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GitHub - nthnn/PH4502C-Sensor: Arduino library for PH4502C sensor.

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Arduino library for PH4502C sensor. Contribute to nthnn/PH4502C-Sensor development by creating an account on GitHub.

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Abstract

The chloride ion selective electrode is used for a rapid, simple, and reliable determination of chloride ion in biological materials (blood serum, urine, fish, and plant tissues), food products (milk, beef extract, nutrient broth and orange, tomato, and grapefruit juices), soils, and waste water (industrial and municipal). The method consists of treating the samples with perchloric acid (pH 1) and potassium peroxydisulfate and determining the chloride content either by a calibration curve or by known addition or analyte addition, using the chloride ion selective electrode. Such sample treatment eliminates most of the interferences occurring in the samples, including iodide, complexing and reducing compounds, and macromolecular and surface-active species. The method is suitable for a wide range of chloride concentration, e.g., 5010 ppm Cl- in nutrient broth and 4890 ppm in beef extract and as low as 12 and 80 ppm in soil extracts.

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national center for biotechnology information

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Ion selective electrode for determination of chloride ion in biological materials, food products, soils and waste water - PubMed

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The chloride ion selective electrode is used for a rapid, simple, and reliable determination of chloride ion in biological materials (blood serum, urine, fish, and plant tissues), food products (milk, beef extract, nutrient broth and orange, tomato, and grapefruit juices), soils, and waste water (in ...

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Department of Electrochemistry, Korea Institute of Materials Science, Changwon, South Korea

School of Materials Science and Engineering, Pusan National University, Busan, South Korea

Department of Engineering Ceramic, Korea Institute of Materials Science, Changwon, South Korea

Advanced Materials Engineering, Korea University of Science and Technology, Daejeon, South Korea

Department of Materials Science and Engineering, Gachon University, Seongnam, South Korea

In this study, a novel chloride ion (Cl −) sensor based on Ag wire coated with an AgCl layer was fabricated using a gel-type internal electrolyte and a diatomite ceramic membrane, which played an important role in preventing electrolyte leakage from the ion-selective electrode. The sensing performance, including reversibility, response, recovery time, low detection limit, and the long-term stability, was systemically investigated in electrolytes with different Cl − contents. The as-fabricated Cl − sensor could detect Cl − from 1 to 500 mM KCl solution with good linearity. The best response and recovery time obtained for the optimized sensor were 0.5 and 0.1 s, respectively, for 10 mM KCl solution. An exposure period of over 60 days was used to evaluate the stability of the Cl − sensor in KCl solution. A relative error of 2% was observed between the initial and final response potentials. Further, a wireless sensing system based on Arduino was also investigated to measure the response potential of Cl − in an electrolyte. The sensor exhibited high reliability with a low standard error of measurement. This type of sensor is crucial for fabricating wireless Cl − sensors for applications in reinforced concrete structures along with favorable performances.

Introduction

Reinforced concrete (RC) plays a critical role in the construction industry, especially in the construction of buildings, bridges, and port facilities, because it has higher tensile and compressive strengths than other building materials ( Javadian et al., 2020). Steel is the most commonly used reinforcement material for RC structures because it has high tensile strength. However, steel reinforcements undergo corrosion because of chloride ions (Cl −), which is a main cause of the failure of RC structures ( Imounga et al., 2020). These ions promote the breakdown of the protective passive layer by decreasing the pH of the pore water, leading to the formation of corrosion pits in metals ( Li et al., 2017). Therefore, Cl − monitoring in RC structures is a key strategy to prevent the corrosion of reinforced steel. The current techniques for determining the local Cl − content in RC structures require destructive sampling methods such as drilling, grinding, and cutting. These methods are time-consuming and costly, and it is difficult to continuously obtain information at the same location over time ( RILEM TC 178-TMC, 2002; Femenias et al., 2016). Furthermore, it results in additional indirect costs due to road closures and traffic delays ( Torres-Luque et al., 2014). Therefore, several research groups are investigating the development of nondestructive in situ monitoring techniques to measure Cl − ingress in RC structures, which can afford fast measurements with high reliability and real-time ingress profiles without destroying the structures ( Abbas et al., 2013). Some existing nondestructive in situ monitoring methods include electrical resistivity measurement ( Corva et al., 2020), fiber optic sensing ( Bassil et al., 2020), potentiometry ( Gandía-Romero et al., 2016), and chronopotentiometry ( Abbas et al., 2013; Abbas, 2015). Among these, the chronopotentiometry technique based on an electrochemical method is employed for the indicative determination of Cl − content in infrastructures, the principle of which is described as follows. In a three-electrode system comprising a counter electrode (CE), reference electrode (RE), and working electrode (WE), a constant current is applied to the WE and the potential response is measured with respect to the RE ( Abbas, 2015; Dobbelaere et al., 2017; Raccichini et al., 2019). The as-fabricated Ag/AgCl ion-selective electrode (Ag/AgCl ISE) used in this method acts as the WE, which when embedded in the sensor device can be used to determine free Cl − concentration near the rebar without changing the surrounding environment ( Angst and Polder, 2014). This is because the electrode shows a Nernstian response to the variation in Cl − or Ag⁺ activity and displays excellent sensitivity ( Karthick et al., 2017). Here, the concrete has a strong alkalinity (pH 12.5-13.5). Therefore, the materials that are used as the electrolytes for RE should have alkali resistance, long-term chemical stability, and leak resistance characteristics. Conventional Ag/AgCl REs comprising a chamber and membrane based on glass are not suitable for the in situ measurements at the steel/concreate interface owing to their high alkalinities at the interface as well as fragility ( Jin et al., 2014). Furthermore, traditional Ag/AgCl REs employ liquid-type solution as an internal electrolyte, which shows the drawback of continuous exudation of KCl internal solution. KCl exudation is one of the most important parameters for the reliable performance of traditional Ag/AgCl REs ( Femenias et al., 2016). Solid-type electrolytes can decrease the rates of electrolyte loss and contamination and prolong the life span of REs. However, these suffer from low ionic conductivities, and the moisture content of the electrolyte varies with the change in concrete moisture, which causes the potential fluctuation of the RE, thus affecting the accuracy of corrosion monitoring in RC structures ( Tang et al., 2015). To address this issue, a gel-type internal electrolyte and diatomite ceramic membrane (DCM) were employed in this study to fabricate the Ag/AgCl ISE, which allowed the application of the electrode in highly alkaline environments, such as RC structures, without the exudation of the internal electrolyte. In particular, the DCM could effectively prevent the leakage of the inner solution, which involved the dissolution of the AgCl coating layer. The aim of the present study was to fabricate a novel Ag/AgCl-based ISE comprising an Ag wire coated with AgCl with a gel-type internal electrolyte and DCM. Cl − sensing characteristics included the response potential, reliability testing, response time, and recovery time of the sensor device, which were systemically determined in the electrolytes with different Cl − contents. Finally, wireless sensing based on Arduino Uno was performed using the as-fabricated Cl − sensor in KCl solution. This type of sensor is a key component for fabricating gel-type internal electrolyte-based Cl − sensors with favorable sensing performances.

