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Probabilistic approach for sensing performances of localized surface plasmon resonance biosensors

We analyze sensing performances of localized surface plasmon resonance biosensors based on the overlap between target distribution and local field intensity produced by silver nanoislands in three detection models of non-specific, non-colocalized, and colocalized detection. The behavior of biomolecules was modeled to follow a probabilistic model using Poisson distribution. The results have found that the colocalized detection achieves the highest overlap signature with the smallest uncertainty and can enhance the limit of detection by more than 10000 times compared to conventional non-specific detection.