Energy/Electron Transfer Switch for Controlling Optical Properties of Silicon Quantum Dots
Bibliographic record
Abstract
Abstract The superior optical properties of Silicon Quantum Dots (SQDs) have made them of increasing interest for a variety of biological and opto-electronic applications. The surface functionalization of the SQDs with aromatic ligands plays a key role in controlling their optical properties due to the interaction of the ligands with the electronic wave function of SQDs. However, there is limited reports in literature describing the impact of spacer groups connecting the aromatic chromophore to SQDs on the optical properties of the SQDs. Herein, we report the synthesis of two SQDs assemblies (1.6 nm average diameter) functionalized with perylene-3,4,9,10-tetracarboxylic acid diimide (PDI) chromophore through N-propylurea and propylamine spacers. Depending on the nature of the spacer, the photophysical measurements provide clear evidence for efficient energy and/or electron transfer between the SQDs and PDI. Energy transfer was confirmed to be the operative process when propylurea spacer was used, in which the rate was estimated to be ~2 × 109 s−1. On the other hand, the propylamine spacer was found to facilitate electron transfer process within the SQDs assembly. To illustrate functionality, the water soluble SQD-N-propylurea-PDI assembly was proven to be nontoxic and efficient for fluorescent imaging of embryonic kidney HEK293 cells and human bone cancerous U2OS cells.
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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".