Experimental

Synthesis of Diatomite Ceramic Membrane

Diatomite (Celite 499, Celite Korea Co. Ltd., South Korea) and kaolin (Sigma-Aldrich, USA), as a strength enhancer material, were used to fabricate diatomite-kaolin composite membrane for sensor applications. The average particle sizes of the as-received diatomite and kaolin were 12.7 and 1.5 μm, respectively. To increase the sinterability, the size of the diatomite particles was decreased to a powder-to-volume ratio of 2:1 by ball-milling for 24 h using alumina balls. Diatomite and kaolin (weight ratio of 9:1) were subjected to ball-milling for 3 h to afford a power-to-volume ratio of 0.5:1. Finally, the diatomite-kaolin composite was dry-pressed at 18.7 MPa with polyethylene glycol as a binder, followed by sintering at 1,200°C for 1 h. The as-fabricated diatomite-kaolin composites were cut into cylinder-shaped samples of 1-mm diameter and 1-mm height.

Synthesis of AgCl-Coated Ag Wire

An Ag wire with a diameter of 1 mm and purity of 99.99% was used to synthesize the AgCl-coated Ag wire. The experimental procedure for synthesizing the Ag/AgCl wire in this study is described as follows. First, prior to galvanostatic polarization (GP), the Ag wire was immersed in acetone (ACS reagent, >99.5%, Sigma-Aldrich, USA) with sonication for 10 min to remove the surface oil, followed by rinsing with distilled water. Next, the Ag wire was ground in one direction using a silicon-carbide abrasive paper (#2000 grit) to remove the native oxide layer and then dipped in acetone and 5% nitric acid (HNO ₃; 60%, Sigma-Aldrich, USA) solution for 10 min to remove the impurities. Thereafter, the Ag wire was cleaned in ethanol ultrasonically and dried in air. For GP, the WE terminal of the potentiostat was connected to the Pt-mesh and the CE terminal was connected to a pretreated Ag wire. After GP, an immersion process was employed to synthesize the AgCl layer on the surface of an Ag segment. The entire process was performed in 1 M hydrochloric acid (HCl; ACS reagent, 37%, Sigma-Aldrich, USA) at room temperature.

Gel-Type Internal Electrolyte

The synthesis of the gel-type internal electrolyte is described as follows. First, 3.5 mol/L potassium chloride (KCl; 99.0%, Sigma-Aldrich, USA) solution was prepared and heated at 60°C. Then, 8 ml of glycerol (>99.5%, Sigma-Aldrich, USA) was poured into the KCl solution, which played an important role in preventing the high-density gelation of internal electrolyte. Finally, 5 wt.% of hydroxyethyl cellulose (HEC, 200-400 mPa·s, DaeJung Co., Ltd., South Korea) was slowly added into the heated KCl solution (100 ml KCl solution with 5 g HEC) with constant stirring at 150 rpm, affording the HEC-gelled electrolyte. The conductivity of the gel-type electrolyte was 226 mS/cm at 5 wt.% HEC and that of 3.5 M KCl solution was 294.8 mS/cm ( Kim et al., 2017). This indicated that the gel-type electrolyte could decrease the migrating rate of ions, which further decreased the rates of electrolyte loss and exterior ions entering into the internal electrolyte. Furthermore, HEC showed good water absorptivity. Using the HEC-based gel-type electrolyte decreased the rate of moisture loss, thus prolonging the life time of RE in the RC structure.

Fabrication of Ag/AgCl Ion-Selective Electrode

In this study, polyvinyl chloride (PVC) was used as the housing material because PVC is abrasion resistant and chemically resistant to many acids, bases, and corrosives, with good mechanical strength and toughness. First, a hole with a dimeter of 1 mm was melted at one end of each PVC chamber using a hot iron. Cylinder-shaped DCMs were then inserted into the holes. Next, the as-synthesized Ag/AgCl wire was inserted into each PVC chamber, leaving a space between the wire and membrane. Thereafter, 8 ml of the gel-type internal electrolyte was slowly poured into each PVC chamber, in which the Ag/AgCl wires were inserted. Finally, the top of each PVC chamber was sealed using a rubber cap and insulating tape. The as-fabricated Ag/AgCl ISE was stored in 1 M KCl solution.

