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Record W2948658372 · doi:10.1021/acs.iecr.9b02177

Printed Thin Films with Controlled Porosity as Lateral Flow Media

2019· article· en· W2948658372 on OpenAlexafffund
Yuanhua Li, Lisa Tran, Carlos D. M. Filipe, John D. Brennan, Robert Pelton

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2019
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsNitrocellulosePorosityMaterials scienceMembraneCalcium carbonatePorous mediumParticle (ecology)Chemical engineeringParticle sizeColloidVolumetric flow rateAdsorptionComposite materialChemistryGeology

Abstract

fetched live from OpenAlex

Traditional materials for lateral flow devices (cellulose, nitrocellulose) are typically produced in large batches with relatively thick membranes and a uniform pore size, making it challenging to develop devices with variable flow rates. With a view to the development of easily manufactured lateral flow media with variable porosity and flow rates, we investigated the ability to produce a fully printed porous media based on colloidal, precipitated calcium carbonate (PCC) dispersions containing latex binders. PCC dispersions with varying particle sizes were printed onto glass surfaces, and it was observed that flow rates could be controlled by varying particle size. On the basis of this finding, a device with three zones, having distinctly different porosities and wicking behaviors, was printed with three calcium carbonate inks in a single printing operation. The printed media were ∼10% the thickness of paper or nitrocellulose membranes, with lower porosities and wicking rates. We demonstrate that such PCC materials can adsorb enzymes such as alkaline phosphatase (ALP) with retention of activity, and show that a colorimetric ALP reaction giving a colored precipitate generated strong color signals on the printed PCC lateral flow media.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.002
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 head0.028
GPT teacher head0.257
Teacher spread0.229 · 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 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

Citations6
Published2019
Admission routes2
Has abstractyes

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