Paper-Based Analytical Methods for Smartphone ... - ACS Publications

Sep 17, 2018 - is anticipated that with cloud computing, digital technologies, and machine ..... Android application for the detection results showing...
0 downloads 0 Views 793KB Size
Subscriber access provided by RENSSELAER POLYTECH INST

Feature

Paper-Based Analytical Methods for Smartphone Sensing with Functional Nanoparticles: Bridges from Smart Surfaces to Global Health Eda Aydindogan, Emine Guler Celik, and Suna Timur Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b03120 • Publication Date (Web): 17 Sep 2018 Downloaded from http://pubs.acs.org on September 17, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

Paper-Based Analytical Methods for Smartphone Sensing with Functional Nanoparticles: Bridges from Smart Surfaces to Global Health Eda Aydindogan†, Emine Guler Celik†, Suna Timur*,†,‡ † Ege University, Faculty of Science, Biochemistry Department, 35100, Bornova, Izmir, Turkey. ‡ Central Research Testing and Analysis Laboratory Research and Application Center, Ege University, 35100, Bornova, Izmir, Turkey.

• Paper-based tests• Point-of-Care Diagnostic(POC) • Nanoparticles • Surface chemistry • Smartphone ABSTRACT: In this feature, the most recent developments as well as ‘pros and cons’ in smartphone sensing, which have been developed using various functional nanoparticles in paper-based sensing systems, will be discussed. Additionally, smart phone sensing and POC combination as a potential tool that opens a gate for knowledge flow ‘from lab scale data to public use’ will be evaluated.

Smartphone technology has gained interest in the last decade, routing research interest to the field of point-of-care (POC) biosensors. Smartphones have strongly changed our lives, in terms of entertainment, banking, shopping and health. It has been reported that in 2016, approximately 62.9% of the population worldwide already own a smartphone and in 2019 the number of users is foreseen to reach 4.68 billion.1 It is anticipated that with cloud computing, digital technologies and machine learning, smartphone based medical applications are going to overthrow conventional health care diagnosis.2 Useful properties of smartphones such as internal memory, high quality cameras and operating systems, wireless connectivity with other devices and its user-friendly interface utilizing easy operation for everyone resulted in more researchers to focus on the smartphones when designing portable POC biosensing devices.2-3 The criteria of an ideal POC device by WHO is affordable, sensitive and specific, user-friendly, rapid and robust, equipment-free and deliverable to those in need – ASSURRED.4 These devices are designed to give qualitative and, more importantly, quantitative results based on interactions with target-specific agents, such as antibodies, aptamers or enzymes, to specific chemical and biological analytes. To observe the interactions between the targets and their specific detection agents, optical, as colorimetric or fluorescence-based sensors, or electrochemical methods are currently being used.59 POC diagnostic advances and applications have also been reviewed in several papers.3, 10-12 As smartphone technology has rapidly improved in the last decade in terms of both hardware and software, smartphones have naturally become the obvious choice for replacing complex instruments as optical or electrochemical interfaces or as the sensor itself.13-14 Paper-based POC diagnostic devices should satisfy the following requirements to be used instead of a sophisticated device: low limit of detection (LOD), high sensitivity and selectivity,

low sample volume, low cost, fast response, and user-friendly format.15 Several features of paper satisfies these requirements: it is inexpensive, abundant and biocompatible, it works by capillary action and does not require external pumping, it can be easily modified, disposed and scaled.16 To date, two main types of paper have been widely used to construct POC devices which are cellulose and nitrocellulose-based materials. Cellulose, a linear chain molecule composed of glucose subunits, is fibrous, porous, hydrophilic, biodegradable, and insoluble in water.17 Cellulose-based materials are the main substrates of dipstick and microfluidic paper-based analytical devices (µPADs).18 Nitrocellulose is obtained by partial nitration of cellulose which strengthens the porous properties of cellulose and shifts from hydrophilic to hydrophobic. Nitrocellulose membranes are the key materials for lateral flow assays (LFAs) and commercial diagnostics due to its ability to bind irreversibly and hydrophobically to proteins by absorption.19-20 Porosity and surface chemistry of these materials is considered as vital for preparation of paper-based diagnostic devices. The most recent works on paper-based sensing platforms have been reviewed by several groups.15, 20-23 Initially, paper-based diagnostic devices were designed only to give qualitative, positive/negative results (e.g., pregnancy tests); however, exceeding their potential, they can now be efficiently used for quantitative analysis as well.24-26 Nanoparticles are defined as materials with at least one dimension in the 1−100 nm range. They can be of various shapes and materials including metals, semiconductors, polymers, and carbon-based materials.27 However, metal nanoparticles are the most widely used nanomaterials in POC due to their localized surface plasmon resonance (LSPR) which refers to the collective oscillation of the conducting electrons of metal nanoparticles when their frequency matches that of the incident electromagnetic radiation.28-29 As a result of this phenom-

ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

enon, strong absorption bands or increased scattering intensity of radiation occurs at certain wavelengths for each metal nanoparticles. LSPR is principally associated with the particle size, shape, composition, interparticle distance, and dielectric constant of the surrounding medium.29 For example, in aggregation based sensing systems, a decrease in distance between particles leads to an overlap between the plasmon fields of the nearby molecules. This causes a red-shift in the LSPR band(s) increasing the intensity and the change in solution color is easily observable.30 Nanoparticles are easy to synthesize, surface modifiable, biocompatible and mostly colored to provide visibility even to the naked eye, practicality and efficiency in analysis.31-32 With a modification on the nanoparticle surface, either with a detection molecule or analyte of interest, they can become sample carriers, targeting agents or detection interfaces on a paper-based sensing platform.33-36 With the advances in paper-based diagnostics, the subject as grown such that almost all targets can be investigated. With the overall improvements in smartphone software, camera design and quality, and wireless information sharing, this new emerging market is of particular interest for researchers hoping to create a new bridge between personalized POC diagnostics and resource-limited settings.37-38 However, without controlled experimental conditions outside of a laboratory, there remains some factors that affect the chemical and biological sensors, such as ambient lighting or temperature.12, 39-40 Furthermore, image analysis is not always easy, particularly when the color change is small. In such cases, red, green, blue (RGB) space may not be possible to use. As an alternative, hue, saturation, value (HSV) or International Commission on Illumination (CIE) color spaces may be used.12 Additionally, to overcome the setback caused by ambient lighting, external housing units, such as a black-box or a white-box, are being developed.41-44 One may argue that these additional units may decrease the portability and simplicity of the device. To overcome this limitation, a simple electronic component was developed, which responds to the integrated light intensity of a wide range of wavelengths, enabling to use various types of light, e.g. monochromatic or polychromatic, as a source.40 Furthermore, suitable mobile applications can be developed to compensate the errors from ambient lighting under different light conditions with a calibration algorithm.7 Another problematic aspect of the smartphone-based POC devices is, the complex nature of the real-life samples and human body that may contain some potentially interfering materials such as dust or other proteins; thus most of the research has been conducted in artificial serum, urine or saliva samples spiked with the analyte of interest.45-46 As a noninvasive biological fluid, saliva was deemed as the future of POC real-life sample analysis because it consists of disease-specific biomarkers in a detectable range.26, 47 Apart from saliva, tears, urine and sweat can also be considered as non-invasive body fluids, however, the composition of these fluids are nonuniform between people and also different for the same patient at different times of the day and external conditions.48 Furthermore, collection of tears and sweat cannot be considered as fully non-invasive as it requires burdensome and inconvenient procedures for the patient. Saliva can be collected with simple earbuds48-49 or cotton swabs50-51 and easily applied on analysis platforms mostly without any sample preparation requirement. At most, a simple centrifugation step is sufficient

Page 2 of 12

enough for sample handling.52 Hence, predicted future studies would prefer saliva as the real-life sample. There are several review articles that discuss smartphonebased sensing platforms,2-3, 11-12, 53-54 paper-based POC devices10, 16, 19-21, 23, 45, 55 and use of nanoparticles in biosensing systems15, 27, 30, 32, 56 separately. However, there exists the necessity for a complementary work to discuss them in an integrated manner. In this review, paper-based biosensor studies embedded with nanoparticles and smartphone imaging technology are discussed. Different optical and electrochemical measurement techniques, their applications, and recent device designs are presented. Furthermore, smartphone technology, as a bridge between smart surfaces and global health, is discussed along with its pros and cons.

SEARCH CRITERIA AND RESULTS Literature searches were performed in Scopus and PubMed databases. The search terms used were “smartphone”, ‘nanoparticles’, ‘paper’, ‘assay’ and ‘sensor’, and review papers were excluded. Afterwards, the resulting publications were screened to eliminate irrelevant data. As a result of the search conducted according to the criteria above, 32 publications were selected for further analysis. The main findings are summarized in Table S1. Information on analyte name and limit of detection, analysis method, and principle are presented. Additionally, nanoparticles used, detection matrix and the use of an external device and their references were included.