Characterization

The crystal structure and crystallinity of the as-synthesized Ag/AgCl wire obtained by GP and immersion were determined using X-ray diffraction (XRD; Ultima IV CuK, Rigaku Corporation, Tokyo, Japan). X-ray photoelectron spectroscopy (XPS; Kratos Analytical Ltd., Manchester, UK) was employed to measure the changes in the surface composition of the Ag/AgCl wire. The morphology and composition were analyzed using field-emission scanning electron microscopy (FE-SEM; Hitachi S-4800, Hitachi Ltd., Tokyo, Japan) coupled with energy-dispersive X-ray spectroscopy (EDS; Oxford INKA X-Act, Oxford Instruments, Abingdon, UK). Cl − measurement with the as-fabricated Ag/AgCl electrode was performed using a potentiostat (BioLogic, EC-Lab instrument, Seyssinet-Pariset, France) in electrolytes containing 0.5 M KNO ₃ and 10-600 mM KCl. The WE terminal of the potentiostat was connected to the as-fabricated Ag/AgCl ISE. The CE terminal was connected to the Pt-mesh, and the RE terminal was connected to the commercial Ag/AgCl RE. When a current pulse was applied between the WE and CE, Cl − species near the WE were consumed, locally depleting the Cl − near the surface of the WE, leading to a concentration gradient that resulted in a potential difference at the WE with respect to the RE ( Bard and Faulkner, 2001). A schematic of the sensing measurement setup is shown in Figure 1.

Figure 1. Schematic of the chloride ion sensor comprising an Ag/AgCl reference electrode, Pt-counter electrode, and Ag/AgCl ion-selective electrode (Ag/AgCl ISE) based on a gel-type internal electrolyte and diatomite ceramic membrane.

Results and Discussion

As most commercial REs use a liquid-type solution as an internal electrolyte, which can leak under various pressures and salinity conditions, periodic replacement and maintenance are necessary to retain the electrode performance ( Kim et al., 2017). Compared to the liquid-filled electrodes, gel-type internal electrolytes have numerous advantages. It is possible to take measurements with various pressure changes using gel-type electrolytes, and these are heat resistant up to 121°C ( Guth et al., 2009; Inzelt et al., 2013). Various water-soluble polymers such as polyvinyl alcohol, methylcellulose, and HEC can be used as gel-type internal electrolytes owing to their viscous characteristics. These play a key role in preventing electrolyte leakage from the internal to external electrodes. Among these polymers, HEC is a nonionic cellulose, the aqueous solutions of which are stable at pH 2-12 with the suitable solution viscosities and gelling states at normal temperatures. HEC also exhibits a high salt tolerance and long-term stability over a wide pH range (3.5-11). For this reason, HEC can be applied to gel-type internal electrolytes for application in coastal structures. Figure 2A shows the relationship between the HEC content, viscosity, and ion conductivity. The results reveal that the viscosity of each electrolyte increases upon the addition of HEC, and a maximum value is obtained at 8 wt.% HEC. The viscosity of the electrolyte with >10 wt.% HEC cannot be measured with the viscometer owing to its limited detection range (1.5-4,800 cP). In contrast, the ion conductivity of the electrolyte decreases with an increase in the HEC content because of the limitation of ion mobilities. The time dependency of ion conductivity for the as-synthesized gel-type electrolytes is shown in Figure 2B. The ion conductivities of all internal electrolytes with different HEC contents stabilize after initial measurements (1-3 days), except with high HEC contents (12 and 15 wt.%) owing to ion transfer limitations. The gel-type electrolyte with 5 wt.% HEC shows higher ion conductivity and appropriate viscosity compared with those of other samples. Based on these results, the electrolyte with 5 wt.% HEC was selected as the optimal internal electrolyte for fabricating the Ag/AgCl ISE. Figure 2C shows the images of the gel-type internal electrolyte, acquired 3 s after electrolyte synthesis. The data show that gelling progresses with an increase in the HEC content. In particular, ideal gelling characteristics are observed with 12 and 15 wt.% HEC. The membrane is a critical part of the electrode. It not only ensures the transmission of electrolyte but also decreases it and determines the lifetime of the electrode. The FE-SEM images of two types of membranes employed are shown in Figures 3a,b. The particle size of the DCM is smaller than that of the Vycor glass membrane (VGM); the average particle sizes are 1.5 and 5.4 μm, respectively. The small particle size of DCM with a dense structure can minimize the leakage of internal electrolyte from the electrode, which can lead to the stability of the electrode in RC structures. Additionally, the morphology of VGM shows a randomly stacked plate-type structure with large pore sizes, which leads to the leakage of the internal electrolyte. Figure 3c shows the time-dependent leakage of the internal electrolyte from the PVC housing with four types of membranes. After 1 month, electrolyte leakage is observed in all membranes, which decreases with an increase in the ceramic content. However, the two types of DCMs show lower leakages compared to those of VGMs. These results are in good agreement with the corresponding FE-SEM data. In this study, GP and immersion were used to synthesize AgCl-coated Ag wire to remove undesired impurities from the Ag wire surface and increase the active sites using small AgCl particles for enhancing the interaction between Cl − and the as-synthesized Ag/AgCl wire by introducing fine roughness. For this process, the WE terminal of the potentiostat was connected to the Pt-mesh and the CE terminal was connected to the Ag wire. In this case, the immersion process after GP is important for the formation of the AgCl layer on the Ag wire surface. The suggested formation mechanism of the AgCl layer on the Ag wire during GP and immersion is described by reactions (1) and (2).

Figure 2. Relationship between (A) hydroxyethyl cellulose (HEC; wt.%), viscosity, and ion conductivity, (B) time-dependent ion conductivity, and (C) optical images of gel-type electrolyte with different HEC contents.

Figure 3. Field-emission scanning electron microscopy (FE-SEM) images of two types of membranes: (a) Vycor glass membrane and (b) as-synthesized diatomite ceramic membrane. (c) Internal electrolyte leakage data for the membranes as a function of time.

