A novel microfluidic viscometer for measuring viscosity of ultrasmall volumes of Newtonian and non-Newtonian liquids
Bibliographic record
Abstract
Abstract Viscosity is a critical fluid property that significantly influences fluid behavior and performance across various systems. Most commercial viscometers require relatively large sample volumes (on the order of milliliters), which restricts their utility in scenarios where only limited sample volumes are available. For instance, human tear fluid—essential for developing effective treatment strategies—is scarce (typically on microliters), especially in individuals with dry eye disease. To address this limitation, we present a novel microfluidic viscometer platform capable of measuring the viscosity of ultra-small volumes (i.e. ∼10 μ l) of Newtonian and non-Newtonian fluids. The working principle is based on the Hagen–Poiseuille equation, incorporating the Weissenberg–Rabinowitsch–Mooney correction for slit-flow, and employs an optically transparent microfluidic chip integrated with supporting devices including a syringe pump, manifold, camera, and differential pressure transducer. Preliminary validation was conducted using glycerol solutions, artificial tears, and tear samples from dry and healthy eyes. This microfluidic viscometer holds promise for measuring the shear viscosity of small volumes of biofluid samples (e.g. synovial fluid, cerebrospinal fluid, tear films) or pharmaceuticals (e.g. monoclonal antibodies, ophthalmic drug delivery products) by developing surface coating materials appropriate for specific samples.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".