Advanced colorimetric paper sensors using color focusing effect

Apr 5, 2019 - ... suited for real-field diagnosis, their widespread use is hindered by signal blurring at the detection spot due to the action of capi...
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Advanced colorimetric paper sensors using color focusing effect based on asymmetric flow of fluid Hyungjun Jang, Jin-Ho Park, Jusung Oh, Kihyeun Kim, and Min-Gon Kim ACS Sens., Just Accepted Manuscript • DOI: 10.1021/acssensors.9b00390 • Publication Date (Web): 05 Apr 2019 Downloaded from http://pubs.acs.org on April 6, 2019

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Advanced colorimetric paper sensors using color focusing effect based on asymmetric flow of fluid Hyungjun Jang‡, Jin-Ho Park‡, Jusung Oh, Kihyeun Kim, and Min-Gon Kim* Department of Chemistry, School of Physics and Chemistry, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea

ABSTRACT: Although paper-based colorimetric sensors utilizing enzymatic reactions are well suited for real-field diagnosis, their widespread use is hindered by signal blurring at the detection spot due to the action of capillary forces on the liquid and the corresponding membrane. In this study, we eliminated signal losses commonly observed during enzyme-mediated colorimetric sensing and achieved pattern-free quantitative analysis of glucose and uric acid by mixing enzymes and color-forming reagents with chitosan oligosaccharide lactate (COL), which resulted in perfectly focused colorimetric signals at the detection spot, using asymmetric flow induced by changing the flow rate of the COL-treated paper. The targets were calibrated with 0–500 mg/dL of glucose and 0–200 mg/dL of uric acid, and the limits of detection was calculated to be 0.6 and 0.03 mg/dL, respectively. In human urine, the correlation has a high response between the measured and spiked concentrations, and the stability of the enzyme mixture including COL increased by 41% for glucose oxidase mixture and 29% for uricase mixture, compared to the corresponding mixtures without COL. Thus, the color focusing and pattern-free sensor, which have the advantages of easy fabrication, easy handling, and high stability, should be applied to real-field diagnosis.

KEYWORDS: asymmetric flow, color focusing, chitosan oligosaccharide lactate, human urine, colorimetric sensor

Microfluidic paper-based analytical devices (μPADs), offering the advantages of low manufacturing cost, small sample volume, portability, simplicity, ease of handling, and rapid response, have received considerable attention as promising biosensor platforms [1-3] and have been extensively applied in clinical diagnostics, environmental monitoring, and food safety analysis [4-7]. Depending on the type of detection, sensing platforms can be classified into those employing colorimetric, electric, and optical (including luminescence, fluorescence, or Raman signals) methods [8-12]. Among these platforms, colorimetric ones are the most popular, offering increased user friendliness and allowing simple and rapid detection with the naked eye [7]. As a fast and easy-to-apply method, enzyme-mediated colorimetric sensing, which is based on the enzyme-catalyzed conversion of analytes into colored products [13,14], is widely employed to detect biomolecules like glucose, uric acid, and cholesterol [15,16]. For instance, the detection of glucose or uric acid is accomplished using mixtures of horseradish peroxidase (HRP) with glucose oxidase (GOx) or uricase, respectively. In particular, GOx and uricase oxidize their respective targets to form hydrogen peroxide as a by-product, which, in turn, is used by HRP to oxidize color-forming reagents and generate color, the intensity of which depends on the target concentration [17,18]. However, almost all colorimetric membrane sensors based on enzyme-mediated reactions use watersoluble color-forming reagents, and therefore, suffer from the problem of sample washout from the detection spot under the action of capillary forces. This phenomenon eventually causes poor signal appearance, non-uniform coloring, and reagent release from the detection spot by fluidic flow [19]. To mitigate the aforementioned problem, membranes are commonly subjected to hydrophobic patterning [5]. The produced pattern acts as a boundary separating the hydrophilic and hydrophobic zones, and allows well-aligned flow or prevents sample release from the detection zone, enhancing the sensing performance and enabling easy handling of the membrane sensor. Diverse patterning methods, including photolithography [20], polydimethylsiloxane or inkjet printing [21,22], solvent-based patterning [23], wax printing [1], the use of a wax pen [24], and screen printing [25], have been developed and applied to the μPADs platform. In addition, various materials, including functional polymers, surfactants, and nanoparticles, have also been employed in paper-based sensors for the same purpose. To obtain a clear signal, sodium dodecyl sulfate and the nanoparticles were incorporated into the paper devices, resulting in an enhancement in the signal from the targets [19,26,27]. Chitosan is one of the most common bio-compatible, non-toxic, and biodegradable functional polymers [28,29]. Recently, Gabriel et al. developed chitosan-modified paper microfluidic devices to obtain uniform color signals at the detection spot, and verified the feasibility of controllable colorimetric sensing [30]. Many researchers have reported highly sensitive colorimetric paper-based sensors using chitosan, which exhibit these properties [31,32]. However,