When a current pulse is applied, chlorine (Cl ₂) is generated on the Pt-mesh (anode) and it reacts with water, resulting in the formation of a mixture of hypochlorous acid (HOCl) and HCl. In other words, the HOCl content in the electrolyte increases with an increase in the GP time. In this reaction, Cl ₂ is oxidized as well as reduced (Cl + H ₂O → H⁺Cl − + H⁺O 2−Cl⁺); such reactions are called auto oxidation-reduction (redox) reactions (disproportionation reaction) ( Gu and Bennion, 1977; Guo et al., 2014) and involve the reduction and oxidation reactions of the same substance (reactant). In HOCl, the oxidation state of chlorine is +1, showing that it has a high affinity for electrons. Therefore, HOCl oxidizes Ag to AgCl. After GP, the immersion process is immediately carried out in the same electrolyte without applying a current pulse. When the applied current pulse is removed, the as-generated HOCl reacts with the Ag wire (cathode), resulting in the generation of AgCl and AgOH on the Ag wire surface. Figure 4 shows the FE-SEM images and elemental compositions of Ag coated with AgCl with different immersion times. The applied current density and GP time for all samples were fixed at 30 mA/cm ² and 2 h, respectively. As a result, the number and sizes of the as-synthesized AgCl particles, as well as the chloride content on the surface of the Ag wire, increase with an increase in immersion time. The AgCl particles appear to be relatively uniform with round shapes of <1-μm sizes and a distinct boundary between these particles in all conditions. The boundaries between AgCl particles and pore channels through the layer, known as micro-channels, are the main pathways for the transport of ions within the AgCl layer. As the AgCl layer thickness increases, the ion transport becomes limited due to micro-channel confinement ( Pemberton and Girand, 1987; Neary and Parkin, 2015; Pargar et al., 2018). For this reason, it is important to optimize the applied current density and treatment time during anodization. Additionally, the immersion time is a more important parameter in the synthesis of AgCl layer on Ag wire in comparison to the current density and GP time. Cl − sensing with the as-fabricated Ag/AgCl ISE was performed to confirm the effect of immersion time and determine the optimal conditions for Cl − sensing by the sensor. The length of the Ag/AgCl wire and distance to the diatomite membrane were fixed at 3 and 1 cm, respectively. The Cl − response was measured using a three-electrode system with the as-fabricated Ag/AgCl ISE, Pt-mesh, and a commercially available Ag/AgCl RE at ambient atmosphere (24°C) and humidity of 50%. The time periods for each applied current pulse and no-pulse were fixed at 5 and 20 s, respectively. The data for Cl − sensing by the sensor in an electrolyte containing 300 mM KCl and 0.5 mM KNO ₃ as a function of immersion time using an applied current pulse of 1 mA are shown in Figure 5. For an immersion time of 1 s, the initial potential difference before applying the current pulse is ca. −0.018 V, which is ideally not zero. This result is attributed to an insufficient AgCl layer on the surface of the Ag wire, which results in different half-cell potentials with respect to the Ag/AgCl RE. Furthermore, when the current pulse is applied, the initial potential difference for an immersion time of 1 s is higher and shows a non-stable sensing signal as compared to those observed with other conditions. This is due to the formation of AgCl salt on the surface of the Ag wire during the current pulse application, resulting in the depletion of Cl − near the WE and generation of a large potential difference. An immersion time of 120 s shows a lower potential difference than those obtained with 1, 60, and 90 s. As the immersion time increases, the AgCl content on Ag wire increases, while the exposed Ag surface decreases. This results in a lower depletion of Cl − near the WE during Cl − sensing, resulting in a low potential difference between the Ag/AgCl ISE and Ag/AgCl RE. In other words, the concentration gradient of Cl − at an immersion time of 120 s is small owing to the large amount of AgCl on the Ag wire. This is in good agreement with the FE-SEM data shown in Figure 4. Therefore, an immersion time of 120 s was selected in this study as the optimal condition for synthesizing the Ag/AgCl ISE. Figure 6 shows the XRD data of the Ag/AgCl wire synthesized employing an immersion time of 120 s. It clearly confirms that the wire is composed of cubic AgCl (JCPDS No. 31-1238) and metallic Ag (JCPDS No. 04-0783). The diffraction peaks at 27.86°, 32.23°, 46.24°, 54.83°, 57.48°, 67.46°, and 76.59° correspond to the (111), (200), (220), (311), (221), (400), and (420) planes, respectively, of the Ag/AgCl layer. The peaks at 38.08°, 44.29°, 64.50°, and 77.89° are attributed to the diffraction of the (111), (200), (220), and (311) planes, respectively, of metallic Ag ( Liu et al., 2019). The elemental compositions and surface chemical states of the as-synthesized Ag/AgCl wire were analyzed using XPS ( Figure 7). Figure 7A shows the XPS survey spectra, indicating that Ag and Cl are the main components, and trace amounts of C and O are attributed to the adventitious hydrocarbons from the XPS instrument. The peaks at 368.04 and 374.02 eV correspond to the binding energies of Ag3d 5/2 and Ag3d 3/2, respectively, of Ag⁺ in AgCl ( Figure 7B) ( Liu et al., 2019). The two peaks at 197.89 and 199.51 eV with a doublet separation of 1.7 eV in the Cl2p spectrum correspond to the binding energies of Cl2p 3/2 and Cl2p 1/2, respectively ( Figure 7C) ( Wang et al., 2012). The results obtained from the XPS data confirm the presence of Ag and AgCl in the wire, which are in complete agreement with the XRD data. High-resolution O1s spectra of the Ag/AgCl wire include three peaks centered at 530.0, 532.3, and 533.8 eV. The latter two peaks are attributed to the organic contamination absorbed on the surface of the Ag wire, whereas the first peak located at a low binding energy is associated with Ag-O ( Figure 7D) ( Wang et al., 2012). The elemental atomic percentages derived from the XPS survey spectra are also shown in Table 1. These data are in good agreement with the EDS data shown in Figure 4d. To optimize the length of the Ag/AgCl wire and distance between the Ag/AgCl wire and diatomite membrane, Cl − sensing by the as-fabricated Ag/AgCl ISE was investigated as a function of the distance at a fixed Ag/AgCl wire length of 4 cm ( Figure 8A). As shown, the potential difference decreases with an increase in the distance between the Ag/AgCl wire and DCM. This is probably due to the low reaction speed with an increase in the distance between the Ag/AgCl wire and Cl − in the electrolyte upon the application of a current pulse. Next, Cl − sensing was analyzed as a function of the Ag/AgCl wire length at a fixed distance of 1 cm ( Figure 8B). As observed, the Ag/AgCl wire with a length of 4 cm and distance of 1 cm shows the lowest potential difference. Based on the above experimental results, an Ag/AgCl length of 4 cm and distance of 1 cm to the membrane were selected to fabricate the Cl − sensor. The potential difference of the Cl − sensor as a function of Cl − concentration with an identical current pulse of 1 mA is shown in Figure 9A. Prior to the application of the current pulse, the potential difference is nearly zero for all Cl − concentrations because of the nearly identical half-cell potential between the as-fabricated Ag/AgCl ISE (WE) and commercially available Ag/AgCl RE. Figure 9B shows the magnification of the fifth data set (enclosed in red rectangle) in Figure 9A. With an increase in the amount of Cl − in the electrolyte, the potential difference between the Ag/AgCl ISE and Ag/AgCl RE decreases. This is due to ion depletion near the WE in the electrolyte with a high Cl − concentration compared to the low-concentration electrolyte. The response and recovery times were calculated using the time required for 90% change in the potential difference upon the supply and removal of Cl −, respectively ( Kwon et al., 2016). The average response and recovery times of the Cl − sensor were 0.5 and 0.1 s, respectively. When no kinetic restrictions are applied, the dynamic equilibrium between metallic silver (Ag ⁰) and Ag⁺ can be established in a short duration. This feature makes the noble Ag metal prone to "corrosion" in environments with aggressive ions such as Cl − ( Pargar et al., 2017). As shown in Figure 9C, a good linear fit curve is obtained, and the correlation coefficient ( R ²) of the Cl − sensor is 0.9712. As R ² value approaches 1, a strong relationship between the sensing signal and Cl − concentration is observed ( Kim et al., 2019). The long-term stability of the Cl − sensor is important for ensuring excellent and reliable sensing performance. The potential difference of the sensor was measured as a function of time over a period of 60 days at 5-day intervals using 100 mM KCl solution ( Figure 10). The relative error between the initial and final signals was 2%, indicating that the potential response was quite stable during the test period. A wireless Cl − monitoring system based on Arduino Uno was developed in this study for the real-time monitoring of Cl − in the electrolyte comprising 1 mM KCl and 0.5 M KNO ₃. Schematic and real images of the wireless sensing system are shown in Figures 11, 12a,b, respectively. The wireless Cl − sensing system is composed of individual electronic components including the micro controller unit, voltage to ampere converter (W103), clock (DS 3231), micro SD module, as-fabricated Cl − sensor, circuit device for the wireless sensing measurement of the contained single channel readout, Arduino Uno, and analysis software (visual basic). Wireless Cl − sensing characterization of the sensor was performed to measure the repeated potential responses in Cl −-containing solution. The potential difference for the Cl − sensor with a regular current pulse of 1 mA is shown in Figure 12c. The 10 reversible cycles of the response curve indicate the stable and repeatable response characteristics with a small relative error (<4%) between the average initial and final signals. Linear fit curves of the Cl − sensor potential response to the merged experimental data of wireless and non-wireless sensing are shown in Figure 12d, the R ² of the Cl − sensor is 0.9761. It was found that the wireless sensing of Cl − sensor is reliable data. A movie for the real-time wireless Cl − sensing with an applied current pulse of 1 mA in 1 mM KCl and 0.5 M KNO ₃ is shown in Supplementary Figure 1.

