Spectrotemporal characterization of photoluminescent silicon nanocrystals and their energy transfer to dyes
Bibliographic record
Abstract
Silicon nanocrystals (SiNCs) are a promising material for applications in bioanalysis and imaging. Compared to other types of semiconductor nanocrystals, the development and characterization of energy transfer (ET) configurations with SiNCs has been far more limited, resulting in an equally limited understanding of this process and its SiNC-specific nuances. Here, we present a systematic and detailed study of ET between SiNCs and dyes. A combination of spectroelectrophoresis and time-gated and time-resolved photoluminescence measurements were used to characterize the photophysical properties of ensembles of SiNCs and gain insight into how these properties varied as a function of nanocrystal size. ET between SiNC donors and a series of non-fluorescent Black Hole Quencher (BHQ) dyes and fluorescent sulfo-Cyanine 5.5 dye acceptors was evaluated in terms of spectral properties, wavelength-resolved efficiencies, trends with spectral overlap integral, and differences between two methods of BHQ association with the SiNCs. The overall results were consistent with a Förster resonance energy transfer (FRET) mechanism where the polydispersity of the SiNCs had a significant impact on the observed ET: the choice of wavelength and timing parameters were important, and ensemble measurements represented an average of heterogeneous ET behaviors. Prospective advantages and disadvantages of SiNCs as ET donors are discussed. This study serves as a foundation for the continued and optimized development of ET configurations with SiNCs.
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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) | 0.000 | 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".