Directional Manipulation of Drops and Solids on a Magneto-Responsive Slippery Surface
Bibliographic record
Abstract
The cloaking of the droplet and solid spheres by a thin ferrofluid layer forms a ferrofluid-wetting ridge, enabling the magnet-assisted directional manipulation of droplets and solid spheres on the magneto-responsive slippery surface. Understanding the interplay of various forces governing motion unravels the manipulation mechanism. The transportation characteristics of droplets and solid spheres on such surfaces enable their controlled manipulation in multiple applications. Here, we prepare magneto-responsive slippery surfaces by using superhydrophobic coatings on glass slides, creating a porous network and impregnating them with ferrofluid. Using a permanent magnet (and its translation) in the proximity of these surfaces, we manipulate the motion of liquid drops and solid spheres. Upon dispensing the droplet on the magneto-responsive slippery surface, the droplet is cloaked by a thin ferrofluid layer and forms a ferrofluid wetting ridge. Incorporating the magnetic field creates a magnetic-nanoparticle-rich zone surrounding the ferrofluid ridge. Thereafter, the motion of the magnet gives rise to the movement of the droplet. We found that the interplay of the magnetic force and viscous drag leads to the magnetic manipulation of droplets in a controlled fashion up to a certain magnet speed. Increasing the magnet speed further results in the uncontrolled motion of the droplet, where the droplet cannot follow the magnet trajectory. Moreover, we delineate multifunctional droplet manipulations, such as trapping, pendant droplet manipulation, coalescence, and microchemical reactions, which have wide engineering applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".