Figure 4. Field-emission scanning electron microscopy (FE-SEM) images and elemental compositions of the AgCl-coated Ag wire with different immersion times: (a) 1 s, (b) 60 s, (c) 90 s, and (d) 120 s. The current density and treatment time for the galvanostatic polarization of all samples are fixed at 30 mA/cm ² and 2 h, respectively.

Figure 5. Potential difference of the chloride ion sensor as a function of immersion time. The electrolyte comprises 300 mM KCl and 0.5 M KNO ₃. The length of the Ag/AgCl wire and the distance between the Ag/AgCl wire and diatomite ceramic membrane are fixed at 3 and 1 cm, respectively.

Figure 6. X-ray diffraction patterns of the Ag/AgCl wire synthesized via galvanostatic polarization and immersion (120 s) in 1 M hydrochloric acid. Current density and treatment time for the galvanostatic polarization are fixed at 30 mA/cm ² and 2 h, respectively.

Figure 7. (A) X-ray photoelectron survey spectra and high-resolution X-ray photoelectron; (B) Ag3d, (C) Cl2p, and (D) O1s spectra for the as-synthesized Ag/AgCl wire.

Table 1. Atomic percentage of Ag/AgCl wire determined from X-ray photoelectron spectroscopy (XPS) survey spectra after galvanostatic polarization and immersion in 1 M hydrochloric acid.

Figure 8. Potential difference of the chloride ion sensor as a function of (A) the distance (D) between the Ag/AgCl wire and diatomite ceramic membrane with a fixed wire length (L) of 4 cm and (B) various wire lengths with a fixed distance between the Ag/AgCl wire and diatomite membrane. The applied current pulse for chloride sensing is fixed at 1 mA, and the electrolyte comprises 300 mM KCl and 0.5 M KNO ₃.

