Methods for Mechanical Filtration and Automated Droplet Monitoring in Electrowetting on Dielectric Devices
Bibliographic record
Abstract
Discrete flow microfluidic devices have been identified as a technology that can be used to efficiently deliver health care services by reducing the cycle times and reagent consumption of common biological protocols and medical diagnostic procedures while reducing overhead costs by performing these applications at the point of care. Electrowetting on dielectric is one promising discrete flow microfluidic platform that can individually create, manipulate, and mix droplets through the application of asymmetric electric fields. The work presented outlines fundamental and practical contributions to the understanding and advancement of electrowetting on dielectric devices that the authors are using to develop a device capable of performing immunoassays on chip. Explicit analytical models for capillary force and the reduction in that force by contact angle hysteresis as a function of the three-dimensional shape of the droplet were derived to develop an empirically validated analytical model for transient motion of droplets in electrowetting on dielectric devices. This model accurately predicts the maximum droplet displacement and travel time to within 2.3% and 2.7%, respectively; whereas the average droplet velocity was always predicted to within 8.1%. It also demonstrates a method for real time monitoring of droplet composition, particle concentration, and chemical reactions in electrowetting on dielectric devices without optical access. This method has been used to determine the concentration of water-methanol solutions, measure the concentration of glass microspheres at various concentrations, and detect the chemical reactions that are typically used in immunoassays. A method for the mechanical filtration of droplets in these devices will also be presented. The proposed filtration method was successful at pore sizes at least two orders of magnitude below the droplet height, which is small enough to separate red and white blood cells in continuous flow microfluidic devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".