Surface nanodroplet-based nanoextraction from sub-milliliter volumes of dense suspensions
Why this work is in the frame
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Bibliographic record
Abstract
A greener analytical technique for quantifying compounds in dense suspensions is needed for wastewater and environmental analysis, chemical or bio-conversion process monitoring, biomedical diagnostics, and food quality control, among others. In this work, we introduce a green, fast, one-step method called nanoextraction for extraction and detection of target analytes from sub-milliliter dense suspensions using surface nanodroplets without toxic solvents and pre-removal of the solid contents. With nanoextraction, we achieve a limit of detection (LOD) of 10-9 M for a fluorescent model analyte obtained from a particle suspension sample. The LOD is lower than that in water without particles (10-8 M), potentially due to the interaction of particles and the analyte. The high particle concentration in the suspension sample, thus, does not reduce the extraction efficiency, although the extraction process was slowed down up to 5 min. As a proof of principle, we demonstrate the nanoextraction for the quantification of model compounds in wastewater slurry containing 30 wt% solids and oily components (i.e. heavy oils). The nanoextraction and detection technology developed in this work may be used in fast analytical technologies for complex slurry samples in the environment, industrial waste, or in biomedical diagnostics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.009 | 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 it