OPTICAL SMARTPHONE-BASED SENSORS Smartphone-based optical sensing platforms typically use four different signal detection principles, which are colorimetric, fluorescence-based, chemi-/bio- luminescence-based, and scattering-based sensing (Figure 1).53 The latest research on paper-based sensor designs mostly focuses on colorimetric principles. Additionally, fluorescence-based sensing platforms are also abundant in the literature. Other detection techniques, which are bio- and chemiluminescence and scattering-based assays, are not yet adapted for smartphone and paper-based analytical platforms. There are several studies that focuses on surface-enhanced Raman scattering (SERS)-based approaches for paper-based biosensors which emerges as a powerful technique for the trace level detection of various biological and chemical species.57-60 The Raman scattering is increased when a Raman-active molecule is confined within the range of the electromagnetic fields produced via excitation of the localized surface plasmon resonance (LSPR) of metal surfaces.61 However, this high-quality technique still is not adapted for smartphone-based approaches. Additionally, Citterio’s group developed several sophisticated designs for both colorimetric and fluorescent-based devices on paper.62 However, these high-quality techniques and designs still are not adapted for smartphone-based approaches.

2 ACS Paragon Plus Environment

Page 3 of 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

Figure 1. Typical optical detection for both light transmitted through liquid sample platforms (i.e., cuvette, well plate) and light reflected from solid sample platforms (i.e., test strip, cassette) using (a) colorimetric assays, (b) fluorescence-based assays, (c) bio- and chemiluminescence assays, and (d) scattering-based assays. Reproduced with permission from McCracken, K. E.; Yoon, J.-Y. Anal. Methods 2016, 8, 6591-6601. Published by The Royal Society of Chemistry.



Colorimetric Sensors

Colorimetric biosensors have become increasingly popular in the last decade as POC diagnostics have become increasingly important. Basically, colorimetric assays measure the changes in the absorbance or reflectance intensity of conjugates. The change in the absorbed or reflected light intensity commonly results from an optical property change over a wide range due to surface plasmon resonance or structural shifts.30 This detection strategy allows on-field measurements to be equipmentfree, which provides low-cost and portability.63 Recent technology in smartphones uses low-cost complementary metal oxide semiconductor (CMOS) arrays that can capture the intensity of the light striking to the pixel and convert it into electrical signals.64 These devices contain several transistors at each pixel that amplify and move the charge. A CMOS sensor is not able to detect the color of incident light itself. For this purpose, a color filter is implemented to the sensor so that each pixel senses only one color - red, green or blue. To mimic the sensitivity of human eye, half of the array has green light sensors and quarter of each red and blue light sensors. This filter is known as Bayer Pattern.65 Thus, colorimetric sensors are able to detect color shifts due to structural changes with simple lighting and an appropriate smartphone application with a suitable algorithm. There are a number of software that have been developed, including ImageJ, Fiji, The Image Studio and Image Lab, for practical analysis.66-67 However, controlling the ambient light conditions for repeatability and sensitivity of the analysis is deemed very hard; thus, additional units for smartphones have recently been preferred for interference-free, trustworthy analysis.12 In the last decade, paper-based colorimetric smartphone sensing platforms have been used to detect 8-hydroxy-2′deoxyguanosine (8-OHdG),68 aflatoxin B1 (AFB1),69 alkaline phosphatase (ALP) activity,70 C-reactive protein (CRP),71

CA125,72 Cd(II) and clenbuterol (CL),73 cocaine,26 Streptococcus pneumonia,74-75 Escherichia coli,63, 74-75 ferritin,76 77 78 79-81 glucose, H2O2, Hg(II), immunoglobulin G (IgG),63 82 83 influenza A, melamine, Mycobacterium tuberculosis complex (MTBC) and tuberculosis (TB),84-86 okadaic acid (OA) and saxitoxin (STX),87 plasmodium lactate dehydrogenase (pLDH),88 porcine epidemic diarrhea virus (PEDV),73 prostatespecific antigen (PSA),89 quinolones (QNs), and tetracyclines (TCs).90 The most used real-life samples for these analytes are milk,63, 70, 74-75, 78, 83, 90 water,73-74, 79-81 blood,74, 85 serum,72, 76-77, 89 and urine73, 77 samples. The most important handicap of the use of colorimetric biosensors by nonspecialists is the lack of certainty even with a corresponding color chart due to perception differences of the naked eye.91 For the purpose of contributing to public health at POC, there are two studies that have excelled compared to the numerous publications using quick response (QR) codes on paper-based assays and augmented reality (AR) to conduct colorimetric measurements in real time.63, 88 Russel et al. developed an immunospot-based assay with antibody-labeled gold nanoparticles, which detects the analyte substances immobilized on paper. A schematic representation of the proposed method is given in Figure 2. A transparent QR code was superimposed on the test paper after the recognition procedure was complete. In case of the absence or low concentration of the analyte, the QR code was not disrupting; thus, the AR app gives a negative outcome. Additionally, when high concentrations of the analyte were detected, the QR code was disrupting with dark red spots resulting from the gold conjugates. As a result, E. coli concentration was successfully measured in spiked milk samples with a limit of detection (LOD) of 106 CFU/mL, which is the threshold of the given infective dose.92

Figure 2. Schematic representation of the nanoparticle-based immunoassay on paper substrates and the proposed method for detecting and interpreting the resulting colorimetric signals with the AR app. Reprinted with permission from Russell, S. M.; Doménech-Sánchez, A.; de la Rica, R. ACS sensors 2017, 2, 848853. Copyright 2017 American Chemical Society.

Furthermore, a dual lateral flow (LF) immunoassay platform modified with QR codes was developed by Mthembu et al. to detect malaria, and the results were shared in real-time with the healthcare workers via Google Analytics platform (Figure 3). Positive and negative QR barcode systems were embedded on the LF immunoassay and scanned after the sample fully migrated through the assay. Generated color on the assigned lines disrupts the QR code, which was recognized by the data analysis platform. Obtained data was shared simultaneously with all the countries across the globe, enabling tracking of infectious diseases in terms of time and place. With this bio-

3 ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

sensing device, Plasmodium lactate dehydrogenase (pLDH) was detected with an LOD of 10 ng/mL.

Figure 3. Schematic representation of a step-by-step procedure for the modification of QR codes for installation and application using a positive test as an example. Reprinted from Mthembu, C. L.; Sabela, M. I.; Mlambo, M.; Madikizela, L. M.; Kanchi, S.; Gumede, H.; Mdluli, P. S. Anal. Methods 2017, 9, 5943-5951 with permission of The Royal Society of Chemistry.

• Fluorescence-based Sensors Smartphones are also combined with fluorescence-based assays to detect the light emitted from the target sample upon radiative excitation. Most frequently used fluorescent particles are small molecule fluorophores, quantum dots (QDs) and fluorescent nanoparticles.93-94 Small molecule fluorophores are low molecular weight, below 1 kDa, generally water-soluble and have reactive groups that enables conjugation with capturing agents such as antibodies, peptides, or nucleic acids.93 As they have low molecular weights, when conjugated to a larger biomolecules, e.g. antibodies, the properties of these conjugates tend to like attached biomolecules and not the fluorophore itself.95-96 Quantum dots (QDs) are semiconductor nanoparticles that emit fluorescence upon excitation. QDs lack water solubility and biocompatibility, thus, they usually are encapsulated with functional polymers to gain these important properties without losing their optical features.93 Most nanoparticles are good quenchers of fluorescence, working either by a fluorescence resonance energy transfer (FRET) or electron-transfer type mechanism.97-98 Nanoparticles, compared to molecular probes, usually biocompatible and do not allow nonspecific bindings of other biomacromolecules or undesirable aggregations.98 When binding proteins or other analytes with fluorescent probes, both the optical properties of the probe and function of the analyte may be affected. On the other hand, most nanoparticles have photostability and scarcely affected from nonspecific interactions in terms of their optical and structural properties.98 Finally, it is fair to state that, nanoparticles are simpler to handle, and the results are more predictable compared to fluorophores. Major advantages of using fluorescence-based sensing platforms instead of colorimetric systems are good selectivity, high sensitivity, and rapid response.99 A relatively narrow peak of emission wavelengths allows for measuring lower concentrations. Additionally, as the fluorescence can be tuned by the excitation source intensity, even lower limits of detection values can be achieved.100 However, fluorescence-based POC sensing platforms suffer from interference caused by the back-

Page 4 of 12

ground excitation light, which can mislead the researcher on the accuracy of the results.101 Failure to deploy low-cost and efficient filters to improve signal-to-noise ratio results in low sensitivity as reported previously.102-103 Significant research efforts have been made to develop low-cost, simple and handheld instruments that can be either a smart surface or a device attached or wirelessly connected to smartphones to eliminate such signals and adapt these platforms on smartphone technology to enable and improve fluorescence-based measurements on paper. Upconversion fluorescence is an anti-Stokes luminescence emitted from upconversion phosphors when they are excited with near-infrared (NIR) light.104-105 Being excited in the NIR region, the background fluorescence and scattering light problem was prevented with upconversion phosphors. Upconversion nanoparticles (UCNPs) were introduced to the literature as all-solid compact laser devices, infrared quantum counter detectors, fluorescent labels for detection of biomolecules, and optical data storage.106-107 They have been used as smart surfaces for fluorescence and luminescence-based sensing platforms due to their excellent biomimetic properties.105, 108-110 The first UCNPs and paper-based biosensor was reported by He et al. to detect an important blood biomarker, matrix metalloproteinase-2 (MMP-2).111 Smartphone-based biosensors using UCNPs have also been studied over the last decade.112115

