Pluronic Additives: A Solution to Sticky Problems in Digital Microfluidics
Bibliographic record
Abstract
Digital microfluidics (DMF) is a promising technique for carrying out miniaturized, automated biochemical assays in which discrete droplets of reagents are actuated on the surface of an array of electrodes. A limitation for DMF is nonspecific protein adsorption to device surfaces, which interferes with assay fidelity and can cause droplets to become unmovable. Here, we report the results of a quantitative analysis of protein adsorption on DMF devices by means of confocal microscopy and secondary ion mass spectrometry. This study led us to a simple and effective method for limiting the extent of protein adsorption: the use of low concentrations of Pluronic F127 as a solution additive. This strategy has a transformative effect on digital microfluidics, facilitating the actuation of droplets containing greater than 1000-fold higher protein concentrations than is possible without the additive. To illustrate the benefits of this new method, we implemented a DMF-driven protein digest assay using large concentrations (1 mg/mL) of protein-substrate. The use of Pluronic additives solves a sticky problem in DMF, which greatly expands the range of applications that are compatible with this promising technology.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".