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PAPER PHYSICS. Paper-based device for pre-concentration of target analytes

2012· article· en· W2921192413 on OpenAlexaff
Vincent Leung, Sean C. Johnstone, Yaqin Xu, Robert Pelton, Carlos D. M. Filipe

Bibliographic record

VenueNordic Pulp & Paper Research Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAnalyteAdsorptionChromatographyStreptavidinPorosityMaterials scienceEthylene oxideAcrylic acidAnalytical Chemistry (journal)ChemistryPolymerComposite materialBiotin

Abstract

fetched live from OpenAlex

Abstract In this paper, we demoostrate proof-ofprinciple for a low cost paper-based Chromatographie device capable of pre-concentrating a target analyte by a factor of three thousand fold (assessed using confocal Iaser scanning microscopy (LSCM)). The device consists of a capture zone and a passive pump. The capture zone was created by immobilizing biotinylated microgel particles onto a selected area of filter paper. Due to the presence of biotin on their surface, these particles have the ability to specifically bind streptavidin, which was the target analyte selected for this study. With the incorporation of a superabsorbent passive pump, created using cross-linked poly(acrylic acid), partial sodium saltgra. fi-poly(ethylene oxide), the paper-based device was able to process a volume of dilute solution much larger than that associated with paper porosity. Flow through the device, including the passive pump, could be modeled using Darcy' s law. A simple equation was also developed to relate the concentration in the pre-concentration device to the concentration in the dilute sample. This type of device can be useful for sample pre-concentration before analysis using other methods, such as mass spectrometry.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.644

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.045
GPT teacher head0.340
Teacher spread0.296 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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