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these sensors required the application of a patterning process, and an intense colorimetric signal still appeared at the paper boundary. The enzyme activity was also reported to decrease in the presence of acetic acid, which is a component required for successful chitosan dissolution. Herein, we developed a pattern-free colorimetric membrane sensor using chitosan oligosaccharide lactate (COL), and used it to obtain perfect color focusing on paper sensors and increased enzyme stability. COL was superior to chitosan because the former was structurally similar to the latter, while exhibiting lower molecular weight. In contrast to other chitosan derivatives, COL increased the membrane wall thickness and formed a film between pores, acting as a blocking agent. Therefore, the flow rate on the paper upon treatment with COL was significantly lower than that obtained by treatment with other derivatives. Due to the asymmetric flow induced by the difference in flow rate between the COL-treated zone and the rest of the paper, glucose and uric acid were detected by using target specific enzymes and color-forming reagents without any blurring of signal. MATERIALS AND METHODS Materials D-glucose, sodium hydroxide, uric acid, chitosan (141 kDa, 218 kDa, and 327 kDa), glycol chitosan (GC), methylglycol chitosan (MGC), COL (5 kDa), and 4-aminoantipyrine (4-AAP) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Standard cholesterol was purchased from Verichem laboratories, Inc. (Avenue, USA). GOx, uricase, cholesterol esterase, cholesterol oxidase and HRP were purchased from Toyobo Co., Ltd. (Osaka, Japan). N,N-Bis(4-sulfobutyl)-3,5-dimethylaniline disodium salt (MADB) was purchased from Dojindo Molecular Technologies, Inc. (Tokyo, Japan). Pooled normal human urine was purchased from Innovative Research (Novi, MI, USA). 1X phosphate buffer saline was obtained from LPS Solution (South Korea). Nitrocellulose (NC) membranes and human serum were purchased from Merck Millipore (Darmstadt, Germany). Deionized water (DW), with a resistivity of 18.2 MΩ cm, was obtained using a PURELAB Option-Q water purification system (UK). Preparation of enzyme mixtures The enzymatic colorimetric assay used to detect glucose and uric acid was performed as follows. GOx/uricase and HRP were diluted to 500 U/mL in PBS with pH 6.0/7.0. 4-AAP and MADB were dissolved in DW to obtain 500 mM concentration. Moreover, DW was used to prepare solutions of 1.67 wt% chitosan (141 kDa) (with 2 wt% acetic acid) and 1.67 wt% MGC, GC and COL (1.67, 3.33, and 5 wt%). For glucose sensing, 500 U/mL GOx–HRP (2 μL) and 500 mM 4-AAP–MADB (2 μL) solutions were mixed with 1–3 wt% (12 μL) solutions of chitosan derivatives. For uric acid detection, 1800 U/mL uricase–500 U/mL HRP (2 μL) and 500 mM 4-AAP–MADB (2 μL) solutions were mixed with 1–3 wt% (12 μL) solutions of the chitosan derivatives. Measurement of flow rate The NC membrane was cut into 4 mm × 25 mm specimens, which were immersed in 1 wt% GC, 1 wt% MGC, 1 wt% chitosan (141 kDa), and 1, 2, or 3 wt% COL. DW (100 μL) was injected into a 96-well plate (Corning, Inc., USA). After drying, the treated membrane specimens were dipped in the 96-well plate, and the flow rate (mm/min) was calculated. Quantitation of glucose and uric acid in human urine Pooled normal human urine with unknown contents of glucose and uric acid was analyzed using a Hitachi 7020 automatic analyzer (Hitachi, Tokyo, Japan), and the glucose and uric acid levels were determined to be 1 and 17 mg/dL, respectively. Calibration of glucose and uric acid in DW and human urine The NC membrane was cut into 20 mm × 20 mm pieces (TBC-50TS cutting machine, TAEWOO Co., Ltd., South Korea) that were spotted with 0.8 μL of enzyme and the color reagent mixtures for glucose and uric acid at six positions and dried in a desiccator for 1.5 min. After drying, 55 μL of glucose (0, 1, 10, 25, 50, 100, 250, and 500 mg/dL) in DW or uric acid (0, 1, 10, 25, 50, 100, and 200 mg/dL) in DW with 20 mM of sodium hydroxide was loaded on the center of the sensor. For detection in human urine, the concentrations of glucose and uric acid were adjusted to 1–250 and 17–200 mg/dL, respectively. As mentioned above, 55 μL of the target in urine was loaded on the center of the sensor. The color signal appeared within 3 min, and its intensity was measured using a Bio Rad Universal Hood III instrument (Bio Rad Laboratories, Inc., Hercules, CA). Stability test of sensor The prepared sensors were stored at room temperature in air for 30 days, and the 55 μL of glucose (250 mg/dL) and uric acid (200 mg/dL) in DW was loaded on the sensor on each day (0, 0.5, 1, 2, 3, 7, 14, and 30). RESULTS AND DISCUSSION Optimization of color focusing

