Electrical Tweezer for Droplet Transportation, Extraction, Merging and DNA Analysis
Bibliographic record
Abstract
Droplets of aqueous solutions distributed in an immiscible oil phase are increasingly used and investigated as a means to handle and assay small volumes of samples. The primary attraction of this method is that surface interactions are kept to a minimum, and changes in sample concentration, especially due to adsorption to the walls, are avoided. Microfluidic methods to generate, transport, merge, split and perform reactions in droplets were developed recently. These methods depend on the continuous flow of the two phases involved inside closed microfluidic channels. Alternatively, an electrowetting phenomenon was also exploited to control the movement of droplets between two solid substrates. However, there are some situations where small volume sample transport and assaying are required in open systems. Here, we demonstrate a simple electromechanical probe (tweezers) that is capable of manipulating a small aqueous droplet in a bi-layer oil phase. The tweezer consists of two needles positioned close to each other and uses polarization of the aqueous droplet in an applied electrical field to confine the droplet between the needles with minimal solid contact. Mechanical motion of the tweezer can be used to transport the droplet to various positions. Operations such as aliquoting, merging and transport are demonstrated. Finally, this method was used to perform a DNA amplification assay where droplets of the sample and the amplification mixture are aliquoted separately, mixed and amplified using an in-situ heater. This electromechanical tweezer is of interest in low-throughput, small-volume biological and chemical assays where the investigator requires direct and open access to the samples.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".