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Record W4407405599 · doi:10.1021/acs.analchem.4c05082

Single-Reservoir Electrophoresis to Facilitate Layer-by-Layer Assembly of Gold Nanoparticles in Lateral Flow Immunoassay

2025· article· en· W4407405599 on OpenAlexafffund
Nikita A. Ivanov, Svetlana M. Krylova, Sergey N. Krylov

Bibliographic record

VenueAnalytical Chemistry · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaYork University
KeywordsChemistryColloidal goldImmunoassayLayer (electronics)NanoparticleNanotechnologyElectrophoresisLayer by layerFlow (mathematics)ChromatographyMechanicsAntibodyOrganic chemistry

Abstract

fetched live from OpenAlex

Electrophoretically facilitated layer-by-layer assembly of gold nanoparticles (GNPs) in lateral flow immunoassays (LFIAs) significantly enhances the signal-to-background ratio and, consequently, the diagnostic sensitivity of these tests. However, conventional two-reservoir electrophoresis on paper is limited by counterflow induced by capillary action, which disrupts the electrophoretic migration of GNPs toward the anode. This counterflow necessitates manual intervention to facilitate the movement of GNP-labeled immunocomplexes from the membrane to the absorption pad, complicating the assay workflow. To address this challenge, we propose a nonconventional single-reservoir electrophoresis system on paper, which inherently eliminates counterflow. In this configuration, the loading side of the paper strip and the cathode reside within the buffer reservoir, while the anode is directly affixed to the opposite end of the paper strip. We demonstrate the efficacy of this single-reservoir system in driving layer-by-layer assembly, while presenting favorable spatial temperature profiles as a side benefit. By eliminating the need for manual steps, this design streamlines the electrophoresis process and enhances the usability of electrophoretically facilitated LFIA assays.

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 score0.026
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.228
Teacher spread0.208 · 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
Published2025
Admission routes2
Has abstractyes

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