Dispersive Liquid–Liquid Microextraction (DLLME) Coupled with Droplet Evaporation on an Omniphobic Nano/Micro Structured Porous Microfiber Membrane for Surface-Enhanced Raman Spectroscopy
Bibliographic record
Abstract
This study demonstrates an ultrasensitive method for surface-enhanced Raman spectroscopy (SERS) based on dispersive liquid–liquid microextraction (DLLME) coupled with droplet evaporation on a functionalized omniphobic microfiber membrane. The omniphobicity of the membrane was achieved by the fabrication of fractal structures using silica nanoparticles and lowering surface energy. The approach involves the preconcentration of the analyte in a ternary mixture of water–oil–ethanol, followed by collecting microemulsion droplets in an omniphobic porous microfiber membrane. A supraparticle of plasmonic nanoparticles formed from droplet evaporation on the membrane acts as hotspots for SERS detection. This method achieves exceptionally low detection limits of 10 –6 –10 –14 M for four nonvolatile and three volatile analytes of significance to food safety and environment after oil spill. The intensity of SERS signals depends on the partition coefficient of the analyte in water and oil microdroplets, the response of functional groups in the chemical structure of the analytes to Raman scattering, and the wetting properties of the microfiber membrane. The preparation method demonstrated in this work may have broad applications in the detection of pesticides in environmental monitoring, pharmaceutical waste, and hydrocarbon pollutants.
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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) | 0.001 | 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 | 0.000 |
| 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".