“Particle-Free” Magnetic Actuation of Droplets on Superhydrophobic Surfaces Using Dissolved Paramagnetic Salts
Bibliographic record
Abstract
Magnetic actuation is a droplet manipulation mechanism in digital microfluidics (DMF), where droplets can be actuated over a (super)hydrophobic surface with a magnetic force. Superparamagnetic particles or ferromagnetic liquids are added to the droplets to provide a “handle” by which the magnet can exert a force on the droplet. In this study, we present a novel method of magnetic manipulation, where droplets instead contain paramagnetic salts with molar magnetic susceptibilities (χ m ) approximately ≈10 000× < that for superparamagnetic particles. Droplet actuation is facilitated by low surface friction on fluorous silica nanoparticle-based superhydrophobic coatings, where <2 μN is required for reproducible droplet actuation. Different paramagnetic salts with χ m from ≈4500 to 72 000 (× 10 –6 cm 3 mol –1 ) were used to make aqueous solutions of different concentration and tested for droplet actuation and sliding angle using permanent magnets (1.8–2.1 kG). Paramagnetic salts are compared in terms of solubility, minimum required concentration, and maximum droplet velocity before disengagement. There is a strong correlation between the magnetic susceptibility of the salt solution, its concentration, and ease of actuation. As an application example, droplets containing a paramagnetic salt and doxorubicin (leukemia drug) are magnetically actuated and interrogated using laser-induced fluorescence. Signal attenuation due to the MnCl 2 salt was examined, and the Stern–Volmer quenching constant was determined.
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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".