Combinatorial SiO <sub>2</sub> ‐Encapsulated Quantum Dot Nanoparticles and their Use in Spectral Unmixing Analysis
Bibliographic record
Abstract
Linear unmixing spectral analysis is a technique where signals from tens of fluorophores can be deconvoluted to increase multiplexing by 4–5‐fold. For the mathematical algorithm‐driven analysis to be applied to analytical assays, there is a need to develop spectrally engineered nanoparticle probes. Herein, silica‐encapsulated quantum dot (QD‐SiO 2 ) nanoparticles with tunable spectral emissions are presented. The mechanism and factors for incorporating hydrophobic quantum dots (QDs) in silica in the reverse microemulsion synthesis are investigated, including 1 H NMR study on the interaction of ligands on QDs with the surfactant. The optimized synthesis reduces Förster resonance energy transfer between QDs in silica particles. In combination with linear unmixing analysis, nanoparticles that encapsulate varying ratios of different color QDs enable multiplexing capability up to 8. Their size of ca. 30 nm can enable in vitro imaging in addition to the use in existing immunoassays and analytical platforms.
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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".