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Blurring of the color signal was observed on the detection spots (color spread to the membrane edge) when a COL-free enzyme mixture–spotted membrane was employed, while pronounced color focusing was observed when COL was utilized (Figure 1). Subsequently, we probed the color focusing effects of other chitosan derivatives (MGC, GC, chitosan (141 kDa), and COL; Figure 2A). In the absence of these materials or in the presence of MGC and GC, color spread from the spotted center to the paper edge because of the water-soluble nature of the employed color-forming reagents, while the color signal was focused in the presence of normal chitosan (141 kDa). However, the use of chitosan resulted in color intensity loss, and the achieved focusing effect remained insufficient (blurry spots were observed). To our delight, use of COL effectively focused the colorimetric signals in a non-patterned paper even at a concentration of 1 wt%, and a higher concentration (3 wt%) was used in subsequent experiments for better performance (Figure S1). To verify the reason for the color focusing effect, the change in flow rate was measured at the NC membrane impregnated with various materials (Figure 2B). For the control (i.e., the non-treated membrane), the measured flow rate 41 mm/min, while it was significantly lower when the NC membrane was impregnated. Flow rates of membranes treated with MGC, GC, chitosan (141 kDa), and 1–3% COL were confirmed to be 17.5, 4.1, 1.04, 0.65, 0.5, and 0.2 mm/min, respectively. From the results, a correlation was observed between improved color focusing and decreasing flow rate by using these materials (Figures 2A and B). It has been reported that the decreasing flow rate was correlated with decreasing pore size of the membrane [33]. Scanning electron microscopy revealed that COL treatment resulted in the blockage of membrane pores and an increase the wall thickness of the membrane (Figure S2). Figure 2C show the steps involved in inducing the asymmetric flow. Due to the difference in flow rate between normal and materialtreated membrane, asymmetric flow was observed on the prepared sensor. When the target-containing sample was loaded on the center of the developed sensor, the sample solution diffuses from the center to the edge of membrane in a laminar flow. Thus, the membrane was preferentially wetted in the regions without the enzyme mixture. Then, the solution gradually permeated into the zone with the enzyme mixture. Therefore, the flow of the solution converged to the center of this zone, and the color focusing effect appeared without blurring. In a supplementary video, the difference between developed and conventional methods is shown using the flow test. Colorimetric sensor optimization To clearly obtain the colorimetric signal, the optimization of spot sizes was conducted for various diameters (3, 4, and 5 mm). The smallest diameter was chosen because the fluid could not perfectly permeate to the center of the spot when the diameter was greater than 3 mm at 3 min (Figure S3). Unlike the flowrate tests using the membrane treated by materials over the entire, the membrane of the developed sensor was locally treated by materials. In this situation, the fluid moved faster by passing through the non-treated membrane until it arrived to the COL treated area. Thus, the real permeation time (3 min) was lower than the calculated time based on the flowrate test (7.5 min). Additionally, optimized sensing conditions were determined by comparing color intensities obtained using different concentrations of different species (HRP, GOx, uricase, MADB, and 4-AAP) involved in enzymatic reactions (Figure S4). Color intensities steadily increased with increasing GOx and uricase concentrations. To account for excess analyte concentrations and the matrix effect of reallife samples, the highest tested concentrations of GOx (50 U/mL) and uricase (180 U/mL) were chosen for further experiments. After optimizing GOx and uricase concentrations, we screened the concentrations of 4-AAP, MADB, and HRP, which revealed that for glucose detection, color intensity saturation was observed at reagent concentrations of ~10 mM or U/mL, while for uric acid detection, color intensity increased until the highest concentration of each reagent was reached. To obtain the highest signal intensity, the concentrations of 4-AAP, MADB, and HRP selected for further experiments were 50 mM, 50 mM, and 50 U/mL, respectively. Quantitation of glucose and uric acid in DW Glucose and uric acid quantitations were performed under optimized conditions using six detection spots, which are used to achieve stable signal intensity. After the glucose/uric acid-in-DW sample (0–500/0–200 mg/dL) was loaded on the center of the sensor, welldefined color signals appeared because of induced asymmetric flow, and their intensity increased with increasing analyte concentration within 3 min (Figure 3). In the case of glucose sensing, the color intensity was measured as 1607±162, 2453±269, 8623±159, 17833±224, 26177±652, 34168±621, 38944±654 and 40941±441, and uric acid detection was measured as 3024±102, 4247±90, 18094±188, 24224±94, 29622±350, 34139±443 and 34787±297, respectively. Since the enzymatic reaction has a nonlinear curve at high concentrations of targets, the intensity of the color signal was saturated over the concentrations of glucose and uric acid (100 and 50 mg/dL). The calibration curve for glucose and uric acid detection was well fitted to the Hill equation: I = Imax×Cn/(kn+Cn) with high response (R2 = 0.99 and 0.98, respectively) [34,35]. I is the measured intensity of color, Imax is the maximum intensity of color signal, C is the concentration of glucose or uric acid, k is the concentration of glucose or uric acid at half Imax, and n is the Hill coefficient. The values of Imax, k, and n were 43407±3028/38866±7128, 35±7/13±7, and 1.1±0.2/0.8±0.2, respectively, for glucose/uric acid. For each target, the limit of detection (LOD) was calculated with 3 times of standard deviation of zero conditions. In addition, insets showed the linear range of detection for glucose (1–100 mg/dL) and uric acid (10–100 mg/dL), and R2 was calculated to be 0.97 and 0.98, respectively, in the log-log plot. The results showed that the developed sensor sufficiently covers the normal physiological ranges of glucose and uric acid levels in human urine (0–14.4 and 24.8–74.4 mg/dL, respectively) [36,37]. Before the sensor was operated in human urine, unknown amounts of glucose and uric acid in urine must be confirmed. By using a chemical analyzer, the concentrations were measured to 1 mg/dL for glucose and 17 mg/dL for uric acid in human urine, and the final concentrations of targets were adjusted to 1, 10, 25, 50, 100, and 250 mg/dL for glucose and 17, 25, 50, 100, and 200 mg/dL for uric acid. This spiked sample was loaded on developed sensor; the correlation between the measured and spiked concentrations of the targets is shown in Figure 4. R2 value of the linear plot was confirmed to be 0.999 and 0.987 for glucose and uric acid, respectively,

