Disposable Off-Chip Micro-Dispenser for Accurate Droplet Transportation
Bibliographic record
Abstract
Liquid dispensing in small scale is typically done by using either automatic syringe pumps or manual operation with syringes or pipettes. The micro-dispensers, which can precisely transport a variety of droplet fluids in the μL range, have received increasingly more attention in the area of lab-on-a-chip. In this paper, we present the design of a portable, efficient, accurate yet cost-effective micro-dispenser, which is ideal for improving simple laboratory applications. Our proposed design is comprised of the main frame and a disposable main tank for accommodating a bulk volume of sample fluid. The whole unit functions as an off-chip reservoir to transport the sample fluid to elsewhere through a passive valve. The height of the disposable main tank and the diameter of the passive valve are carefully designed upon the physical properties of the sample fluid. Moreover, our micro-dispenser is equipped with a flexible layer of elastic diaphragm. An electromagnetic actuator is utilized to produce push-pull force, while a Peltier thermoelectric device supported by a fuzzy logic controller is dedicated to controlling the temperature within the tank. The experimental measurements of our prototyped device exhibit that our proposed design features multiple advantages over the conventional methods, including repeatable dispensing, high accuracy, disposability, and controllable temperature.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.002 |
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".