Surface Nanodroplet-Based Extraction Combined with Offline Analytic Techniques for Chemical Detection and Quantification
Bibliographic record
Abstract
Liquid–liquid extraction based on surface nanodroplets can be a green and sustainable technique to extract and concentrate analytes from a sample flow. However, because of the extremely small volume of each droplet (<10 fL, tens of micrometers in base radius and a few or less than 1 μm in height), only a few in situ analytical techniques, such as surface-enhanced Raman spectroscopy, were applicable for the online detection and analysis based on nanodroplet extraction. To demonstrate the versatility of surface nanodroplet-based extraction, in this work, the formation of octanol surface nanodroplets and extraction were performed inside a 3 m Teflon capillary tube. After extraction, surface nanodroplets were collected by injecting air into the tube, by which the contact line of surface droplets was collected by the capillary force. As the capillary allows for the formation of ∼10 12 surface nanodroplets on the capillary wall, ≥2 mL of octanol can be collected after extraction. The volume of the collected octanol was enough for the analysis of offline analytical techniques such as UV–vis, GC-MS, and others. Coupled with UV–vis, reliable extraction and detection of two common water pollutants, triclosan and chlorpyrifos, was shown by a linear relationship between the analyte concentration in the sample solution and UV–vis absorbance. Moreover, the limit of detection (LOD) as low as 2 × 10 –9 M for triclosan (∼0.58 μg/L) and 3 × 10 –9 M for chlorpyrifos (∼1.05 μg/L) could be achieved. The collected surface droplets were also analyzed via gas chromatography (GC) and fluorescence microscopy. Our work shows that surface nanodroplet extraction may potentially streamline the process in sample pretreatment for sensitive chemical detection and quantification by using common analytic tools.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".