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and the corresponding slopes were calculated to be 1.05 and 0.95. The calculated recovery rate is shown in Table S1, and good response was observed under all the tested conditions except for the lowest concentration of glucose. Additionally, the simultaneous multiple detection for various analytes was conducted on the human urine and serum (Figure S5). A total of 1, 17, 0 mg/dL and 88, 3, 104 mg/dL of glucose, uric acid and cholesterol were measured using a Hitachi 7020 automatic analyzer in the control urine and serum samples. The color intensity of each sensing spot, which included target specific enzymes with color-forming reagents, significantly increased when additional amounts of analytes (100, 50 and 50 mg/dL of glucose, uric acid and cholesterol) were added. Although the test was conducted with real urine and serum, each sensing spot showed good selectivity without interference. However, in uric acid detection, the intensity was slightly increased by adding the standard cholesterol sample, which has the small amount of uric acid. Stability test and comparison w/ or w/o COL Enzyme immobilization was known as a stabilizing material of enzymes as it mimicked the natural mode of enzymes in living cells by forming a heterogeneous immobilized enzyme system. As a support material, chitosan has various advantages including biocompatibility, non-toxicity, high affinity to proteins, hydrophilicity, mechanical stability, and rigidity [38,39]. Color signal stability was checked using two types of sensors (with and without COL). The targets were loaded onto reagent-impregnated membranes that were subsequently exposed to the atmosphere for 30 days under ambient conditions (Figure 5), and color signal intensities were measured after 0, 1, 2, 3, 4, 7, 14, and 30 days. For glucose sensing, color intensity decreased by 53% after 30 days when no COL was used, while a smaller decrease of 12% was obtained in the presence of COL. The corresponding decreases obtained for uric acid sensing were 76% (without COL) and 47% (with COL). Therefore, the COL-impregnated sensors were more stable than the (COL-free) sensors. CONCLUSION Herein, we used COL to successfully develop a pattern-free membrane sensor for simple, easy, and accurate colorimetric detection of glucose and uric acid, based on induced asymmetric flow. This excellent performance was ascribed to COL physically blocking membrane pores and thus obstructing fluid flow to give the localized enzymatic reaction at the detection spot sufficient time for color generation. Therefore, the usage of COL obviated the need for membrane patterning and allowed quantitation of glucose and uric acid at concentrations of 0–500 and 0–200 mg/dL, respectively, while the respective LODs were determined to be 0.6 and 0.03 mg/dL. For detection in human urine, the linear ranges of this sensor successfully covered the physiological ranges of the targets. Since the location of the sensing spot can be freely controlled in this sensor, we hypothesize that the color focusing effect could be helpful in measuring the color signal using a detector that required setting of the sensing area. In future, we plan to improve the developed sensor and to fabricate a portable device for target detection. ASSOCIATED CONTENT

