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Record W4311688064 · doi:10.3390/mi13122232

A Nitrocellulose Paper-Based Multi-Well Plate for Point-of-Care ELISA

2022· article· en· W4311688064 on OpenAlexafffund
Zhen Qin, Zongjie Huang, Peng Pan, Yueyue Pan, Runze Zuo, Yu Sun, Xinyu Liu

Bibliographic record

VenueMicromachines · 2022
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanada Foundation for Innovation
KeywordsNitrocelluloseImmunoassayDetection limitChromatographyPoint of careMaterials sciencePoint-of-care testingFabricationLaserNanotechnologyChemistryAntibodyMembraneOpticsImmunologyPhysics

Abstract

fetched live from OpenAlex

Low-cost diagnostic tools for point-of-care immunoassays, such as the paper-based enzyme-linked immunoassay (ELISA), have become increasingly important, especially so in the recent COVID-19 pandemic. ELISA is the gold-standard antibody/antigen sensing method. This paper reports an easy-to-fabricate nitrocellulose (NC) paper plate, coupled with a desktop scanner for ELISA, which provides a higher protein immobilization efficiency than the conventional cellulose paper-based ELISA platforms. The experiments were performed using spiked samples for the direct ELISA of rabbit IgG with a limit of detection (LOD) of 1.016 μg/mL, in a measurement range of 10 ng/mL to 1 mg/mL, and for the sandwich ELISA of sperm protein (SP-10) with an LOD of 88.8 ng/mL, in a measurement range of 1 ng/mL to 100 μg/mL. The described fabrication method, based on laser-cutting, is a highly flexible one-step laser micromachining process, which enables the rapid production of low-cost NC paper-based multi-well plates with different sizes for the ELISA measurements.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.469

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 head0.008
GPT teacher head0.211
Teacher spread0.203 · 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

Citations18
Published2022
Admission routes2
Has abstractyes

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