Impact of Joule Heating on Electrokinetic Lateral Flow Assay
Bibliographic record
Abstract
Lateral flow assays (LFAs) are indispensable tools for point-of-care testing. However, their typically high limit of detection (LOD) restricts their applicability in many applications. Recent advances have shown that coupling LFAs with electrophoresis can lower the LOD by orders of magnitude without compromising the assay's simplicity, speed, or cost. Nevertheless, Joule heating resulting from the applied electric current unavoidably raises the temperature of the test strip, which may lead to biomolecule denaturation and a deterioration in sensitivity. We used a two-stage, double-antigen lateral flow assay for the detection of IgG antibodies against hepatitis B surface antigen (HBsAg) in human serum. In the first stage, IgG antibodies reacted with immobilized HBsAg during capillary flow. In the second stage, protein G conjugated with Au nanoparticles was electrophoretically driven through the test strip, resulting in the formation of labeled immune complexes. The second stage was accompanied by Joule heating of the membrane. We demonstrate that membrane overheating (exceeding 80 °C) causes a 42-fold increase in the LOD (decrease of sensitivity), along with the emergence of false-positive results. In this study, we identify the key parameters influencing heating, such as buffer composition and ionic strength, common additives (e.g., surfactants, electroosmotic flow mediators, cations), applied voltage, and test strip geometry. These findings offer practical guidance for the development of electrokinetic assays, enabling operation within a controlled thermal regime and eliminating the need for extensive thermal profiling.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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.
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".