An effective microscopic model for plasmonic sensing of malaria

Malaria remains a major health threat in low-resource regions and rapid diagnostic tests often lack the sensitivity required for early detection. To address this and help establish more sensitive testing devices, we develop a predictive microscopic model for plasmonic biosensing using metasurfaces. Specifically, we consider the detection of plasmodium lactate dehydrogenase (pLDH), a well-known malaria biomarker. An example metasurface is studied to showcase the effective microscopic model – it consists of a gold nanohole array (150 nm film; 150 nm diameter; 400 nm period) and the biochemistry above it is modelled as stacks of closely packed adlayers. Using Maxwell Garnett effective medium theory we link the refractive index of the pLDH biomarker adsorbed layer on top of the metasurface to the bulk concentration of pLDH in the buffer. This effective microscopic model accounts for the combined optical properties of the biochemistry matrix, bound pLDH, and the buffer medium. By simulating the sensor using the finite element method and an approximate analytical method, we show that the effective model allows one to determine the sensor response, predict binding interactions, and quantify concentration changes on the sensor surface. We then calculate the sensor sensitivity for our example metasurface and its theoretical limit of detection (LOD) based on spectral and intensity interrogation. The lowest LOD calculated based on the model is 0.02 nM of pLDH, equivalent to 0.7 ng/mL, which is a 30 times improvement over current rapid diagnostic tests. While this improvement in performance is highly promising, further work on transferring the ideal theory developed here to field-tested empirical performance will be required. The effective microscopic model we introduce is quite general and the framework developed offers a broadly applicable tool for the design and optimization of other types of highly sensitive plasmonic biosensors.