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Record W3018522147 · doi:10.1039/d0lc00248h

Open sessile droplet viscometer with low sample consumption

2020· article· en· W3018522147 on OpenAlexafffund
Matthias Hermann, Kyle J. Bachus, Graham T. T. Gibson, Richard D. Oleschuk

Bibliographic record

VenueLab on a Chip · 2020
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsCMC Microsystems (Canada)Queen's University
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsViscometerSample (material)Materials scienceSessile drop techniqueChemistryComposite materialContact angleChromatographyViscosity

Abstract

fetched live from OpenAlex

This paper reports a portable viscometer that requires less than 10 μL of sample for a measurement. Using a two-droplet Laplace-induced pumping system on an open microfluidic substrate, the device measures the viscosity of a liquid by determining the time required for one droplet to completely pump into a second droplet. The pumping behaviour follows the Hagen-Poiseuille and Laplace relations where the flow rate, Q, is proportional to the liquid's kinematic viscosity, μ. The progress of pumping is measured by tracking the change in curvature of one of the droplets using a laser that is positioned perpendicular to the microfluidic chip and directed at the "tail" of the shrinking droplet. The angle of incidence and degree of refraction changes depending on the size of the droplet, which is tracked by a linear diode array placed beneath the microfluidic chip. Droplet reservoirs and connecting channels were defined by precise patterning of a glass substrate coated with a commercially available omniphobic coating (Ultra Ever Dry®) using laser micromachining. A 500 μm wide and 20 mm long channel with circular reservoirs (d = 1.5 mm) enabled the measurement of dynamic viscosities in the range of η = 1.0-2.87 mPa s. The materials cost for the entire viscometer (fluidics and electronics, etc.) is <15 USD.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.234
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2020
Admission routes2
Has abstractyes

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