Figure 9. (A) Potential difference of the chloride ion sensor (D: 1 cm, L: 4 cm Ag/AgCl) as a function of the chloride ion content of 10-600 mM KCl and (B) magnified image of the fifth bar (enclosed in red rectangle) in (A). (C) Linear fitting of the chloride ion sensor potential with the experimental data. The applied current pulse is fixed at 1 mA.

Figure 10. Long-term stability of the chloride ion sensor in the electrolyte comprising 100 mM KCl and 0.5 M KNO ₃.

Figure 11. Schematic of the wireless chloride ion sensing system based on Arduino Uno.

Figure 12. (a) Wireless chloride ion sensing system based on Arduino Uno and (b) magnified image of the wireless sensor module. (c) Potential difference for the wireless chloride ion sensor with a regular current pulse of 1 mA in the electrolyte comprising 1 mM KCl and 0.5 M KNO ₃. (d) Linear fitting of the chloride ion sensor potential response to the merged experimental data of wireless and non-wireless sensing.

Conclusion

A novel Cl − sensor based on Ag wire coated with an AgCl layer composed of a gel-type internal electrolyte and DCM was manufactured via GP and immersion in 1 M HCl solution. Both the DCM and the gel-type electrolyte played essential roles in preventing electrolyte leakage from the ion-selective electrode. The AgCl layer was dense and uniformly coated on the surface of the Ag wire; the AgCl particles were relatively uniform with round shapes and a size of <1 μm. The size and content of AgCl increased with an increase in the immersion time at a fixed current density of 30 mA/cm ² and GP time of 2 h. The length of the Ag/AgCl wire and its distance to the membrane were optimized to minimize the potential difference. Consequently, the sensor could detect Cl − in 10-500 mM Cl − concentration with good linearity; moreover, the sensor exhibits good long-term stability (about 2 months). A wireless sensing system based on Arduino was investigated to measure the Cl − response in an electrolyte containing 1 mM KCl and 0.5 M KNO ₃. A small relative error of <4% was obtained between the initial and final signals. A linearity of 0.9712 was obtained from the experimental data of non-wireless Cl − sensing. In conclusion, this study presented a practical method for inexpensive and scalable fabrication of wireless Ag/AgCl-based Cl − sensors for applications in RC structures with good reliability.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material, further inquiries can be directed to the corresponding author/s.

Author Contributions

SK and GP conducted the most experiment and wrote the paper. H-JA, BY, and I-HS made some parts and did analysis. K-HL made Arduino set-up. J-HL and J-YL organized the experiment and revised the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by the Global Frontier Program through the Global Frontier Hybrid Interface Materials (GFHIM) project (Grant 2013M3A6B1078870) of the National Research Foundation of Korea (NRF), which was funded by the Ministry of Science, ICT, and the Future Planning and Technology Innovation Program (20002694) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea), and the Fundamental Research Program of the Korea Institute of Materials Science (PNK6740).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fchem.2020.574986/full#supplementary-material

Video S1. The movie of wireless chloride ion sensing based on Arduino Uno.

References

Inzelt, G., Lewenstam, A., and Scholz, F. (2013). Handbook of Reference Electrodes. Berlin: Springer.
Google Scholar

# data[].attributes.attachment_link.title
Facial Fabrication and Characterization of Novel Ag/AgCl Chloride Ion Sensor Based on Gel-Type Electrolyte

# data[].attributes.attachment_link.description
In this study, a novel chloride ion (Cl−) sensor based on Ag wire coated with an AgCl layer was fabricated using a gel-type internal electrolyte and a diatomite ceramic membrane, which played an important role in preventing electrolyte leakage from the ion-selective electrode.

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1. Introduction

In many parts of the world, rapid industrialization and population growth in urban areas have increased the water demand and gradually worsened the water quality owing to the depletion of the water volumes in rivers. The dams and reservoirs, which have been constructed to retain and manage such water resources efficiently, slow down the cycling of the water volumes in the river systems; hence, the pollutants cannot be fully removed, and the water quality degenerates. In particular, the excessive inflow of nutrient salts such as nitrates and phosphates causes multiple problems such as eutrophication, which results in the growth of algal blooms and reduced water availability. Hence, managing these pollutants requires special government-driven management programs [ 1, 2].

Ion-selective electrodes (ISEs) have been used for the determination of ion concentrations in a wide range of clinical, environmental, and food analysis applications, thereby extending the application range of potentiometry. The successful application of ISE technology in real-time sensing of nitrate requires the continuous measurement of individual nutrient concentrations with acceptable sensitivity [ 5, 6]. However, one of the major deficiencies of ISE technology, compared to standard spectroscopy-based analyzers, is the potentially limited measurement accuracy due to the signal drift and loss of sensitivity over time [ 7]. Indeed, the signal drift can be the primary factor affecting the determination accuracy for nutrient concentrations when an ISE-based on-site ion monitoring system that requires frequent immersion of the electrodes into a solution is used [ 8]. The reasons for the signal drift are very complex; it is typically caused by a change in the membrane of an electrode and leaching of the inner solution. The contact between the membrane and electrode body may become obscured owing to the deposition of organic matter; in addition, the contact may be lost owing to surface wear caused by mechanical stress; these are the main causes of changes in the membrane [ 9]. Therefore, a sensor must be replaced when it shows a decrease in performance or loses its function, which can result in data loss. However, the previously mentioned factors make it difficult to predict the status of the electrode and hence the replacement time on site.

