Hand-powered microfluidics: A membrane pump with a patient-to-chip syringe interface
Bibliographic record
Abstract
In this paper, we present an on-chip hand-powered membrane pump using a robust patient-to-chip syringe interface. This approach enables safe sample collection, sample containment, integrated sharps disposal, high sample volume capacity, and controlled downstream flow with no electrical power requirements. Sample is manually injected into the device via a syringe and needle. The membrane pump inflates upon injection and subsequently deflates, delivering fluid to downstream components in a controlled manner. The device is fabricated from poly(methyl methacrylate) (PMMA) and silicone, using CO2 laser micromachining, with a total material cost of ∼0.20 USD/device. We experimentally demonstrate pump performance for both deionized (DI) water and undiluted, anticoagulated mouse whole blood, and characterize the behavior with reference to a resistor-capacitor electrical circuit analogy. Downstream output of the membrane pump is regulated, and scaled, by connecting multiple pumps in parallel. In contrast to existing on-chip pumping mechanisms that typically have low volume capacity (∼5 μL) and sample volume throughput (∼1-10 μl/min), the membrane pump offers high volume capacity (up to 240 μl) and sample volume throughput (up to 125 μl/min).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".