To date, paper-based platforms using fluorescent materials or nanoparticles, mostly UCNPs, were designed in smart platforms to detect brain natriuretic peptide (BNP) and suppression of tumorigenicity 2 (ST2)113 cocaine,112 dipicolinic acid (DPA),99 Escherichia coli O157 and Salmonella spp.,116 and influenza A.114 Thiram was also detected by a luminescencebased paper immunoassay using UCNPs.115 Real-life samples for detection of these analytes were serum,99, 113 human saliva,112 human urine,99 nasal samples,114 and commercial apple juice samples.115 Several studies put effort into developing devices for smartphone imaging technology for POC diagnostics over the last decade.3, 43, 117-118 Shah et al. proposed a universal serial bus (USB)-powered excitation module that couples ultraviolet (UV) light-emitting diodes (LED) with long Stokes-shift quantum dots, enabling ratiometric smartphone fluorescence measurements without optical filters or any other attachments. 114 The device was tested with LF assay to detect influenza A nucleoprotein, and LOD was obtained as ~2.0 fmol, which is >2 orders of magnitude better than the smartphone-based assays developed with gold nanoparticles. Another simple, hand-held multiplexed household prognosis platform was developed by You et al. combining UCNP-based LF immunoassays with a smartphone-based reader for early diagnosis and real-time prognosis of HF at bedside.113 With the advantage of multiplexed detection, which is the ability to convert the clinical sample to a result on a smartphone, biomarkers of heart failure, brain natriuretic peptide (BNP), and suppression of tumorigenicity 2 (ST2) were detected with high sensitivity and specificity. LODs were reported as 5.0 pg/mL and 1.0 ng/mL for BNP and ST2, respectively, which are only one order higher than the clinical cutoff values. These two designs are given in Figure 4.

4 ACS Paragon Plus Environment

Page 5 of 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

Figure 4. (A) Design of USB-powered UV LED module. (a) Exploded view shows the USB type A LED driver and repositionable UV LED board. (b) Schematic and circuit diagram of the LED driver. (c) Photo of the LED module. Reprinted with permission from Shah, K. G.; Singh, V.; Kauffman, P. C.; Abe, K.; Yager, P. Anal. Chem. 2018, 90 (11), 6967-6974. Copyright 2018 American Chemical Society. (B) Schematic illustration of household fluorescent LFS platform. The multiplexed UC-LFS platform integrating a (a) smartphone-based portable reader and (b) dualcolor UCNP-based LFS. (c) When two analytes of BNP and ST2 are present in a sample, the dual-color UCNPs would be captured in two test lines, respectively. (d) Otherwise, no UCNPs would be trapped in test lines. Dual-color UCNPs would be captured in control lines in two cases. (e) Details of smartphone-based reader are displayed in the enlarged view. Reprinted with permission from You, M.; Lin, M.; Gong, Y.; Wang, S.; Li, A.; Ji, L.; Zhao, H.; Ling, K.; Wen, T.; Huang, Y.; Gao, D.; Ma, Q.; Wang, T.; Ma, A.; Li, X.; Xu, F. ACS Nano, 2017, 11 (6), 6261–6270. Copyright 2017 American Chemical Society.

ELECTROCHEMICAL SENSORS Electrochemical biosensor refers to a molecular sensing instrument which combines a biological detection element with an electrode transducer which transforms the biological recognition into an electrical signal.119 A reference electrode, commonly made from Ag/AgCl, a counter or auxiliary electrode and a working electrode, also known as the sensing or redox electrode are usually required for electrochemical sensing.120 The most common measurement techniques are amperometric and potentiometric measurements. In amperometry, constant potential is applied to the system and current change is ob-

served which is correlated with the reduction or oxidation of the electroactive species in a biochemical reaction.121 The accumulation of a charge potential at the working electrode compared to the reference electrode in an electrochemical cell is measured in potentiometric devices.120 As amperometric measurements provide high sensitivity and wide linear range, this technique has been preferred and widely used.122-124 Additionally, amperometric measurements provide different methods such as chronoamperometry, cyclic voltammetry (CV) and differential pulse voltammetry (DPV) for different analyte detections and the use of different methods can significantly improve the performance of sensing platforms. Electrochemical biosensor systems has been extensively investigated since the 1970s to detect numerous analytes such as proteins,125-127 glucose,128-130 or DNA131-133 using aptamers,134-136 137-138 antibodies, or functional polymer surfaces138-139 as capture molecules. This comprehensive area of research has been reviewed by several groups, emphasizing the importance of the subject.119-120, 140 Electrochemical biosensors have been adapted as POC diagnostic platforms in the last decade due to their high sensitivity, portability, cost-efficiency, and simplicity.141-142 Various portable external device units for smartphone-based electrochemical sensing has been developed in the last decade. Low cost modules which are connected to audio jack of a smartphone was developed with screen printed electrodes (SPEs) to measure different analytes such as antibodies, proteins or nucleic acids.9, 143-144 Additionally, a smartphone-based µPotentiostat combining novel circuital techniques for readout digitalization with a lab-on-a-chip concept enabling chronoamperometric and cyclic voltammetry measurements was developed by Aymerich et al.145 This device was USB powered and a test measurement was conducted to measure ethanol concentration in blood and this design featured sensitive results for illegal thresholds of ethanol. Another USB powered system was developed to perform CV and DPV measurements for simultaneous detection of electroactive biomolecules.146 A test measurement was performed to detect ascorbic acid, dopamine, and uric acid at different concentrations in artificial urine using SPEs, coin-sized detector, and smartphone for performance control, calculation, and results display in real time. Several wireless designs was also developed, enabling data transfer via Bluetooth, by using a smartphone, hand-held detector, and modified electrodes.147-148 CV and EIS measurements were performed with the hand-held detector to monitor electrode modification and detection, as well as data transmission to the smartphone for glucose and volatile organic compounds, respectively. In these designs, the smartphone was used to control the system, process data, and display results in real time through an application. Finally, a custom smartphone case was built with a permanent bare 3electrode system, a Bluetooth-based data transfer system and a potentiostat.149 A magnetic enzyme pellet is disposed onto magnetic sensor surface and sensing is conducted by a custom-built application. After dropping a blood sample onto the enzyme pellet, within a few seconds, glucose concentration of the blood is detected with the integrated potentiostat in the case and the results can be visualized on the smartphone screen. These devices contain in common a built-in low-cost potentiostat and the user is able to perform the analysis in an on-phone app. There exists different ways of constructing

5 ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

electrochemical sensing platforms such as a single electrode on a 3-electrode system, microelectrode array (MEA), and µPADs.150 Among those, µPADs emerge as the new generation POC design as they combine low cost with high specificity and selectivity by enabling a multiplexed analysis on paper.55, 151 Even though µPADs have numerous advantages, they have not been extensively combined with smartphone technology because of the requirement of external hardware to control electrochemical measurement variables, such as applied potential or measured current, which eliminates the cost-effectiveness of the smartphone-based POC analysis. SPEs, either on microfluidic systems or on paper, were used to some extent in combination with smartphones to enable POC electrochemical analysis.143, 152-153 Thus, future research is focused on developing novel low-cost, hand-held, and simple devices to be implemented on electrochemical sensing platforms on smartphones.9, 154 Only one study on paper-based electrochemical biosensors combined with smartphones fell into our search criteria. Fan et al. developed a wireless POC device (Figure 5) for on-site analysis of neuron-specific enolase (NSE) as a model analyte with µPADs.150 µPADs were modified by amino-functional graphene, thionine, and gold nanoparticles (NH2G/Thi/AuNPs) to compose a functional smart surface to enhance the binding of NSE antibody on the µPAD surface. The results were comparable with those measured by Autolab with an LOD of 10 pg/mL.

Figure 5. Prototype of the wireless POCT system: (a) µPADs, (b) electrochemical detector with µPADs, (c) smartphone running an Android application for the detection results showing NSE. Reprinted from Fan, Y.; Liu, J.; Wang, Y.; Luo, J.; Xu, H.; Xu, S.; Cai, X. Biosens. Bioelectron. 2017, 95, 60-66 with permission of Elsevier B.V.

DISCUSSION AND FUTURE PERSPECTIVES Since the beginning of the 21st century, smartphones have become a vital part of our daily lives. Technological advances, in terms of both hardware and software, have led researchers to think, create, and develop conventional analytical devices on a small scale that is adaptable for smartphones. The main concern in developing such devices was to reach even to the rural areas of the world, making healthcare and rapid diagnostic tools available for everyone: creating a bridge between controlled laboratory experiments and public health.