Researchers are actively studying sensor-based monitoring technology for diagnosing the state of other systems or for monitoring system errors [ 10, 11]. According to relevant reports, the most important aspect is to develop an appropriate algorithm for fault detection. In turn, the majority of studies of fault detection algorithms have adopted methods that incorporate a diagnostic model [ 12]. Bertrand-Krajewski et al. Referenxe [ 13] proposed a sensor for an uncertainty index and applied a law of propagation of uncertainties based on a 1st-order Taylor series function to a sensor diagnosis system. Ramanathan et al. [ 14] presented five rule-based fault detection methods for evaluating the ISE performance and demonstrated that it can be calibrated to diagnose faults. In addition, Jung et al. [ 6] and Cho et al. [ 15] proposed a two-point normalization method for correcting the sensitivity change and signal drift during the long-term use of an array of multiple ISEs. Moreover, the potential advantages of using AI-based techniques in condition monitoring and diagnosis have been widely reported [ 16, 17].

Water quality monitoring systems with ISEs are advantageous for long-distance communication. In particular, a communication system that exchanges information to respond immediately to sensor failures on-site is required. Many researchers have reported that the previously mentioned diagnostic systems create synergies when combined with Internet-of-Things (IoT) technology; in addition, IoT has been applied in many recent sensing technology studies [ 18, 19, 20]. These technologies primarily form part of a system such as an automatic on-site water quality measurement, a data logging, and wireless communication system. This system type is particularly useful because it allows the user to monitor the conditions of the data and system at any time and in any place [ 21, 22, 23]. Recently, an intelligent diagnostic algorithm for discriminating abnormal power supply in IoT-based buildings was proposed [ 24]. In addition, a deep learning-based approach as an intelligent fault diagnosis algorithm has been recently attempted in image-based systems, and a high probability has been reported [ 25]. Despite their extensive use and great success, the focus is on the analysis of environment field monitoring data, and these diagnostic algorithms rarely applied to the water quality monitoring system. Thus, if IoT technology is used to monitor continuously the environmental information and condition of the measuring instruments and sensors, any sudden degradation of the ion sensor can be effectively managed.

In this study, an IoT-based nitrate monitoring system was developed and evaluated in an on-site application for stable water quality monitoring. In addition, the results of a laboratory test and past performance were used to propose a self-diagnostic index (SDI) for the detection of faults of the nitrate measurement sensor. The developed system reacts to sudden performance problems and notifies the user of sensor failures in real time; thus, data loss can be prevented, and the reliability of the monitoring system is ensured.

2. Materials and Methods

2.1. Configuration of IoT-Based Nitrate Measurement System

The sensor was designed based on the materials and method proposed by previous research [ 5, 6]. A polyvinyl chloride-based membrane was used to fabricate a nitrate measurement electrode. The membrane was prepared using the compositions of tetradodecylammonium nitrate (TDDA), 2-nitrophenyl octylether (NPOE), and high-molecular-weight polyvinyl chloride (PVC), as shown in the Table 1. The NO ₃−− ISEs were filled with an internal solution of 0.01 M NaNO ₃ + 0.01 M NaCl. An Ag/AgCl electrode prepared by coating silver wire (99%) with a diameter of 1 mm with Ag/AgCl ink (model 01164, ALS Co., Tokyo, Japan) was immersed as the inner reference electrode. A double junction electrode (Orion 90-02, Thermo Fisher, Waltham, Mass.) was used as the reference electrode.

In addition, an automatic system was configured to incorporate a metering pump (SR10/30, ASF THOMAS, Germany) and a sampling pump (SR10/60, ASF THOMAS, Fürstenfeldbruck, Germany) for the measurement of two standard solutions; a valve was integrated to exhaust the samples. As shown in Figure 1, the proposed system was fully configured with an open source-based IoT system. An Arduino Due board (Arduino LLC, MA, USA) was used as a low-cost microcontroller to log the signals of the electrode and deliver messages to a server via a WizFi-250 module (WIZnet Co., Ltd., Seongnam, Korea) for wireless communication. The Arduino Due has a driving voltage of 3.3 VDC and an analog-to-digital converter (ADC) resolution of 12 bits; it is suitable for multiple-channel analog collection and digital signal processing. In the previous study [ 5], a water quality monitoring mainboard, an ADC chip, and a relay were connected to a filter through a jump line and a breadboard; however, the signal stability was affected by measurement noise. Accordingly, a printed circuit board (PCB) to which the signal filter, Arduino Due board, and WizFi module were attached and six additional relays were installed to facilitate the on/off control of the monitoring system. The Arduino board connects WIFI to the WizFi module through SPI communication, publishes MQTT messages, and then transmits raw data to the Raspberry Pi board through RS232 serial communication to store the data separately.

The developed system communicates with the cloud server by using the message queuing telemetry transport (MQTT) protocol and provides monitoring information through the developed web-based monitoring page. A web-based nitrate monitoring page was constructed and tested to monitor measurement data on the web. As shown in Figure 1, the message queuing telemetry transport (MQTT) protocol was implemented through the Arduino Due-embedded board, thereby enabling it to recognize the broker server via the wireless network and to publish data from the three nitrate electrodes [ 26]. Moreover, the web-based nitrate monitoring page was constructed and tested to monitor measurement data on the web. The data are published on the broker's own webpage and sent on to the user. The web page was hosted using a commercial cloud server (Naver Cloud Platform, Naver, Seongnam, Korea) and the server configuration was Node.JS, and Vue.JS was used for building a front-end UI.

The upper and lower parts of the PCB are schematically shown in Figure 2A,B, respectively. The Arduino board and WizFi module were connected to the lower part to realize the installation in Figure 2C. The PCB signal was filtered with a Sallen-Key second-order filter to collect stably the electrode signals ( Figure 3). This filter has a 10.33 Hz low-pass cutoff frequency; it can amplify signals twice and remove inter-channel disturbance. To display the current concentrations on the LCD panel, a Raspberry Pi board (Raspberry Pi Foundation, UK) was added to the monitoring system. The Raspberry Pi board was installed to check the LCD screen in the field, and additionally, in preparation for problems with internet connection, raw data was stored using it as a backup DB. In addition, a separate database (DB) was constructed to collect raw data, and a self-diagnostic algorithm for the electrodes (explained below) was installed for calculation.

