A Digital Microfluidic Method for in Situ Formation of Porous Polymer Monoliths with Application to Solid-Phase Extraction
Bibliographic record
Abstract
We introduce the marriage of two technologies: digital microfluidics (DMF), a technique in which droplets are manipulated by application of electrostatic forces on an array of electrodes coated by an insulator, and porous polymer monoliths (PPMs), a class of materials that is popular for use for solid-phase extraction and chromatography. In this work, circular PPM discs were formed in situ by dispensing and manipulating droplets of monomer solutions to designated spots on a DMF device followed by UV-initiated polymerization. We used PPM discs formed in this manner to develop a digital microfluidic solid-phase extraction (DMF-SPE) method, in which PPM discs are activated and equilibrated, samples are loaded, PPM discs are washed, and the samples are eluted, all using microliter droplets of samples and reagents. The new method has extraction efficiency (93%) comparable to that of pipet-based ZipTips and is compatible with preparative sample extraction and recovery for on-chip desalting, removal of surfactants, and preconcentration. We anticipate that DMF-SPE may be useful for a wide range of applications requiring preparative sample cleanup and concentration.
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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) | 0.000 | 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) | 0.001 | 0.000 |
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".