Magnetically manipulated droplet splitting on a 3D-printed device to carry out a complexometric assay
Bibliographic record
Abstract
A method for performing droplet actuation, splitting, and dispensing using only magnetic force and physical confinement is reported. The combination of low-friction superhydrophobic surfaces and droplets containing superparamagnetic particles is demonstrated to reliably dispense droplets with a precision (≤6%) similar to standard air-displacement pipettes. The 3D printed microfluidic chips incorporate individual wells, a weir structure and differential channel depths to facilitate droplet splitting in differing ratios. Both empirical observations and numerical simulations show that the splitting is a combination of wetting and pressure differences. The method enables a parent drop to be dispensed and split into droplets ranging in size from 5-20 μL using different well volumes. Once dispensed/split the droplets can be further actuated, merged and mixed. An EDTA-based complexometric colorimetric titration for water hardness is conducted on-chip. The degree of colour change is then determined utilizing a cell phone camera and image analysis and used to calculate water hardness; this measurement was found to agree with the traditional, larger scale method. The simple, robust dispensing method is adaptable to other digital microfluidic assays.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".