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A Smartphone Biosensor Based on Analysing Structural Colour of Porous Silicon† Cite This: Analyst, 2019, 144, 3942 Tengfei Cao, a Yiliang Zhao, a Crystal A

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A Smartphone Biosensor Based on Analysing Structural Colour of Porous Silicon† Cite This: Analyst, 2019, 144, 3942 Tengfei Cao, a Yiliang Zhao, a Crystal A Analyst View Article Online PAPER View Journal | View Issue A smartphone biosensor based on analysing structural colour of porous silicon† Cite this: Analyst, 2019, 144, 3942 Tengfei Cao, a Yiliang Zhao, a Crystal A. Nattoo, b,c Rabeb Layouni d and Sharon M. Weiss *a,c We report a smartphone compatible, low-cost porous silicon biosensor, which correlates the structural colour of a porous silicon microcavity (PSiM) to spectral peak position. Molecules captured in the PSiM cause a colour change that can be quantified through image analysis. Minimal external accessories are employed. Spectrometer measurements of the PSiM reflectance spectrum shifts are carried out concur- rently with the smartphone measurements to benchmark the accuracy of the smartphone biosensor. We Received 4th January 2019, estimate that the smartphone biosensor supports an equivalent accuracy of 0.33 nm for the detection of Accepted 5th May 2019 colour changes corresponding to spectral shifts of the PSiM. Biosensing functionality is demonstrated DOI: 10.1039/c9an00022d using a biotin–streptavidin assay with an estimated detection limit of 500 nM. The PSiM-smartphone bio- rsc.li/analyst sensor is a promising platform for label-free point of care diagnostics. 1. Introduction designed for a single purpose instead of multi-parameter testing. Moreover, the data processing capabilities of these Point-of-care (POC) testing is a fast-growing sector in the sensors are usually basic and not intended for more compli- diagnostics market due to the convenience and low cost of cated detection and analysis tasks. Over the past few years, POC measurements.1 Biosensors are the crucial elements of smartphones have emerged as a promising host platform for POC diagnostics, which transform biomolecular or medical POC diagnostics systems due to their ubiquitous availability information into easily understandable readouts. Current and advanced computational capabilities.14 Smartphones are mainstream sensing technologies include enzyme-linked often integrated with high-quality sensors that can be repur- immunosorbent assay (ELISA),2,3 mass spectrometry (MS),4 posed to facilitate sensing applications, and internet connec- surface plasmon resonance (SPR),5,6 polymerase chain reaction tivity enables further extension of their capabilities. Significant Published on 06 May 2019. Downloaded by Vanderbilt University Library 12/13/2019 4:09:22 PM. (PCR),7 and electrochemical immunoassays.8,9 These techno- advances in the area of smartphone sensing have been logies have achieved high sensitivity and accuracy, and are reported for smartphone microscopy;15,16 colorimetric test being widely used in hospitals and other laboratories. strip,17,18 lateral flow assay,19,20 and ELISA readers;21 SPR However, tests run using these mainstream sensing techno- sensors;22,23 spectrometers;24,25 and fluorimeters.26 In many logies generally require high-cost instrumentation, strict cases, the reported smartphone devices dramatically reduce sample preparation procedures, and well-trained personnel, the cost factor while maintaining comparable performance to which restrict their usage for POC applications. There have benchtop instruments. For example, based on a smartphone been many efforts to create portable sensing technologies for and external, disposable optical system, Filippini et al. demon- POC diagnostics, including glucose meters,10 paper-based test strated an angle-resolved SPR sensor that measured a refractive − strips,11,12 and lab on chip devices.13 These sensors have the index change of 2 × 10 6 RIU.22 By adding external illumina- advantage of low cost and convenience, but are generally tion and a diffraction grating, Cunningham et al. reported a smartphone spectrometer with extremely high accuracy, and a fluorescence-based spectrophotometer showing comparable aInterdisciplinary Graduate Program in Materials Science, Vanderbilt University, performance to conventional laboratory fluorimeters.24,26 Nashville, TN 37235, USA bDepartment of Electrical and Computer Engineering, University of Miami, Ozcan et al. reported a smartphone microplate reader for Coral Gables, FL 33146, USA ELISA that demonstrated >99% accuracy in screening for cDepartment of Electrical Engineering and Computer Science, Vanderbilt University, several diseases.21 Nashville, TN 37235, USA. E-mail: [email protected] In this paper, we present a new implementation for a cost- dDepartment of Chemical and Biomolecular Engineering, Vanderbilt University, effective, sensitive smartphone biosensor that requires only a Nashville, TN 37235, USA †Electronic supplementary information (ESI) available. See DOI: 10.1039/ bandpass filter and a 3D printed box, and leverages the advan- c9an00022d tages of the high surface area sensor material, porous silicon, 3942 | Analyst,2019,144, 3942–3948 This journal is © The Royal Society of Chemistry 2019 View Article Online Analyst Paper for the test chip. The biosensing system works by detecting the Smartphone measurements structural colour change of a porous silicon microcavity (PSiM) An iPhone SE, model A1662, was used to carry out quantitative that results when target molecules are captured in the pores of measurements of the colour of the PSiMs. Broadband white ff the PSiM. This sensing approach is di erent from traditional light was emitted from the smartphone LED and transmitted colorimetric approaches that require, for example, an enzy- through a Thorlabs bandpass filter (centre wavelength λ0 = matic reaction, fluorescent species, or nanoparticle aggrega- 606.5 nm; full-width-at-half-maximum (FWHM) = 10 nm; see 27,28 tion to cause a colour change. While label-free porous Fig. 2c) before reflecting off the porous silicon sample. The silicon optical biosensors have been previously reported for reflected light was collected by the smartphone camera. We ff high detection sensitivity of a variety of analytes, cost-e ective note that it should be possible to replace the Thorlabs band- fabrication, compatibility with many surface chemistries, and pass filter with a free-standing porous silicon bandpass filter, molecular detection in complex media, their operation relies if desired. To achieve a more uniform illumination, we on lamps or lasers for the light source and benchtop spec- attached a piece of white paper to the phone in front of the 29,30 trometers or power meters for the detector. By using the LED to diffuse the light, and we also added a small piece of built-in LED flash of a smartphone as the light source and the black tape on the back side of the filter fixture to block the smartphone camera as the photon detector, a suitably brightest spot of light emitted from the LED. A custom 3D designed label-free porous silicon sensor can operate without printed box was fabricated and used to hold the smartphone, a benchtop measurement system. Moreover, a porous silicon filter, and PSiM in fixed positions during all measurements smartphone sensor does not require external lenses or (Fig. 1). A camera app developed by KendiTech was used to gratings, as is the case for many other smartphone biosensors. control the focus, flash, zoom, ISO, speed, exposure value and Here, we demonstrate that a label-free PSiM smartphone white balance of the smartphone camera. Videos clips of sensing system can operate with an equivalent accuracy of approximately 1 min were recorded instead of taking individ- 0.33 nm and an estimated detection limit of 500 nM based on ual pictures for better accuracy and reproducibility. The videos – a biotin streptavidin assay. We note that the spectrometer were uploaded to a desktop computer, and a MatLab code was measurements reported in the paper are necessary only for used to convert the videos into arrays of time sequenced inten- characterizing the intrinsic properties of the system, and are sity values. Each video was treated as a series of RGB images, not necessary for quantifying molecular detection events. and the data comprising one RGB image was exported to three Calibration between the light intensity and concentration of matrices in MatLab that contain the intensities measured by target molecules is the only prerequisite for quantitative detec- the red (R), green (G), and blue (B) pixels, respectively, in the tion applications. camera imaging sensor. In this work, we only use the R-values in our calculations because the bandpass filter restricts the incident light to red wavelengths. Detailed procedures for the 2. Materials and methods smartphone measurements are provided in ESI.† We anticipate Materials that the computations carried out on the desktop computer in this work could also be carried out directly on the phone using All chemicals were analytical grade and used without further an appropriately designed app or through a cloud computing Published on 06 May 2019. Downloaded by Vanderbilt University Library 12/13/2019 4:09:22 PM. purification. Single side polished, boron doped p-type silicon approach. wafers (〈100〉, 0.01–0.02 Ω cm, 500–550 μm) were purchased from Pure Wafer, WRS Materials Company. N-Hydroxysuccinicmide (NHS) and 10-Undecenoic acid were purchased from Alfa Aesar. N-(3-Dimethylaminopropyl)-N′- ethylcarbodiimide hydrochloride (EDC), EZ-Link Amine-PEG2- Biotin, sulfo-NHS-biotin, streptavidin and ethanol were all pur- chased from Thermo Fisher Scientific. Hydrofluoric acid (HF) was purchased from Acros Organics. Deionized (DI) water (∼15 MΩ cm) was used as a solvent for all experiments except where noted otherwise. Reflectance measurements A Newport
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