Encapsulated Nanodroplets for Enhanced Fluorescence Detection by Nano‐Extraction
Bibliographic record
Abstract
Enhancement of the detection signal of fluorescence microscopy in highly diluted solutions is of great importance in chemical analysis, sensing, and bioassay applications. Surface nanodroplets with atto- to femto-liter volumes are promising tools for sensitive online detection by integrating their extremely efficient nano-extraction and optical advantages. In this paper, the development of novel basic units of nanodroplets-in-a-microdroplet by simple solvent exchange is reported. The encapsulated nanodroplets are applied for ultrasensitive and online detection in fluorescence imaging. The biphasic nature of the droplet composite enables simultaneous extraction and enrichment of both hydrophobic and hydrophilic compounds. Furthermore, the desirable lensing effect of the curved surface of the nanodroplets enhances the collection of light emitted from the fluorophore extracted in the droplets by ≈60-fold, allowing sensitive and quantitative analysis of the fluorophore using fluorescence microscopy. The results highlight the potential of encapsulated nanodroplets as a simple and innovative method of signal enhancement in chemical analysis. By integrating selective concentration, extraction, and sensitive detection, the encapsulated nanodroplets reported here may have broad applications in many chemical and biological matrices.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".