Page 6 of 12

Even though great progress has been achieved, there remains some challenges to be solved. For optical smartphone-based analytical devices, much effort should be put toward the development of miniaturized external devices to eliminate the outside lighting effect on analysis. These optical attachments should be lightweight, low-cost, and simple for everyone (e.g., elderly people taking tests at home). On the other hand, novel image processing techniques should be developed to optimize optical attachments. Even though these simplifications may weaken the performance of the sensing system, using advanced computational capacities of smartphones, different image processing algorithms can be developed.3 More importantly, colorimetric immunoassay designs should be improved as they do not necessarily require additional attachments and can use only ambient light for analysis. Compared to fluorescence- or luminescence-based sensing systems, colorimetric detection can be performed by analyzing the change in the color intensity with only a smartphone image of a paperbased assay, which will enable rapid and POC diagnosis. Similarly, electrochemical analysis devices were also attempted to be miniaturized for smartphone analysis. Conventionally, electrochemical measurements were conducted by a potentiostat, and a smartphone is used as an interface to control and display the measurements. Data were transferred between a smartphone and the electrochemical measurement device by wired or wireless methods. The most significant problem here is the necessity for a complex and nonportable external device for measurements. One of the most recent technologies uses a built-in near field communication (NFC) platform, which provides communication between electronic tags and a smartphone.3 This technology enabled bio-chemical detections to be low-cost and portable at the POC, and with a built-in NFC reader on a smartphone, electrochemical measurements can be conducted using microfabricated tags without any external device. Finally, it is important to state that the smartphone-based sensor literature is rather young, and the designs are mostly tested in laboratory settings. However, both optical and electrochemical biosensors for smartphone adapted methods should evolve for POC diagnostics and be developed outside of controlled environments using real-life samples with minimal user involvement. It is reasonable to conclude that future work should be focused on sensor fabrication, data transmission, and development of algorithms for smartphones to upgrade the performance with maintaining portability and cost effectiveness. To conclude, a general SWOT analysis on smartphone-based POC biosensors is presented in Table 1. Table 1. SWOT Analysis on smartphone-based POC biosensors. Strengths (S)

Weaknesses (W)

Opportunities (O)

Threats (T)

Low-cost

Requirement of external units

Point-of-care diagnosis of disease

In-laboratory, controlled environment testing

Simple and user-

Lack of reallife sample

Real-time prognosis

Continuously changing technology of

of

6 ACS Paragon Plus Environment

Page 7 of 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

friendly

studies

disease

mobile phones

Sensitive and rapid

Weakness of communication technologies

Worldwide information sharing via Google Analytics

Standardization of devices for mass production

Ambient light interference

Potential unbiased applications using QR code technology

Patient’s psychology

Applications on-site

Widely applicable

Support Program (BIDEB) is acknowledged for their financial support for E. Aydindogan.

AUTHOR INFORMATION Corresponding Author * E-mail: [email protected]. Phone and fax: +90 232 3438624.

ORCiDs Eda Aydindogan 0000-0003-4882-6445 Emine Guler Celik 0000-0003-2381-9775 Suna Timur 0000-0002-1981-7577

Possible electrochemical applications of NFC platform to eliminate external device requirement

Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

The future of diagnostics will potentially have smartphonebased, portable, rapid, and low-cost readers to continuously monitor human health with biological fluids, such as saliva, sweat, or blood. It is intriguing to picture the real awareness of smartphone-based biosensors in public use. For instance, there exist numerous studies on POC cancer detection with smartphone-based technologies.54, 72, 118, 155-156 Although it is acknowledged that current device designs are not yet commercially available such as pregnancy test kits, the aim of POC diagnostic research is to have such a market in on-site, realtime healthcare. However, patient psychology must be considered when launching such devices for public use. Patients’ realization of having cancer or any other serious disease might result in unpredictable scenarios if a poorly prepared design is launched to the market. The final device for any analyte detection must be correct, accurate, and precise before being rapid, low-cost or simple. Therefore, the gravitas of research on critically important smartphone POC diagnostics must be taken into account.

Notes The authors declare no competing financial interest.

Biographies Eda Aydindogan is an Ms.D student at the Biochemistry Department at the Faculty of Science of Ege University in Turkey. She has been working on biofunctional materials and biosensors during her master’s degree and expects to present her thesis in January 2019. Emine Guler Celik has received her Ph.D. degree of Biochemistry in Ege University in 2017. Her current research focuses on lateral flow tests and biosensors, nanomaterials and drug delivery. Suna Timur has received her Ph.D. degree of Biochemistry in Ege University in 2001. She is currently a full-time Professor at Department of Biochemistry, Ege University. Her current research focuses on electrochemical sensors and biosensors, nanobiomaterials, nanomedicine, and drug delivery.

REFERENCES ASSOCIATED CONTENT Supporting Information The table of comprehensive listing containing the main findings of the conducted literature search is given in the Supporting Information.

The Supporting Information is available free of charge on the ACS Publications website.

ACKNOWLEDGEMENTS This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK, project number 117Z609) and COST Action CA16113 CliniMARK: ‘good biomarker practice’ to increase the number of clinically validated biomarkers’ as the main action. Additionally, Council of Scientist

1. Statista, T. S. P., Number of mobile phone users worldwide 2015-2020. 2016. 2. Kanchi, S.; Sabela, M. I.; Mdluli, P. S.; Inamuddin; Bisetty, K., Smartphone based bioanalytical and diagnosis applications: A review. Biosens. Bioelectron. 2018, 102, 136-149. 3. Zhang, D.; Liu, Q., Biosensors and bioelectronics on smartphone for portable biochemical detection. Biosens. Bioelectron. 2016, 75, 273-284. 4. Mabey, D.; Peeling, R. W.; Ustianowski, A.; Perkins, M. D., Diagnostics for the developing world. Nat. Rev. Microbiol. 2004, 2, 231-240. 5. Zhdanov, A.; Keefe, J.; Franco-Waite, L.; Konnaiyan, K. R.; Pyayt, A., Mobile phone based ELISA (MELISA). Biosens. Bioelectron. 2018, 103, 138-142. 6. Preechakasedkit, P.; Siangproh, W.; Khongchareonporn, N.; Ngamrojanavanich, N.; Chailapakul, O., Development of an automated wax-printed paper-based lateral flow device for alphafetoprotein enzyme-linked immunosorbent assay. Biosens. Bioelectron. 2018, 102, 27-32. 7. Shen, L.; Hagen, J. A.; Papautsky, I., Point-of-care colorimetric detection with a smartphone. Lab Chip 2012, 12 (21), 4240-3.

7 ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

8. Zhu, H.; Sencan, I.; Wong, J.; Dimitrov, S.; Tseng, D.; Nagashima, K.; Ozcan, A., Cost-effective and rapid blood analysis on a cell-phone. Lab Chip 2013, 13 (7), 1282-8. 9. Sun, A.; Wambach, T.; Venkatesh, A. G.; Hall, D. A., A Low-Cost Smartphone-Based Electrochemical Biosensor for Point-ofCare Diagnostics. IEEE Biomed. Circuits Syst. Conf. 2014, 2014, 312315. 10. Darwish, N. T.; Sekaran, S. D.; Khor, S. M., Point-of-care tests: A review of advances in the emerging diagnostic tools for dengue virus infection. Sens. Actuators, B 2018, 255, 3316-3331. 11. Xu, X.; Akay, A.; Wei, H.; Wang, S.; Pingguan-Murphy, B.; Erlandsson, B. E.; Li, X.; Lee, W.; Hu, J.; Wang, L.; Xu, F., Advances in Smartphone-Based Point-of-Care Diagnostics. Proc. IEEE 2015, 103 (2), 236-247. 12. Roda, A.; Michelini, E.; Zangheri, M.; Di Fusco, M.; Calabria, D.; Simoni, P., Smartphone-based biosensors: A critical review and perspectives. TrAC, Trends Anal. Chem. 2016, 79, 317325. 13. Delaney, J. L.; Doeven, E. H.; Harsant, A. J.; Hogan, C. F., Use of a mobile phone for potentiostatic control with low cost paperbased microfluidic sensors. Anal. Chim. acta 2013, 790, 56-60. 14. Gallegos, D.; Long, K. D.; Yu, H.; Clark, P. P.; Lin, Y.; George, S.; Nath, P.; Cunningham, B. T., Label-free biodetection using a smartphone. Lab Chip 2013, 13 (11), 2124-32. 15. Quesada-Gonzalez, D.; Merkoci, A., Nanoparticle-based lateral flow biosensors. Biosens. Bioelectron. 2015, 73, 47-63. 16. Gong, M. M.; Sinton, D., Turning the Page: Advancing Paper-Based Microfluidics for Broad Diagnostic Application. Chem. Rev. 2017, 117 (12), 8447-8480. 17. O'Sullivan, A. C., Cellulose: The structure slowly unravels. Cellulose 1997, 4 (3), 173-207. 18. Pelton, R., Bioactive paper provides a low-cost platform for diagnostics. TrAC, Trends Anal. Chem. 2009, 28 (8), 925-942. 19. Koczula, Katarzyna M.; Gallotta, A., Lateral flow assays. Essays Biochem. 2016, 60 (1), 111-120. 20. Yetisen, A. K.; Akram, M. S.; Lowe, C. R., Paper-based microfluidic point-of-care diagnostic devices. Lab Chip 2013, 13 (12), 2210-51. 21. Lopez-Marzo, A. M.; Merkoci, A., Paper-based sensors and assays: a success of the engineering design and the convergence of knowledge areas. Lab Chip 2016, 16 (17), 3150-76. 22. Martinez, A. W.; Phillips, S. T.; Whitesides, G. M.; Carrilho, E., Diagnostics for the developing world: microfluidic paper-based analytical devices. Anal. Chem. 2010, 82 (1), 3-10. 23. Hu, J.; Wang, S.; Wang, L.; Li, F.; Pingguan-Murphy, B.; Lu, T. J.; Xu, F., Advances in paper-based point-of-care diagnostics. Biosens. Bioelectron. 2014, 54, 585-97. 24. Noiphung, J.; Songjaroen, T.; Dungchai, W.; Henry, C. S.; Chailapakul, O.; Laiwattanapaisal, W., Electrochemical detection of glucose from whole blood using paper-based microfluidic devices. Anal. Chim. acta 2013, 788, 39-45. 25. Nantaphol, S.; Chailapakul, O.; Siangproh, W., A novel paper-based device coupled with a silver nanoparticle-modified boron-doped diamond electrode for cholesterol detection. Anal. Chim. acta 2015, 891, 136-143. 26. Guler, E.; Yilmaz Sengel, T.; Gumus, Z. P.; Arslan, M.; Coskunol, H.; Timur, S.; Yagci, Y., Mobile Phone Sensing of Cocaine in a Lateral Flow Assay Combined with a Biomimetic Material. Anal. Chem. 2017, 89 (18), 9629-9632. 27. Zamborini, F. P.; Bao, L.; Dasari, R., Nanoparticles in Measurement Science. Anal. Chem. 2012, 84 (2), 541-576. 28. Mayer, K. M.; Hafner, J. H., Localized Surface Plasmon Resonance Sensors. Chem. Rev. 2011, 111 (6), 3828-3857. 29. Anker, J. N.; Hall, W. P.; Lyandres, O.; Shah, N. C.; Zhao, J.; Van Duyne, R. P., Biosensing with plasmonic nanosensors. Nat. Mater. 2008, 7, 442-453. 30. Vilela, D.; González, M. C.; Escarpa, A., Sensing colorimetric approaches based on gold and silver nanoparticles