Supporting Information The Supporting Information is available free of charge on the ACS Publications website. Optimization of COL concentrations; SEM image of NC membrane and impregnated with COL; Optimization of spots size; Optimization of concentration of enzyme mixture; Table of recovery test in human urine; Supplementary video for comparison of flow between developed and conventional method (PDF, mp4). Corresponding Author Min-Gon Kim. Tel.: +82-62-715-3330, fax: +82-62-715-2887, e-mail: [email protected] ORCID Min-Gon Kim: 0000-0002-3525-0048

Author Contributions ‡These authors contributed equally.

ACKNOWLEDGMENT

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This work was supported by the Technology Innovation Program (10060155) funded by the Ministry of Trade, Industry & Energy (MI, Korea) and was financially supported by grants from the GRL Program (NRF-2013K1A1A2A02050616) funded by the Ministry of Science, ICT and Future Planning of the Republic of Korea. REFERENCES 1. Carrilho, E.; Martinez, A.W.; Whitesides, G.M. Understanding Wax Printing: A Simple Micropatterning Process for Paper-Based Microfluidics, Anal. Chem. 2009, 81, 7091-7095. 2. 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, 3-10. 3. Mahadeva, S.K.; Walus, K.; Stoeber, B. Paper as a Platform for Sensing Applications and Other Devices: A Review, ACS Appl. Mater. Interfaces. 2015, 7, 8345-8362. 4. Shih, W.C.; Yang, M.C.; Lin, M.S. Development of disposable lipid biosensor for the determination of total cholesterol, Biosens. Bioelectron. 2009, 24, 1679-1684. 5. Yetisen, A.K.; Akram, M.S.; Lowe, C.R. Paper-based microfluidic point-of-care diagnostic devices, Lab Chip. 2013, 13, 22102251. 6. Chang, J.; Li, H.; Hou, T.; Li, F. Paper-based fluorescent sensor for rapid naked-eye detection of acetylcholinesterase activity and organophosphorus pesticides with high sensitivity and selectivity, Biosens. Bioelectron. 2016, 86, 971-977. 7. Singh, A.T.; Lantigua, D.; Meka, A.; Taing, S.; Pandher, M.; Camci-Unal, G. Paper-Based Sensors: Emerging Themes and Applications. Biosens Bioelectron. 2012, 32, 2838, doi:10.3390/s18092838. 8. Rattanarat, P.; Dungchai, W.; Cate, D.; Volckens, J.; Chailapakul, O.; Henry, C.S. Multilayer Paper-Based Device for Colorimetric and Electrochemical Quantification of Metals, Anal. Chem. 2014, 86, 3555-3562. 9. Oliveira, K.A.; Medrado e Silva, P.B.; de Souza, F.R.; Martins, F.T.; Coltro, W.K.T. Kinetic study of glucose oxidase on microfluidic toner-based analytical devices for clinical diagnostics with image-based detection, Anal. Methods. 2014, 6, 4995-5000. 10. Oh, J.-M.; Chow, K.-F. Recent developments in electrochemical paperbased analytical devices, Anal. Methods. 