Nanoparticle Design Rules for Colorimetric Plasmonic Sensors

Abstract

Localized surface plasmon resonance (LSPR) sensors are used in a broad range of detection applications across the chemical, biological, environmental, and medical disciplines. These types of sensors traditionally use the plasmon resonance wavelength of a nanoparticle array to detect changes in refractive index at the sensor surface and, therefore, require expensive spectroscopic instrumentation for readout. However, simple, portable, and low-cost LSPR sensors can be achieved by transitioning to colorimetric measurements, in which refractive index changes are quantified using the R, G, and B pixel intensities from digital nanoparticle images. In this study, we use R, G, and B pixel intensities to quantify color coordinates in the HSV, CIE L*a*b*, and rgb chromaticity color spaces. We show that for sensors comprising 115 nm diameter nanoparticles, hue (H) is the most sensitive color parameter, with a change per refractive index unit (Δhue/ΔRIU) of 0.71 and a figure of merit of 183 RIU–1. Furthermore, we compared hue figures of merit (FOM) for nanoparticles in four different diameters (34.1, 59.8, 81.5, and 115 nm) and showed that hue sensitivity peaks at a diameter of 81.5 nm, with a FOM of 222 RIU–1. In contrast, the spectroscopic sensitivity, quantified in units of Δnm/ΔRIU, increased continually with nanoparticle size. Therefore, the design requirements for colorimetric plasmonic sensors differ from those for spectroscopic plasmonic sensors. This difference in size dependence was explored further using Mie calculations to simulate nanoparticle extinction spectra. Our results revealed that, while λmax responds linearly to refractive index changes, hue responds in a sigmoidal fashion. As a result, the nanoparticle size used in colorimetric sensors relying on hue measurement should be carefully selected to achieve a linear sensor response. We provide general design rules for optimizing hue-based colorimetric sensors and demonstrate that our sensor can be used with a smartphone to detect antibody–antigen interactions.

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