Page 8 of 12

aggregation: Chemical creativity behind the assay. A review. Anal. Chim. acta 2012, 751, 24-43. 31. Chen, X.; Mao, S. S., Titanium Dioxide Nanomaterials:  Synthesis, Properties, Modifications, and Applications. Chem. Rev. 2007, 107 (7), 2891-2959. 32. Daniel, M.-C.; Astruc, D., Gold Nanoparticles:  Assembly, Supramolecular Chemistry, Quantum-Size-Related Properties, and Applications toward Biology, Catalysis, and Nanotechnology. Chem. Rev. 2004, 104 (1), 293-346. 33. Ferreira, D. C. M.; Giordano, G. F.; Soares, C. C. d. S. P.; de Oliveira, J. F. A.; Mendes, R. K.; Piazzetta, M. H.; Gobbi, A. L.; Cardoso, M. B., Optical paper-based sensor for ascorbic acid quantification using silver nanoparticles. Talanta 2015, 141, 188-194. 34. Liang, P.; Yu, H.; Guntupalli, B.; Xiao, Y., Paper-Based Device for Rapid Visualization of NADH Based on Dissolution of Gold Nanoparticles. ACS Appl. Mater. Interfaces 2015, 7 (27), 1502315030. 35. Kumar, S.; Bhushan, P.; Bhattacharya, S., Development of a paper-based analytical device for colorimetric detection of uric acid using gold nanoparticles-graphene oxide (AuNPs-GO) conjugates. Anal. Methods 2016, 8 (38), 6965-6973. 36. Choleva, T. G.; Kappi, F. A.; Giokas, D. L.; Vlessidis, A. G., Paper-based assay of antioxidant activity using analyte-mediated on-paper nucleation of gold nanoparticles as colorimetric probes. Anal. Chim. acta 2015, 860, 61-69. 37. Chib, A.; van Velthoven, M. H.; Car, J., mHealth adoption in low-resource environments: a review of the use of mobile healthcare in developing countries. J. Health Commun. 2015, 20 (1), 4-34. 38. Peeling, R. W.; McNerney, R., Emerging technologies in point-of-care molecular diagnostics for resource-limited settings. Expert Rev. Mol. Diagn. 2014, 14 (5), 525-34. 39. Choi, J. R.; Hu, J.; Feng, S.; Wan Abas, W. A.; PingguanMurphy, B.; Xu, F., Sensitive biomolecule detection in lateral flow assay with a portable temperature-humidity control device. Biosens. Bioelectron. 2016, 79, 98-107. 40. Park, Y. M.; Han, Y. D.; Chun, H. J.; Yoon, H. C., Ambient light-based optical biosensing platform with smartphoneembedded illumination sensor. Biosens. Bioelectron. 2017, 93, 205211. 41. Liu, Z.; Zhang, Y.; Xu, S.; Zhang, H.; Tan, Y.; Ma, C.; Song, R.; Jiang, L.; Yi, C., A 3D printed smartphone optosensing platform for point-of-need food safety inspection. Anal. Chim. acta 2017, 966, 81-89. 42. Ming, K.; Kim, J.; Biondi, M. J.; Syed, A.; Chen, K.; Lam, A.; Ostrowski, M.; Rebbapragada, A.; Feld, J. J.; Chan, W. C. W., Integrated Quantum Dot Barcode Smartphone Optical Device for Wireless Multiplexed Diagnosis of Infected Patients. ACS Nano 2015, 9 (3), 3060-3074. 43. Roda, A.; Guardigli, M.; Calabria, D.; Calabretta, M. M.; Cevenini, L.; Michelini, E., A 3D-printed device for a smartphonebased chemiluminescence biosensor for lactate in oral fluid and sweat. Analyst 2014, 139 (24), 6494-6501. 44. Kim, S. C.; Jalal, U. M.; Im, S. B.; Ko, S.; Shim, J. S., A smartphone-based optical platform for colorimetric analysis of microfluidic device. Sens. Actuators, B 2017, 239, 52-59. 45. Sher, M.; Zhuang, R.; Demirci, U.; Asghar, W., Paperbased analytical devices for clinical diagnosis: recent advances in the fabrication techniques and sensing mechanisms. Expert Rev. Mol. Diagn. 2017, 17 (4), 351-366. 46. Geng, Z.; Zhang, X.; Fan, Z.; Lv, X.; Su, Y.; Chen, H., Recent Progress in Optical Biosensors Based on Smartphone Platforms. Sensors (Basel) 2017, 17 (11), 2449-2468. 47. Drummer, O. H., Drug testing in oral fluid. Clin. Biochem. Rev. 2006, 27 (3), 147-59. 48. Soni, A.; Jha, S. K., A paper strip based non-invasive glucose biosensor for salivary analysis. Biosens. Bioelectron. 2015, 67, 763-768.

