One‐Step Nanoextraction and Ultrafast Microanalysis Based on Nanodroplet Formation in an Evaporating Ternary Liquid Microfilm
Bibliographic record
Abstract
Abstract Preconcentration is key for detection from an extremely low concentration solution, but requires separation steps from a large volume of samples using extracting solvents. Here, a simple approach is presented for ultrafast and sensitive microanalysis from a tiny volume of aqueous solutions. In this approach, liquid–liquid nanoextraction in an evaporating thin liquid film on a spinning substrate is coupled with quantitative analysis in one step. The approach is exemplified using a liquid mixture comprising a target compound to be analyzed in water, mixed with extractant oil and co‐solvent ethanol. With rapid evaporation of ethanol, nanodroplets of oil form spontaneously in the film. The compounds are highly concentrated by liquid evaporation and meanwhile extracted to nanodroplets. A detection limit of nanomolar to picomolar is demonstrated for fluorescent model compounds in only ≈5 µL of solution with the entire process taking ≈10 s. The combination of nanoextraction and infrared microscopy also enables simultaneous chemical identification. The dynamics of thin film evaporation are revealed using fast imaging. The principle behind this approach is general, providing a powerful technique for fast and sensitive chemical analysis of a vast library of compounds for environment monitoring, national security, early diagnosis, and many other applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".