Self-Referenced Smartphone-Based Nanoplasmonic Imaging Platform for Colorimetric Biochemical Sensing

• Published in: Analytical chemistry (Washington) 2017-01, Vol.89 (1), p.611-615
• Main Authors: Wang, Xinhao, Chang, Te-Wei, Lin, Guohong, Gartia, Manas Ranjan, Liu, Gang Logan
• Format: Article
• Language: English
• Subjects: Absorbance; Analytical chemistry; Bioassays; Biochemistry; Biosensors; Colorimetry; Colorimetry - instrumentation; Detection; Equipment Design; Image processing systems; Limit of Detection; Nanostructure; Nanotechnology - instrumentation; Optics; Plasmonics; Platforms; Refractometry; Smartphone - instrumentation; Smartphones; Surface Plasmon Resonance - instrumentation; Urinalysis
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/acs.analchem.6b02484

Colorimetric sensors usually suffer due to errors from variation in light source intensity, the type of light source, the Bayer filter algorithm, and the sensitivity of the camera to incoming light. Here, we demonstrate a self-referenced portable smartphone-based plasmonic sensing platform integrated with an internal reference sample along with an image processing method to perform colorimetric sensing. Two sensing principles based on unique nanoplasmonics enabled phenomena from a nanostructured plasmonic sensor, named as nanoLCA (nano Lycurgus cup array), were demonstrated here for colorimetric biochemical sensing: liquid refractive index sensing and optical absorbance enhancement sensing. Refractive indices of colorless liquids were measured by simple smartphone imaging and color analysis. Optical absorbance enhancement in the colorimetric biochemical assay was achieved by matching the plasmon resonance wavelength with the chromophore’s absorbance peak wavelength. Such a sensing mechanism improved the limit of detection (LoD) by 100 times in a microplate reader format. Compared with a traditional colorimetric assay such as urine testing strips, a smartphone plasmon enhanced colorimetric sensing system provided 30 times improvement in the LoD. The platform was applied for simulated urine testing to precisely identify the samples with higher protein concentration, which showed potential point-of-care and early detection of kidney disease with the smartphone plasmonic resonance sensing system.