Increasing accuracy and specificity in Biosensing.

Lateral flow assays (LFAs) using monodisperse gold nanoparticles (AuNPs) outperform those with polydisperse particles in key metrics such as reproducibility, sensitivity, and manufacturing consistency. Here's a detailed comparison:

 CRITERIA

MONODISPERSE AuNPs

POLYDISPERSE AuNPs

Flow Consistency and Signal Uniformity

Ensure even flow rates across the membrane, critical for consistent test line developement

Exhibit irregular flow patterns due to size variation: larger particles migrate slower than smaller ones, causing uneven signal distribution and higher risk of false negatives/positives

Conjugation Efficiency

Enable precise antibody coupling due to predictable surface chemistry, minimizing wasted reagents 

Risk uneven antibody loading, as smaller particles have less surface area and larger particles may sterically hinder binding sites

Reproducibility

Meeting stringent quality standards (PDI <0.1 by DLS) ensures batch-to-batch consistency critical for clinical diagnostics

Introduce variability in: (a) antibody conjugation density, (b) flow kinetics, (c) optical signal intensity

Stability and Aggregation Resistance

Resist aggregation due to uniform surface charge distribution, maintaining colloidal stability during storage and testing

Prone to size-dependent aggregation, which can cause false signals or block migration in LFAs (can clog membrane pores)

Assay Sensitivity

The uniform size of NPs ensures optimal binding efficiency to target biomolecules. This enhances the signal intensity per particle, improving the assay’s limit of detection

While polydisperse mixtures (e.g., 20 nm + 50 nm) can enhance signal intensity in some cases, their inconsistent size distribution reduces optical uniformity and complicates result interpretation

Consistent Optical Properties

provide with stable and predictable color signal that is critical for visual detection in LFAs

diffuse colors, reduced readability and assay reliability

Manufacturing Consistency

PDI <0.1 ensures reproducibility

Batch variability complicates scaling

 


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