2015, 7, 79517960. 11. 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, 3150-3176. 12. Ju, Q.; Noor, M.O.; Krull, U.J. Paper-based biodetection using luminescent nanoparticles, Analyst. 2016, 141, 2838-2860. 13. Hossain, S.M.Z.; Brennan, J.D. β-Galactosidase-Based Colorimetric Paper Sensor for Determination of Heavy Metals, Anal. Chem. 2011, 83, 8772-8778. 14. Liu, S.; Su, W.; Ding, X. A Review on Microfluidic Paper-Based Analytical Devices for Glucose Detection, Sensors 2016, 16, 2086, doi:10.3390/s18092838. 15. Li, C.G.; Joung, H.A.; Noh, H.; Song, M.B.; Jung, H. One-touch-activated blood multidiagnostic system using a minimally invasive hollow microneedle integrated with a paper-based sensor, Lab Chip. 2015, 15, 3286-3292. 16. Wang, X.; Li, F.; Cai, Z.; Liu, K.; Li, J.; Zhang, B.; He, J. Sensitive colorimetric assay for uric acid and glucose detection based on multilayer-modified paper with smartphone as signal readout, Anal. Bioanal. Chem. 2018, 410, 2647-2655. 17. Cao, X.; Li, Y.; Zhang, Z; Yu, J.; Qian, J.; Liu, S. Catalytic activity and stability of glucose oxidase/horseradish peroxidase coconfined in macroporous silica foam, Analyst. 2012, 137, 5785-5791. 18. Pal, J.; Pal, T. Enzyme mimicking inorganic hybrid Ni@MnO2 for colorimetric detection of uric acid in serum samples, RSC Adv. 2016, 6, 83738-83747. 19. Evans, E.; Gabriel, E.F.; Benavidez, T.E.; Coltro, W.K.T.; Garcia, C.D. Modification of microfluidic paper-based devices with silica nanoparticles, Analyst. 2014, 139, 5560-5567. 20. Martinez, A.W.; Phillips, S.T.; Butte, M.J.; Whitesides, G.M. Patterned Paper as a Platform for Inexpensive, Low‐Volume, Portable Bioassays, Angew. Chem. Int. Ed. 2007, 46, 1318-1320. 21. Bruzewicz, D.A.; Reches, M.; Whitesides, G.M. Low-Cost Printing of Poly(dimethylsiloxane) Barriers To Define Microchannels in Paper, Anal. Chem. 2008, 80, 3387-3392. 22. Abe, K.; Suzuki, K.; Citterio, D. Inkjet-Printed Microfluidic Multianalyte Chemical Sensing Paper, Anal. Chem. 2008, 80, 69286934. 23. Song, M.-B.; Joung, H.-A.; Oh, Y.K.; Jung, K.; Ahn, Y.D.; Kim, M.G. Tear-off patterning: a simple method for patterning nitrocellulose membranes to improve the performance of point-of-care diagnostic biosensors, Lab Chip. 2015, 15, 3006-3012. 24. Li, Z.; Li, F.; Xing, Y.; Liu, Z.; You, M.; Li, Y.; Wen, T.; Qu, Z.; Li, X.L.; Xu, F. Pen-on-paper strategy for point-of-care testing: Rapid prototyping of fully written microfluidic biosensor, Biosens. Bioelectron. 2017, 98, 478-485. 25. Mohammadi, S.; Maeki, M.; Mohamadi, R.M.; Ishida, A.; Tani, H.; Tokeshi, M. An instrument-free, screen-printed paper microfluidic device that enables bio and chemical sensing, Analyst. 2015, 140, 6493-6499.

