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Record W2020202842 · doi:10.1039/c4an01626b

Printing silicone-based hydrophobic barriers on paper for microfluidic assays using low-cost ink jet printers

2014· article· en· W2020202842 on OpenAlex
Vinodh Rajendra, Clémence Sicard, John D. Brennan, Michael A. Brook

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Analyst · 2014
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSiliconeMicrofluidicsInkwellJet (fluid)NanotechnologyMaterials scienceChromatographyChemistryComposite materialEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Paper-based microfluidic devices exhibit many advantages for biological assays. Normally, the assays are restricted to certain areas of the paper by hydrophobic barriers comprised of wax or alkyl ketene dimers (AKD). Neither hydrophobic barrier is able to constrain aqueous solutions of surfactants, which are frequently used in biological assays. We demonstrate that rapidly curing silicone resins can be inkjet printed onto pure cellulose paper using inexpensive thermal ink-jet printers. The Piers-Rubinsztajn (PR) reaction dominates the cure chemistry leading to cellulose fibers that are surface coated with a silicone resin. The resulting barriers are able to resist penetration by surfactant solutions and even by the lower surface energy solvents DMF and DMSO. The utility of the barrier was demonstrated using a coliform assay based on detection of β-galactosidase.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.532

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)0.0000.000

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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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