Design of Gold Nanoparticle‐Based Colorimetric Biosensing Assays
Bibliographic record
Abstract
Gold nanoparticle (AuNP)-based colorimetric biosensing assays have recently attracted considerable attention in diagnostic applications due to their simplicity and versatility. This Minireview summarizes recent advances in this field and attempts to provide general guidance on how to design such assays. The key to the AuNP-based colorimetric sensing platform is the control of colloidal AuNP dispersion and aggregation stages by using biological processes (or analytes) of interest. The ability to balance interparticle attractive and repulsive forces, which determine whether AuNPs are stabilized or aggregated and, consequently, the color of the solution, is central in the design of such systems. AuNP aggregation in these assays can be induced by an "interparticle-crosslinking" mechanism in which the enthalpic benefits of interparticle bonding formation overcome interparticle repulsive forces. Alternatively, AuNP aggregation can be guided by the controlled loss of colloidal stability in a "noncrosslinking-aggregation" mechanism. In this case, as a consequence of changes in surface properties, the van der Waals attractive forces overcome interparticle repulsive forces. Using representative examples we illustrate the general strategies that are commonly used to control AuNP aggregation and dispersion in AuNP-based colorimetric assays. Understanding the factors that play important roles in such systems will not only provide guidance in designing AuNP-based colorimetric assays, but also facilitate research that exploits these principles in such areas as nanoassembly, biosciences and colloid and polymer sciences.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".