(Invited) Adventures in Si Nanocrystal Surface Chemistry....Controlling Optical Properties and so Much More
Bibliographic record
Abstract
Silicon nanocrystals (SiNCs) have been attracting attention as active materials in a variety of proto-type devices including, solar cells, light-emitting diodes, and photodetectors. These, and other device structures require well-defined materials with predictable properties. Traditionally SiNC surfaces are rendered processable and stable toward oxidation by employing a variations of the general hydrosilylation reaction; they all involve the addition of a silicon-hydride bond in the SiNC surface across a carbon-carbon double (or triple) bond and affords a “monolayer” attached through a robust silicon-carbon linkage. Recently, it has come to light that typical hydrosilylation conditions can afford insulating multilayers that can render SiNCs electrically inactive. This finding led us to explore alternative functionalization protocols. In doing so, we discovered that surface chemistry provides another degree of freedom with which SiNC properties may be tailored. For example, it can be used to tailor the photoluminescent response throughout the visible spectrum, used to prepare SiNC/polymer hybrids, and even induce reactivity that provides polymerization of workhorse electronic polymers without the use of expensive transition metal catalysts. This presentation will include a discussion of our recent exploration into SiNC surface chemistry, new reactive platforms that have opened the door to new functional materials, and what our discoveries mean for the future of these fascinating materials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".