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Record W3203352471 · doi:10.1021/acsapm.1c00856

Bicomponent Cellulose Fibrils and Minerals Afford Wicking Channels Stencil-Printed on Paper for Rapid and Reliable Fluidic Platforms

2021· article· en· W3203352471 on OpenAlexafffund
Katariina Solin, Maryam Borghei, Monireh Imani, Tero Kämäräinen, Kaisa Kiri, Tapio Mäkelä, Alexey Khakalo, Hannes Orelma, Orlando J. Rojas

Bibliographic record

VenueACS Applied Polymer Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsUniversity of British Columbia
FundersH2020 LEIT NanotechnologiesH2020 European Research CouncilCanada Excellence Research Chairs, Government of CanadaAcademy of Finland
KeywordsFluidicsMicrofluidicsNanotechnologyMaterials scienceStencilWettingScalabilityInkwellCelluloseCalibrationComputer scienceChemical engineeringComposite materialComputational scienceEngineering

Abstract

fetched live from OpenAlex

Flexible and easy-to-use microfluidic systems are suitable options for point-of-care diagnostics. Here, we investigate liquid transport in fluidic channels produced by stencil printing on flexible substrates as a reproducible and scalable option for diagnostics and paper-based sensing. Optimal printability and flow profiles were obtained by combining minerals with cellulose fibrils of two different characteristic dimensions, in the nano- and microscales, forming channels with ideal wettability. Biomolecular ligands were easily added by inkjet printing on the channels, which were tested for the simultaneous detection of glucose and proteins. Accurate determination of clinically relevant concentrations was possible from linear calibration, confirming the potential of the introduced paper-based diagnostics. The results indicate the promise of simple but reliable fluidic channels for drug and chemical analyses, chromatographic separation, and quality control.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.829

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 head0.013
GPT teacher head0.199
Teacher spread0.186 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

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