Organic-free, versatile sessile droplet microfluidic device for chemical separation using an aqueous two-phase system
Bibliographic record
Abstract
This work presents a novel portable, versatile sessile droplet microfluidic (SDMF) device to perform liquid manipulation operations such as confining, splitting and colorimetric detection. Furthermore, chemical isolations based on an aqueous two-phase system (ATPS) for separating an analyte of choice from a complicated sample matrix can be carried out. ATPS extractions can replace conventional liquid-liquid extractions and take away the need for harmful organic solvents. Superhydrophobic (SH) surfaces were fabricated from a commercially available material, Ultra-Ever Dry® (UED®). On these SH surfaces, surface energy traps (SETs) were produced either by air plasma treatment (simultaneously) or laser micromachining (sequentially) to dock/pin an ATPS containing droplet onto the surface. Splitting of droplets or removing a precise volume of the top phase from a pinned extraction system was achieved with a sandwich-chip approach. For this, an additional SET patterned substrate was placed on top of the droplet and subsequently lifted. This multipurpose platform was used to isolate Cd from a mixture of several other metal ions (i.e. Mn, Ni, Cu, Pb, Fe) for its subsequent interference-free detection. An ATPS consisting of sodium sulfate and polyethylene glycol (PEG) as phase forming components and potassium iodine as extractant allowed separation of cadmium with an extraction efficiency of q(Cd2+) = 98.5%. Using a portable, cost-effective, smartphone-based UV/vis spectrometer, Cd was detected with a LoD of 3.4 ppm. Alternatively, the multipurpose platform can also be used as sampling platform for a benchtop UV/vis spectrometer, where a LoD of 0.53 ppm was obtained. Potential applications of the presented platform include sample preparation and separation that can be achieved by aqueous two-phase extractions, such as proteins, antibodies, DNA, cells, organic molecules and metal ions.
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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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".