Functional Femtoliter Droplets for Ultrafast Nanoextraction and Supersensitive Online Microanalysis
Bibliographic record
Abstract
Abstract A universal femtoliter surface droplet‐based platform for direct quantification of trace of hydrophobic compounds in aqueous solutions is presented. Formation and functionalization of femtoliter droplets, concentrating the analyte in the solution, are integrated into a simple fluidic chamber, taking advantage of the long‐term stability, large surface‐to‐volume ratio, and tunable chemical composition of these droplets. In situ quantification of the extracted analytes is achieved by surface‐enhanced Raman scattering (SERS) spectroscopy by nanoparticles on the functionalized droplets. Optimized extraction efficiency and SERS enhancement by tuning droplet composition enable quantitative determination of hydrophobic model compounds of rhodamine 6G, methylene blue, and malachite green with the detection limit of 10−9to 10−11mand a large linear range of SERS signal from 10−9to 10−6mof the analytes. The approach addresses the current challenges of reproducibility and the lifetime of the substrate in SERS measurements. This novel surface droplet platform combines liquid–liquid extraction and highly sensitive and reproducible SERS detection, providing a promising technique in current chemical analysis related to environment monitoring, biomedical diagnosis, and national security monitoring.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".