Digital Microfluidic Magnetic Separation for Particle-Based Immunoassays
Bibliographic record
Abstract
We introduce a new format for particle-based immunoassays relying on digital microfluidics (DMF) and magnetic forces to separate and resuspend antibody-coated paramagnetic particles. In DMF, fluids are electrostatically controlled as discrete droplets (picoliters to microliters) on an array of insulated electrodes. By applying appropriate sequences of potentials to these electrodes, multiple droplets can be manipulated simultaneously and various droplet operations can be achieved using the same device design. This flexibility makes DMF well-suited for applications that require complex, multistep protocols such as immunoassays. Here, we report the first particle-based immunoassay on DMF without the aid of oil carrier fluid to enable droplet movement (i.e., droplets are surrounded by air instead of oil). This new format allowed the realization of a novel on-chip particle separation and resuspension method capable of removing greater than 90% of unbound reagents in one step. Using this technique, we developed methods for noncompetitive and competitive immunoassays, using thyroid stimulating hormone (TSH) and 17β-estradiol (E2) as model analytes, respectively. We show that, compared to conventional methods, the new DMF approach reported here reduced reagent volumes and analysis time by 100-fold and 10-fold, respectively, while retaining a level of analytical performance required for clinical screening. Thus, we propose that the new technique has great potential for eventual use in a fast, low-waste, and inexpensive instrument for the quantitative analysis of proteins and small molecules in low sample volumes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".