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26. Rattanarat, P.; Dungchai, W.; Siangproh, W.; Chailapakul, O.; Henry, C.S. Sodium dodecyl sulfate-modified electrochemical paper-based analytical device for determination of dopamine levels in biological samples, Analytica Chimica Acta. 2012, 744, 1-7 27. Nouanthavong, S.; Nacapricha, D.; Henry, C.S.; Sameenoi, Y. Pesticide analysis using nanoceria-coated paper-based devices as a detection platform. Analyst, 2016, 141, 1837 28. Kumar, MNVR. A review of chitin and chitosan applications. React Funct Polym. 2000, 46, 1-27. 29. Wang, S.; Ge, L.; Song, X.; Yu, J.; Ge, S.; Huang, J.; Zheng, F. Paper-based chemiluminescence ELISA: Lab-on-paper based on chitosan modified paper device and wax-screen-printing, Biosens. Bioelectron. 2012, 31, 212-218. 30. Gabriel, E.F.; Garcia, P.T.; Cardoso, T.M.G; Lopes, F.M.; Martins, F.T.; Coltro, W.K.T. Highly sensitive colorimetric detection of glucose and uric acid in biological fluids using chitosan-modified paper microfluidic devices, Analyst. 2016, 141, 4749-4756. 31. Santana-Jiménez, L.A.; Márquez-Lucero, A.; Osuna, V.; Estrada-Moreno, I.; and Dominguez, R.B. Naked-Eye Detection of Glucose in Saliva with Bienzymatic Paper-Based Sensor. Sensors 2018, 18, 1071; doi:10.3390/s18041071. 32. Wang, X.; Li, F.; Cai, Z.; Liu, K.; Li, J.; Zhang, B.; He, J. Sensitive colorimetric assay for uric acid and glucose detection based on multilayer-modified paper with smartphone as signal readout. Analytical and Bioanalytical Chemistry, 2018, 410, 2647–2655. 33. Park, J.; Shin, J.H.; Park, J.-K. Pressed Paper-Based Dipstick for Detection of Foodborne Pathogens with Multistep Reactions, Anal. Chem. 2016, 88, 3781-3788. 34. Cheng, C.-M.; Martinez, A.W.; Gong, J.; Mace, C.R.; Phillips, S.T.; Carrilho, E.; Mirica, K.A.; Whitesides, G.M. Paper‐Based ELISA, Angew. Chem. Int. Ed. 2010, 49, 4881-4884. 35. Hsu, M.-Y.; Yang, C.-Y.; Hsu, W.-H.; Lin, K.-H.; Wang, C.-Y.; Shen, Y.-C.; Chen, Y.-C.; Chau, S.-F.; Tsai, H.-Y.; and Cheng, C.-M. Monitoring the VEGF level in aqueous humor of patients with ophthalmologically relevant diseases via ultrahigh sensitive paper-based ELISA, Biomaterials. 2014. 35. 3729-3735. 36. Hossain, M.F.; Park, J.Y. Plain to point network reduced graphene oxide - activated carbon composites decorated with platinum nanoparticles for urine glucose detection, Sci. Rep. 2016, 6, 21009, doi: 10.1038/srep21009. 37. Low, S.Y.; Loganathan, A.; Sivasangaran, S.; Samy, A.L. Association Between Urine Uric Acid Levels and Quality of Life in Academicians and Non-academicians in UTAR, Kampart, International Conference for Innovation in Biomedical Engineering and Life Sciences. 2016, pp. 118–121. 38. Krajewska, B. Application of chitin- and chitosan-based materials for enzyme immobilizations: a review, Enzyme and Microbial Technology. 2004, 35, 2-3, 126-139. 39. Jesionowski, T.; Zdarta, J.; Krajewska, B. Enzyme immobilization by adsorption: a review, Adsorption. 2014, 20, 5-6, 801-821.