8 ACS Paragon Plus Environment

Page 9 of 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

49. Soni, A.; Surana, R. K.; Jha, S. K., Smartphone based optical biosensor for the detection of urea in saliva. Sens. Actuators, B 2018, 269, 346-353. 50. Calabria, D.; Caliceti, C.; Zangheri, M.; Mirasoli, M.; Simoni, P.; Roda, A., Smartphone–based enzymatic biosensor for oral fluid L-lactate detection in one minute using confined multilayer paper reflectometry. Biosens. Bioelectron. 2017, 94, 124-130. 51. Lee, J. R.; Choi, J.; Shultz, T. O.; Wang, S. X., Small Molecule Detection in Saliva Facilitates Portable Tests of Marijuana Abuse. Anal. Chem. 2016, 88 (15), 7457-61. 52. Rauf, S.; Zhang, L.; Ali, A.; Liu, Y.; Li, J., Label-Free Nanopore Biosensor for Rapid and Highly Sensitive Cocaine Detection in Complex Biological Fluids. ACS Sensors 2017, 2 (2), 227-234. 53. McCracken, K. E.; Yoon, J.-Y., Recent approaches for optical smartphone sensing in resource-limited settings: a brief review. Anal. Methods 2016, 8 (36), 6591-6601. 54. Bender, L. J.; Yue, K. R. Y.; To, J. M.; Deacken, L.; Jadad, R. A., A Lot of Action, But Not in the Right Direction: Systematic Review and Content Analysis of Smartphone Applications for the Prevention, Detection, and Management of Cancer. J. Med. Internet Res. 2013, 15 (12), e287. 55. Dungchai, W.; Chailapakul, O.; Henry, C. S., Electrochemical Detection for Paper-Based Microfluidics. Anal. Chem. 2009, 81 (14), 5821-5826. 56. Holzinger, M.; Le Goff, A.; Cosnier, S., Nanomaterials for biosensing applications: a review. Front. Chem. 2014, 2, 63. 57. Lee, C. H.; Tian, L.; Singamaneni, S., Paper-Based SERS Swab for Rapid Trace Detection on Real-World Surfaces. ACS Appl. Mater. Interfaces 2010, 2 (12), 3429-3435. 58. Wang, Z.; Zong, S.; Wu, L.; Zhu, D.; Cui, Y., SERSActivated Platforms for Immunoassay: Probes, Encoding Methods, and Applications. Chem. Rev. 2017, 117 (12), 7910-7963. 59. Saha, A.; Jana, N. R., Paper-Based Microfluidic Approach for Surface-Enhanced Raman Spectroscopy and Highly Reproducible Detection of Proteins beyond Picomolar Concentration. ACS Appl. Mater. Interfaces 2015, 7 (1), 996-1003. 60. He, S.; Chua, J.; Tan, E. K. M.; Kah, J. C. Y., Optimizing the SERS enhancement of a facile gold nanostar immobilized paperbased SERS substrate. RSC Adv. 2017, 7 (27), 16264-16272. 61. Shafer-Peltier, K. E.; Haynes, C. L.; Glucksberg, M. R.; Van Duyne, R. P., Toward a Glucose Biosensor Based on SurfaceEnhanced Raman Scattering. J. Am. Chem. Soc. 2003, 125 (2), 588593. 62. Yamada, K.; Citterio, D.; Henry, C. S., "Dip-and-read" paper-based analytical devices using distance-based detection with color screening. Lab Chip 2018, 18 (10), 1485-1493. 63. Russell, S. M.; Doménech-Sánchez, A.; de la Rica, R., Augmented Reality for Real-Time Detection and Interpretation of Colorimetric Signals Generated by Paper-Based Biosensors. ACS Sensors 2017, 2 (6), 848-853. 64. Bigas, M.; Cabruja, E.; Forest, J.; Salvi, J., Review of CMOS image sensors. Microelectron. J. 2006, 37 (5), 433-451. 65. Menon, D.; Calvagno, G., Color image demosaicking: An overview. Signal Process Image 2011, 26 (8), 518-533. 66. Schindelin, J.; Arganda-Carreras, I.; Frise, E.; Kaynig, V.; Longair, M.; Pietzsch, T.; Preibisch, S.; Rueden, C.; Saalfeld, S.; Schmid, B.; Tinevez, J.-Y.; White, D. J.; Hartenstein, V.; Eliceiri, K.; Tomancak, P.; Cardona, A., Fiji: an open-source platform for biological-image analysis. Nat. Methods 2012, 9, 676-82. 67. Schneider, C. A.; Rasband, W. S.; Eliceiri, K. W., NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 2012, 9 (7), 671-675. 68. Zhu, X.; Sarwar, M.; Yue, Q.; Chen, C.; Li, C. Z., Biosensing of DNA oxidative damage: a model of using glucose meter for non-glucose biomarker detection. Int. J. Nanomedicine 2017, 12, 979-987.

69. Li, X.; Yang, F.; Wong, J. X. H.; Yu, H. Z., Integrated Smartphone-App-Chip System for On-Site Parts-Per-Billion-Level Colorimetric Quantitation of Aflatoxins. Anal. Chem. 2017, 89 (17), 8908-8916. 70. Yu, L.; Shi, Z.; Fang, C.; Zhang, Y.; Liu, Y.; Li, C., Disposable lateral flow-through strip for smartphone-camera to quantitatively detect alkaline phosphatase activity in milk. Biosens. Bioelectron. 2015, 69, 307-15. 71. Russell, S. M.; de la Rica, R., Paper transducers to detect plasmon variations in colorimetric nanoparticle biosensors. Sens. Actuators, B 2018, 270, 327-332. 72. Hosu, O.; Ravalli, A.; Lo Piccolo, G. M.; Cristea, C.; Sandulescu, R.; Marrazza, G., Smartphone-based immunosensor for CA125 detection. Talanta 2017, 166, 234-240. 73. Xiao, W.; Huang, C.; Xu, F.; Yan, J.; Bian, H.; Fu, Q.; Xie, K.; Wang, L.; Tang, Y., A simple and compact smartphone-based device for the quantitative readout of colloidal gold lateral flow immunoassay strips. Sens. Actuators, B 2018, 266, 63-70. 74. Choi, J. R.; Hu, J.; Tang, R.; Gong, Y.; Feng, S.; Ren, H.; Wen, T.; Li, X.; Wan Abas, W. A. B.; Pingguan-Murphy, B.; Xu, F., An integrated paper-based sample-to-answer biosensor for nucleic acid testing at the point of care. Lab Chip 2016, 16 (3), 611-621. 75. Cheng, N.; Song, Y.; Zeinhom, M. M. A.; Chang, Y. C.; Sheng, L.; Li, H.; Du, D.; Li, L.; Zhu, M. J.; Luo, Y.; Xu, W.; Lin, Y., Nanozyme-Mediated Dual Immunoassay Integrated with Smartphone for Use in Simultaneous Detection of Pathogens. ACS Appl. Mater. Interfaces 2017, 9 (46), 40671-40680. 76. Srinivasan, B.; O'Dell, D.; Finkelstein, J. L.; Lee, S.; Erickson, D.; Mehta, S., ironPhone: Mobile device-coupled point-ofcare diagnostics for assessment of iron status by quantification of serum ferritin. Biosens. Bioelectron. 2018, 99, 115-121. 77. Ortiz-Gómez, I.; Salinas-Castillo, A.; García, A. G.; Álvarez-Bermejo, J. A.; de Orbe-Payá, I.; Rodríguez-Diéguez, A.; Capitán-Vallvey, L. F., Microfluidic paper-based device for colorimetric determination of glucose based on a metal-organic framework acting as peroxidase mimetic. Microchim. Acta 2017, 185 (1), 47. 78. Zhang, W.; Niu, X.; Li, X.; He, Y.; Song, H.; Peng, Y.; Pan, J.; Qiu, F.; Zhao, H.; Lan, M., A smartphone-integrated ready-touse paper-based sensor with mesoporous carbon-dispersed Pd nanoparticles as a highly active peroxidase mimic for H2O2 detection. Sens. Actuators, B 2018, 265, 412-420. 79. Jarujamrus, P.; Meelapsom, R.; Pencharee, S.; Obma, A.; Amatatongchai, M.; Ditcharoen, N.; Chairam, S.; Tamuang, S., Use of a Smartphone as a Colorimetric Analyzer in Paper-based Devices for Sensitive and Selective Determination of Mercury in Water Samples. Anal. Sci. 2018, 34 (1), 75-81. 80. Chen, G. H.; Chen, W. Y.; Yen, Y. C.; Wang, C. W.; Chang, H. T.; Chen, C. F., Detection of mercury(II) ions using colorimetric gold nanoparticles on paper-based analytical devices. Anal. Chem. 2014, 86 (14), 6843-9. 81. Han, K. N.; Choi, J. S.; Kwon, J., Gold nanozyme-based paper chip for colorimetric detection of mercury ions. Sci. Rep. 2017, 7 (1), 2806. 82. Pilavaki, E.; Parolo, C.; McKendry, R.; Demosthenous, A. Wireless paper-based biosensor reader for the detection of infectious diseases at the point of care. 2016 IEEE SENSORS 2016, 13(12), 1-3. 83. Gao, N.; Huang, P.; Wu, F., Colorimetric detection of melamine in milk based on Triton X-100 modified gold nanoparticles and its paper-based application. Spectrochim. Acta, Part A 2018, 192, 174-180. 84. Tsai, T. T.; Huang, C. Y.; Chen, C. A.; Shen, S. W.; Wang, M. C.; Cheng, C. M.; Chen, C. F., Diagnosis of Tuberculosis Using Colorimetric Gold Nanoparticles on a Paper-Based Analytical Device. ACS Sens. 2017, 2 (9), 1345-1354. 85. Tsai, T. T.; Shen, S. W.; Cheng, C. M.; Chen, C. F., Paperbased tuberculosis diagnostic devices with colorimetric gold nanoparticles. Sci. Technol. Adv. Mater. 2013, 14 (4), 044404.

