Probabilistic evaluation of surface-enhanced localized surface plasmon resonance biosensing

Area of Science:

• Nanophotonics
• Biosensing
• Surface Plasmon Resonance

Background:

• Localized Surface Plasmon Resonance (LSPR) biosensing offers high sensitivity for molecular detection.
• Understanding detection characteristics is crucial for optimizing biosensor performance.
• Probabilistic models are needed to account for molecular distribution in biosensing.

Purpose of the Study:

• To investigate the detection characteristics of LSPR biosensing using a probabilistic model.
• To analyze the impact of different detection scenarios (non-specific, non-colocalized, colocalized) on LSPR signals.
• To determine the enhancement in the limit of detection through optimized molecular localization.

Main Methods:

• Modeling LSPR biosensing with a probabilistic Poisson distribution for target molecules.
• Utilizing random nanoislands for near-field localization.
• Simulating three detection scenarios: non-specific, non-colocalized, and colocalized detection.
• Analyzing optical signatures and confidence intervals across scenarios.

Main Results:

• Optical signatures increased monotonically with target concentration and size in all scenarios.
• Colocalized detection yielded the largest optical signatures.
• Colocalized detection exhibited the narrowest confidence intervals due to enhanced spatial certainty.
• The limit of detection was improved by over four orders of magnitude via colocalization.

Conclusions:

• Colocalization of target molecules with localized fields significantly enhances LSPR biosensing sensitivity.
• The probabilistic model accurately predicts detection characteristics across different scenarios.
• Optimized molecular localization is key to achieving ultra-low limits of detection in LSPR biosensors.