to its straightforward nature and low propensity to form surface oligomers. In the present study, we employed in situ IR spectroscopy in combination with visible light transmittance measurements to investigate the radical induced grafting process on the well-defined SiNCs. Our findings support the proposed model: unfunctionalized hydride-terminated SiNCs form agglomerates in organic solvents, which break up during the grafting process. However, clearing of the dispersion is not a valid indicator for complete surface functionalization. Furthermore, radical-initiated grafting reactions in which azobisisobutyronitrile (AIBN) is the initiator are strongly influenced by external factors including initiator concentration, grafting temperature, as well as substrate steric demand. The monomer concentration was proven to have a low impact on the grafting process. Based on these new insights an underlying mechanism could be discussed, offering an unprecedented view on the functionalization of SiNC surfaces via radical initiated hydrosilylation.
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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 | 0.001 |
| 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".