9 ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

86. Veigas, B.; Jacob, J. M.; Costa, M. N.; Santos, D. S.; Viveiros, M.; Inacio, J.; Martins, R.; Barquinha, P.; Fortunato, E.; Baptista, P. V., Gold on paper-paper platform for Au-nanoprobe TB detection. Lab Chip 2012, 12 (22), 4802-8. 87. Fang, J.; Qiu, X.; Wan, Z.; Zou, Q.; Su, K.; Hu, N.; Wang, P., A sensing smartphone and its portable accessory for on-site rapid biochemical detection of marine toxins. Anal. Methods 2016, 8 (38), 6895-6902. 88. Mthembu, C. L.; Sabela, M. I.; Mlambo, M.; Madikizela, L. M.; Kanchi, S.; Gumede, H.; Mdluli, P. S., Google Analytics and quick response for advancement of gold nanoparticle-based dual lateral flow immunoassay for malaria - Plasmodium lactate dehydrogenase (pLDH). Anal. Methods 2017, 9 (41), 5943-5951. 89. Huang, J.-Y.; Lin, H.-T.; Chen, T.-H.; Chen, C.-A.; Chang, H.-T.; Chen, C.-F., Signal Amplified Gold Nanoparticles for Cancer Diagnosis on Paper-Based Analytical Devices. ACS Sens. 2018, 3 (1), 174-182. 90. Li, Z.; Li, Z.; Zhao, D.; Wen, F.; Jiang, J.; Xu, D., Smartphone-based visualized microarray detection for multiplexed harmful substances in milk. Biosens. Bioelectron. 2017, 87, 874-880. 91. Kumar Ashok, A.; Hennek Jonathan, W.; Smith Barbara, S.; Kumar, S.; Beattie, P.; Jain, S.; Rolland Jason, P.; Stossel Thomas, P.; Chunda‐Liyoka, C.; Whitesides George, M., From the Bench to the Field in Low‐Cost Diagnostics: Two Case Studies. Angew. Chem., Int. Ed. 2015, 54 (20), 5836-5853. 92. Kothary Mahendra, H.; Babu Uma, S., Infective Dose of Foodborne Pathogens in Volunteers: A Review. J. Food Saf. 2007, 21 (1), 49-68. 93. Zhang, H.; Uselman, R. R.; Yee, D., Exogenous nearinfrared fluorophores and their applications in cancer diagnosis: biological and clinical perspectives. Expert Opin. Medical Diagn. 2011, 5 (3), 241-251. 94. Luby, B. M.; Charron, D. M.; MacLaughlin, C. M.; Zheng, G., Activatable fluorescence: From small molecule to nanoparticle. Adv. Drug Delivery Rev. 2017, 113, 97-121. 95. Achilefu, S.; Dorshow, R. B.; Bugaj, J. E.; Rajagopalan, R., Novel receptor-targeted fluorescent contrast agents for in vivo tumor imaging. Invest. Radiol. 2000, 35 (8), 479-85. 96. Ke, S.; Wen, X.; Gurfinkel, M.; Charnsangavej, C.; Wallace, S.; Sevick-Muraca, E. M.; Li, C., Near-infrared optical imaging of epidermal growth factor receptor in breast cancer xenografts. Cancer Res. 2003, 63 (22), 7870-5. 97. Bunz, U. H. F.; Rotello, V. M., Gold Nanoparticle– Fluorophore Complexes: Sensitive and Discerning “Noses” for Biosystems Sensing. Angew. Chem., Int. Ed. 2010, 49 (19), 32683279. 98. Wolfbeis, O. S., An overview of nanoparticles commonly used in fluorescent bioimaging. Chem. Soc. Rev. 2015, 44 (14), 47434768. 99. Wang, Q. X.; Xue, S. F.; Chen, Z. H.; Ma, S. H.; Zhang, S.; Shi, G.; Zhang, M., Dual lanthanide-doped complexes: the development of a time-resolved ratiometric fluorescent probe for anthrax biomarker and a paper-based visual sensor. Biosens. Bioelectron. 2017, 94, 388-393. 100. Lakowicz, J. R., Principles of fluorescence spectroscopy, 3e. Springer, US 2006, 3, 1-954. 101. Jung, H. S.; Chen, X.; Kim, J. S.; Yoon, J., Recent progress in luminescent and colorimetric chemosensors for detection of thiols. Chem. Soc. Rev. 2013, 42 (14), 6019-31. 102. Lee, L.; Nordman, E.; Johnson, M.; Oldham, M., A LowCost, High-Performance System for Fluorescence Lateral Flow Assays. Biosensors 2013, 3 (4), 360-373. 103. Williams, G.; Backhouse, C.; Aziz, H., Integration of Organic Light Emitting Diodes and Organic Photodetectors for Labon-a-Chip Bio-Detection Systems. Electronics 2014, 3 (1), 43-75. 104. Gorris, H. H.; Wolfbeis, O. S., Photon-upconverting nanoparticles for optical encoding and multiplexing of cells,

Page 10 of 12

biomolecules, and microspheres. Angewandte Chemie (International ed. in English) 2013, 52 (13), 3584-600. 105. Haase, M.; Schafer, H., Upconverting nanoparticles. Angew. Chem., Int. Ed. Engl. 2011, 50 (26), 5808-29. 106. Guo, H.; Dong, N.; Yin, M.; Zhang, W.; Lou, L.; Xia, S., Visible Upconversion in Rare Earth Ion-Doped Gd2O3 Nanocrystals. J. Phys. Chem. B 2004, 108 (50), 19205-19209. 107. Auzel, F., Upconversion and Anti-Stokes Processes with f and d Ions in Solids. Chem. Rev. 2004, 104 (1), 139-174. 108. E., A. D.; J., M. R.; H., F. L.; S., W. O., Luminescent Sensing of Oxygen Using a Quenchable Probe and Upconverting Nanoparticles. Angew. Chem., Int. Ed. 2011, 50 (1), 260-263. 109. Ju, Q.; Tu, D.; Liu, Y.; Li, R.; Zhu, H.; Chen, J.; Chen, Z.; Huang, M.; Chen, X., Amine-Functionalized Lanthanide-Doped KGdF4 Nanocrystals as Potential Optical/Magnetic Multimodal Bioprobes. J. Am. Chem. Soc. 2012, 134 (2), 1323-1330. 110. Le‐Le, L.; Ruobing, Z.; Leilei, Y.; Kezhi, Z.; Weiping, Q.; R., S. P.; Yi, L., Biomimetic Surface Engineering of Lanthanide‐ Doped Upconversion Nanoparticles as Versatile Bioprobes. Angew. Chem., Int. Ed. Engl. 2012, 51 (25), 6121-6125. 111. He, M.; Liu, Z., Paper-Based Microfluidic Device with Upconversion Fluorescence Assay. Anal. Chem. 2013, 85 (24), 11691-11694. 112. He, M.; Li, Z.; Ge, Y.; Liu, Z., Portable Upconversion Nanoparticles-Based Paper Device for Field Testing of Drug Abuse. Anal. Chem. 2016, 88 (3), 1530-1534. 113. You, M.; Lin, M.; Gong, Y.; Wang, S.; Li, A.; Ji, L.; Zhao, H.; Ling, K.; Wen, T.; Huang, Y.; Gao, D.; Ma, Q.; Wang, T.; Ma, A.; Li, X.; Xu, F., Household Fluorescent Lateral Flow Strip Platform for Sensitive and Quantitative Prognosis of Heart Failure Using DualColor Upconversion Nanoparticles. ACS Nano 2017, 11 (6), 62616270. 114. Shah, K. G.; Singh, V.; Kauffman, P. C.; Abe, K.; Yager, P., Mobile Phone Ratiometric Imaging Enables Highly Sensitive Fluorescence Lateral Flow Immunoassays without External Optical Filters. Anal. Chem. 2018, 90 (11), 6967-6974. 115. Mei, Q.; Jing, H.; Li, Y.; Yisibashaer, W.; Chen, J.; Nan Li, B.; Zhang, Y., Smartphone based visual and quantitative assays on upconversional paper sensor. Biosens. Bioelectron. 2016, 75, 427432. 116. Rajendran, V. K.; Bakthavathsalam, P.; Jaffar Ali, B. M., Smartphone based bacterial detection using biofunctionalized fluorescent nanoparticles. Microchim. Acta 2014, 181 (15), 18151821. 117. Lee, W.-I.; Shrivastava, S.; Duy, L.-T.; Yeong Kim, B.; Son, Y.-M.; Lee, N.-E., A smartphone imaging-based label-free and dual-wavelength fluorescent biosensor with high sensitivity and accuracy. Biosens. Bioelectron. 2017, 94, 643-650. 118. Wang, L.-J.; Chang, Y.-C.; Sun, R.; Li, L., A multichannel smartphone optical biosensor for high-throughput point-of-care diagnostics. Biosens. Bioelectron. 2017, 87, 686-692. 119. Wang, J., Electrochemical biosensors: Towards point-ofcare cancer diagnostics. Biosens. Bioelectron. 2006, 21 (10), 18871892. 120. Grieshaber, D.; MacKenzie, R.; Vörös, J.; Reimhult, E., Electrochemical Biosensors - Sensor Principles and Architectures. Sensors 2008, 8 (3), 1400-1458. 121. Luppa, P. B.; Sokoll, L. J.; Chan, D. W., Immunosensors-principles and applications to clinical chemistry. Clin. Chim. Acta 2001, 314 (1-2), 1-26. 122. Musameh, M.; Wang, J.; Merkoci, A.; Lin, Y., Lowpotential stable NADH detection at carbon-nanotube-modified glassy carbon electrodes. Electrochem. Commun. 2002, 4 (10), 743-746. 123. Willner, I. I.; Katz, E., Integration of Layered Redox Proteins and Conductive Supports for Bioelectronic Applications. Angew. Chem., Int. Ed. Engl. 2000, 39 (7), 1180-1218. 124. Jia, J.; Wang, B.; Wu, A.; Cheng, G.; Li, Z.; Dong, S., A Method to Construct a Third-Generation Horseradish Peroxidase

