Single-Reservoir Electrophoresis to Facilitate Layer-by-Layer Assembly of Gold Nanoparticles in Lateral Flow Immunoassay
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".