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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 OpenAlexafffund
Vinodh Rajendra, Clémence Sicard, John D. Brennan, Michael A. Brook

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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.

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations59
Published2014
Admission routes2
Has abstractyes

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