10 ACS Paragon Plus Environment

Page 11 of 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

Biosensor:  Self-Assembling Gold Nanoparticles to ThreeDimensional Sol−Gel Network. Anal. Chem. 2002, 74 (9), 2217-2223. 125. Kara, P.; de la Escosura-Muñiz, A.; Maltez-da Costa, M.; Guix, M.; Ozsoz, M.; Merkoçi, A., Aptamers based electrochemical biosensor for protein detection using carbon nanotubes platforms. Biosens. Bioelectron. 2010, 26 (4), 1715-1718. 126. Zhou, Y.; Yin, H.; Li, X.; Li, Z.; Ai, S.; Lin, H., Electrochemical biosensor for protein kinase A activity assay based on gold nanoparticles-carbon nanospheres, phos-tag-biotin and βgalactosidase. Biosens. Bioelectron. 2016, 86, 508-515. 127. Bryan, T.; Luo, X.; Bueno, P. R.; Davis, J. J., An optimised electrochemical biosensor for the label-free detection of C-reactive protein in blood. Biosens. Bioelectron. 2013, 39 (1), 94-98. 128. Wang, J., Electrochemical Glucose Biosensors. Chem. Rev. 2008, 108 (2), 814-825. 129. Zhou, M.; Zhai, Y.; Dong, S., Electrochemical Sensing and Biosensing Platform Based on Chemically Reduced Graphene Oxide. Anal. Chem. 2009, 81 (14), 5603-5613. 130. Shan, C.; Yang, H.; Song, J.; Han, D.; Ivaska, A.; Niu, L., Direct Electrochemistry of Glucose Oxidase and Biosensing for Glucose Based on Graphene. Anal. Chem. 2009, 81 (6), 2378-2382. 131. Drummond, T. G.; Hill, M. G.; Barton, J. K., Electrochemical DNA sensors. Nat. Biotechnol. 2003, 21, 1192-9. 132. Fan, H.; Zhao, K.; Lin, Y.; Wang, X.; Wu, B.; Li, Q.; Cheng, L., A new electrochemical biosensor for DNA detection based on molecular recognition and lead sulfide nanoparticles. Anal. Biochem. 2011, 419 (2), 168-172. 133. Huang, H.; Bai, W.; Dong, C.; Guo, R.; Liu, Z., An ultrasensitive electrochemical DNA biosensor based on graphene/Au nanorod/polythionine for human papillomavirus DNA detection. Biosens. Bioelectron. 2015, 68, 442-446. 134. Farjami, E.; Campos, R.; Nielsen, J. S.; Gothelf, K. V.; Kjems, J.; Ferapontova, E. E., RNA Aptamer-Based Electrochemical Biosensor for Selective and Label-Free Analysis of Dopamine. Anal. Chem. 2013, 85 (1), 121-128. 135. Ferapontova, E. E.; Olsen, E. M.; Gothelf, K. V., An RNA Aptamer-Based Electrochemical Biosensor for Detection of Theophylline in Serum. J. Am. Chem. Soc. 2008, 130 (13), 42564258. 136. Bozokalfa, G.; Akbulut, H.; Demir, B.; Guler, E.; Gumus, Z. P.; Odaci Demirkol, D.; Aldemir, E.; Yamada, S.; Endo, T.; Coskunol, H.; Timur, S.; Yagci, Y., Polypeptide Functional Surface for the Aptamer Immobilization: Electrochemical Cocaine Biosensing. Anal. Chem. 2016, 88 (7), 4161-4167. 137. Aydindogan, E.; Guler Celik, E.; Odaci Demirkol, D.; Yamada, S.; Endo, T.; Timur, S.; Yagci, Y., Surface Modification with a Catechol-Bearing Polypeptide and Sensing Applications. Biomacromolecules 2018, 19(7), 3067-3076. 138. Yilmaz, S. T.; Guler, C. E.; Cansu, A.; Pinar, G. Z.; Raif, I.; Eda, A.; Mustafa, C.; Ebru, A.; Hakan, C.; Suna, T.; Yusuf, Y., A Functional Platform for the Detection of JWH-073 as a Model for Synthetic Cannabinoids. ChemElectroChem 2018, 5 (9), 1253-1258. 139. Demir, B.; Yilmaz, T.; Guler, E.; Gumus, Z. P.; Akbulut, H.; Aldemir, E.; Coskunol, H.; Colak, D. G.; Cianga, I.; Yamada, S.; Timur, S.; Endo, T.; Yagci, Y., Polypeptide with electroactive endgroups as sensing platform for the abused drug ‘methamphetamine’ by bioelectrochemical method. Talanta 2016, 161, 789-796. 140. Yuyan, S.; Jun, W.; Hong, W.; Jun, L.; A., A. I.; Yuehe, L., Graphene Based Electrochemical Sensors and Biosensors: A Review. Electroanalysis 2010, 22 (10), 1027-1036. 141. da Silva, E. T. S. G.; Souto, D. E. P.; Barragan, J. T. C.; Giarola, J. de F.; de Moraes, A. C. M.; Kubota, L. T.; Electrochemical Biosensors in Point-of-Care Devices: Recent Advances and Future Trends. ChemElectroChem 2017, 4 (4), 778-794. 142. Zarei, M., Portable biosensing devices for point-of-care diagnostics: Recent developments and applications. TrAC, Trends Anal. Chem. 2017, 91, 26-41.

143. Sun, A. C.; Yao, C.; A.G, V.; Hall, D. A., An efficient power harvesting mobile phone-based electrochemical biosensor for point-of-care health monitoring. Sens. Actuators, B 2016, 235, 126135. 144. Aronoff-Spencer, E.; Venkatesh, A. G.; Sun, A.; Brickner, H.; Looney, D.; Hall, D. A., Detection of Hepatitis C core antibody by dual-affinity yeast chimera and smartphone-based electrochemical sensing. Biosens. Bioelectron. 2016, 86, 690-696. 145. Aymerich, J.; Márquez, A.; Terés, L.; Muñoz-Berbel, X.; Jiménez, C.; Domínguez, C.; Serra-Graells, F.; Dei, M., Costeffective smartphone-based reconfigurable electrochemical instrument for alcohol determination in whole blood samples. Biosens. Bioelectron. 2018, 117, 736-742. 146. Ji, D.; Liu, Z.; Liu, L.; Low, S. S.; Lu, Y.; Yu, X.; Zhu, L.; Li, C.; Liu, Q., Smartphone-based integrated voltammetry system for simultaneous detection of ascorbic acid, dopamine, and uric acid with graphene and gold nanoparticles modified screen-printed electrodes. Biosens. Bioelectron. 2018, 119, 55-62. 147. Ji, D.; Liu, L.; Li, S.; Chen, C.; Lu, Y.; Wu, J.; Liu, Q., Smartphone-based cyclic voltammetry system with graphene modified screen printed electrodes for glucose detection. Biosens. Bioelectron. 2017, 98, 449-456. 148. Liu, L.; Zhang, D.; Zhang, Q.; Chen, X.; Xu, G.; Lu, Y.; Liu, Q., Smartphone-based sensing system using ZnO and graphene modified electrodes for VOCs detection. Biosens. Bioelectron. 2017, 93, 94-101. 149. Bandodkar, A. J.; Imani, S.; Nuñez-Flores, R.; Kumar, R.; Wang, C.; Mohan, A. M. V.; Wang, J.; Mercier, P. P., Re-usable electrochemical glucose sensors integrated into a smartphone platform. Biosens. Bioelectron. 2018, 101, 181-187. 150. Fan, Y.; Liu, J.; Wang, Y.; Luo, J.; Xu, H.; Xu, S.; Cai, X., A wireless point-of-care testing system for the detection of neuronspecific enolase with microfluidic paper-based analytical devices. Biosens. Bioelectron. 2017, 95, 60-66. 151. Zhao, C.; Thuo, M. M.; Liu, X., A microfluidic paperbased electrochemical biosensor array for multiplexed detection of metabolic biomarkers. Sci. Technol. Adv. Mater. 2013, 14 (5), 054402. 152. Delaney, J. L.; Doeven, E. H.; Harsant, A. J.; Hogan, C. F., Reprint of: Use of a mobile phone for potentiostatic control with low cost paper-based microfluidic sensors. Anal. Chim. Ccta 2013, 803, 123-127. 153. Arduini, F.; Micheli, L.; Moscone, D.; Palleschi, G.; Piermarini, S.; Ricci, F.; Volpe, G., Electrochemical biosensors based on nanomodified screen-printed electrodes: Recent applications in clinical analysis. TrAC, Trends Anal. Chem. 2016, 79, 114-126. 154. Dou, Y.; Jiang, Z.; Deng, W.; Su, J.; Chen, S.; Song, H.; Aldalbahi, A.; Zuo, X.; Song, S.; Shi, J.; Fan, C., Portable detection of clenbuterol using a smartphone-based electrochemical biosensor with electric field-driven acceleration. J. Electroanal. Chem. 2016, 781, 339-344. 155. Catarino, R.; Vassilakos, P.; Scaringella, S.; UndurragaMalinverno, M.; Meyer-Hamme, U.; Ricard-Gauthier, D.; Matute, J. C.; Petignat, P., Smartphone Use for Cervical Cancer Screening in Low-Resource Countries: A Pilot Study Conducted in Madagascar. PLoS One 2015, 10 (7), e0134309. 156. Gallay, C.; Girardet, A.; Viviano, M.; Catarino, R.; Benski, A. C.; Tran, P. L.; Ecabert, C.; Thiran, J. P.; Vassilakos, P.; Petignat, P., Cervical cancer screening in low-resource settings: a smartphone image application as an alternative to colposcopy. Int. J. Women's Health 2017, 9, 455-461.

11 ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 12

Table of Contents

12 ACS Paragon Plus Environment