Enhancing the Peroxidase-Mimicking Activity of Gold Nanoparticles for Lateral Flow Assays: Quantitative Evaluation in a Kinetic View
Bibliographic record
Abstract
Highly sensitive lateral flow immunoassays (LFIAs) are essential for various point-of-care applications, and gold nanoparticles (Au NPs) are by far the most commonly used labels. However, conventional LFIAs often suffer from high detection limits (LOD) or low sensitivity. In this study, we investigated three strategies to enhance the sensitivity of LFIAs by improving the peroxidase-mimicking (POD) activity of Au NPs. The POD activity of unmodified Au NPs was negligible (<0.01 units/mg, U/mg). The first strategy involved coupling Au NPs with horseradish peroxidase (HRP), which increased the POD activity to 65 U/mg. The second approach involved forming a thin palladium or iridium shell on Au NPs, which elevated the POD activity to 0.69–0.71 U/mg. The third strategy involved binding mercury ions (Hg 2+ ) to Au NPs, resulting in a POD activity of up to 3 U/mg. Finally, we developed a simple quantitative model to estimate the LOD of LFIAs based on the POD kinetic parameters. Using Au-HRP conjugates, we demonstrated that the experimentally measured LOD was consistent with the calculated values. The developed model provides a framework for evaluating LFIAs with catalytic signal amplification and can be used to guide the development of highly sensitive 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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".