Numerical Investigation of the Combined Effects of Biomolecular Adsorption and Microdroplet Evaporation on the Performance of the Electrocapillary-Based Digital Microfluidic Systems
Bibliographic record
Abstract
In this article, microdroplet motion in the electrocapillary-based digital microfluidic systems is modeled accurately, and the combined effects of the biomolecular adsorption and micro-droplet evaporation on the performance of the device are investigated. An electrohydrodynamic approach is used to model the driving and resisting forces, and Fick’s law and Gibbs equation are used to calculate the microdroplet evaporation and adsorption rate. Effects of the adsorption and evaporation rates are then implemented into the microdroplet dynamics by adding new terms into the force balance equation. It is shown that mass loss due to the evaporation tends to increase the protein concentration, and on the other hand, the increased concentration due to the mass loss increases the biomolecular adsorption rate which has a reverse effect on the concentration. The modeling results indicate that evaporation and adsorption play crucial roles in the microdroplet dynamics.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".