Tuning the Gold Nanoparticle Colorimetric Assay by Nanoparticle Size, Concentration, and Size Combinations for Oligonucleotide Detection
Bibliographic record
Abstract
Gold nanoparticle (GNP)-based aggregation assay is simple, fast, and employs a colorimetric detection method. Although previous studies have reported using GNP-based colorimetric assay to detect biological and chemical targets, a mechanistic and quantitative understanding of the assay and effects of GNP parameters on the assay performance is lacking. In this work, we investigated this important aspect of the GNP aggregation assay including effects of GNP concentration and size on the assay performance to detect malarial DNA. Our findings lead us to propose three major competing factors that determine the final assay performance including the nanoparticle aggregation rate, plasmonic coupling strength, and background signal. First, increasing nanoparticle size reduces the Brownian motion and thus aggregation rate, but significantly increases plasmonic coupling strength. We found that larger GNP leads to stronger signal and improved limit of detection (LOD), suggesting a dominating effect of plasmonic coupling strength. Second, higher nanoparticle concentration increases the probability of nanoparticle interactions and thus aggregation rate, but also increases the background extinction signal. We observed that higher GNP concentration leads to stronger signal at high target concentrations due to higher aggregation rate. However, the fact the optimal LOD was found at intermediate GNP concentrations suggests a balance of two competing mechanisms between aggregation rate and signal/background ratio. In summary, our work provides new guidelines to design GNP aggregation-based POC devices to meet the signal and sensitivity needs for infectious disease diagnosis and other applications.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".