Leveraging liquid dielectrophoresis for microfluidic applications
Bibliographic record
Abstract
Miniaturized fluidic systems have been developed in recent years and offer new and novel means of leveraging the domain of microfluidics for the development of micro-total analysis systems (microTAS). Initially, such systems employed closed microchannels in order to facilitate chip-based biochemical assays, requiring very small quantities of sample and/or reagents and furthermore providing rapid and low-cost analysis on a compact footprint. More recently, advancements in the domain of surface microfluidics have suggested that similar low volume sample handling and manipulation capabilities for bioassays can be attained by leveraging the phenomena of liquid dielectrophoresis and droplet dielectrophoresis (DEP), without the need for separate pumps or valves. Some of the key aspects of this surface microfluidic technology and its capabilities are discussed and highlighted in this paper. We, furthermore, examine the integration and utility of liquid DEP and droplet DEP in providing rapid and automated sample handling and manipulation capabilities on a compact chip-based platform.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".