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Figure Legends Figure 1. Schematic of the developed pattern-free membrane sensor.

Figure 2. Color focusing effect of chitosan and its derivatives (A). Color signals at six detection spots on membranes treated with a) nothing (control), b) 1 wt% MGC, c) 1 wt% GC, d) 1 wt% chitosan (141 kDa), and e) 3 wt% COL. Flow rate (mm/min) of the chitosan-derivative-treated NC membranes (B). Illustration of the color focusing effect based on the induced asymmetric flow on the COL-treated NC membranes (C). Error bars indicate standard deviation (n=3) for each material, and the scale bar is 1 cm.

Figure 3. Colorimetric detection of glucose and uric acid in DW. Plots of signal intensity vs. analyte concentration for glucose (A) and uric acid (B) in DW (n=5). The calibration curve was fitted to the Hill equation. Insets show log-log plots for 1–100 mg/dL glucose and 10–100 mg/dL uric acid. All signals were measured 3 min after the onset of the reaction.

Figure 4. Plots of measured concentration vs. spiked concentration of glucose (A) and uric acid (B) in human urine (n=5).

Figure 5. Stability of enzyme mixtures in the presence (black) and absence (red) of COL. The membrane sensors were exposed to the atmosphere under ambient conditions for 30 days, and color signals were checked after 0, 1, 2, 3, 4, 7, 14, and 30 days. This test was conducted using both glucose (A) and uric acid (B) with triplicates.

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Figure 2.

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Figure 3.

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Figure 4.

300

y = 1.05x + 1.53 250 R2 = 0.999 200 150 100 50 0 0

100 150 200 250 50 Spiked [glucose] (mg/dL)

Measured [uric acid] (mg/dL)

(B)

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Figure 5.

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w/ COL w/o COL

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