The overall sequence of the developed water nitrate monitoring system with self-diagnostic function is shown in Figure 4. The sequence includes a two-point normalization setup with rinsing, sampling pumps, drainage via a solenoid valve, and measurements with the ISEs, while the data communication processes with the MQTT server and fault diagnosis are presented. The two-point normalization method consisting of a sensitivity adjustment followed by an offset adjustment was used to minimize the potential drift. Two different concentrations of 10 and 100 mg L −1 were used as known standard solutions of low and high concentrations, respectively, to determine the slope and offset values prior to sample measurement. Through this process, the signal drift, sensitivity, and deviation values were obtained for each electrode required for the self-test function.

2.2. Self-Diagnostic Algorithm for ISE

Selection of SDI for Electrode

The change in sensitivity is an essential factor for assessing the performance of the ISE [ 14]. In addition, the continuous data from sensor signals must be analyzed to determine whether there is a signal drift (which is also an important index for assessing the sensor performance). In this study, the current status of the nitrate electrodes was quantitatively checked using three indicators, i.e., electrode drift index (S ₁), sensitivity change index (S ₂), and estimated value change index between multiple electrodes (S ₃):

The SDI (%) is presented in Equation (4):

The drift index (S ₁, Equation (1)) represents the drift from the standard electromotive force (in mV) of the ISE, indicating a change in EMF values determined via three identical and consecutive measurements of a standard solution before the use of the electrode in the practical measurements. The sensitivity index (S ₂, Equation (2)) reflects the change in the sensitivity of the electrode (in mV) due to the presence of foreign substances or bubbles on the membrane surface; it is determined with standard NO ₃− solutions of two concentrations (i.e., 10 and 100 ppm (mg L −1)) before the practical measurements. Finally, the deviation from other electrodes introduced when predicting the concentration of a sample is represented by S ₃ (Equation (3)). In this study, it is the difference between the average prediction result of multiple electrodes and each individual prediction. A laboratory test was performed to select appropriate values for each index to determine the replacement time of the electrode.

2.3. Experimental Procedure

2.3.1. Laboratory Test for Effectiveness of Self-Diagnostic Algorithm

Before the field installation of the developed online water quality measurement system, the performance of the system and effectiveness of the self-diagnostic algorithm were evaluated in a laboratory test in which samples of tap water from Seoul were repeatedly tested at 8 h intervals for one month. Before each series of measurements, two standard NO ₃− solutions with different concentrations (10 and 100 ppm (mg L −1)) were used to determine the sensitivity and drift of the electrode with respect to the accurate nitrate concentrations of the samples identified by a commercial soil and water quality analysis center (National Instrumentation for Environmental Management (NICEM), Seoul, Korea).

2.3.2. Field Verification Experiment

As shown in Figure 5, a field monitoring experiment was conducted to operate and evaluate the self-diagnostic functions of the developed online water nitrate monitoring system and ISE. The test site is schematically shown in Figure 5A; the water source is indicated by the nearby mountainous area. The location (37°47′47.1″ North and 128°51′24.5″ East) is presented in the satellite image in Figure 5B. In addition, because the water quality of the test site was expected to change owing to waste water emissions from the nearby residential and agricultural areas, the developed online stationary monitoring system was used to monitor the nitrate concentration ( Figure 5C). The monitoring experiments were conducted four times at 6 h intervals each day; during each experiment, 16 samples were collected and sent to the NICEM laboratory to determine actual concentrations in the samples. The three nitrate ISEs were installed in an array ( Figure 5 D), and their mean value was sent to the online webpage. Subsequently, the self-diagnostic algorithm identified the replacement time corresponding to the electrode values, which was determined with the index that had been validated in the previously mentioned laboratory test.

2.3.3. Evaluation of Self-Diagnostic Methods

The precision, recall, and accuracy of the setup were investigated in this study to determine the effectiveness of the SDI. The factors that evaluate a model can eventually be defined as the relationship between the model's predicted and actual correct label. The result is classified into "True and False"; thus, the classification model outputs "True" and "False", thereby dividing the results into a 2 × 2 matrix ( Table 2) [ 27].

The precision () is determined as the conditional probability that represents the classifier's ability to place a value into the correct category as opposed to placing all values (correct or incorrect) into the same category:

The recall () is defined as the probability that, if the model should be assigned the label "True" under the actual category in True, the result is considered "true positive". The recall is presented in Equation (6):

Although accuracy is commonly used to evaluate categorization techniques, this measure is much less sensitive to variations in the number of correct decisions than precision and recall. The accuracy () is defined as follows:

Thus, the electrode condition was classified into three states (normal, management, and replacement); "replacement" and "management" were categorized as "True", and "normal" was categorized as "False".

3. Results

3.1. Lab Test Results Following Application of SDI

The effectiveness of the developed online monitoring system and self-diagnostic algorithm was tested by continuously measuring a sample in the laboratory setting for three days. The repeated measurements of the samples with an NO ₃− concentration of approximately 17.85 mg/L according to the standard analysis resulted in an average NO ₃− concentration of 15.51 ± 3.38 mg/L, which is similar to that of the real samples. The results are presented in Figure 6; the predicted NO ₃− concentrations of each electrode are plotted in Figure 6A, and the corresponding SDIs in Figure 6B-D are the SDIs of each electrode. In the test, an SDI of 70 or higher indicated the optimal time for the replacement of the electrode. In the case of NO ₃− electrode 1 (filled circles, Figure 6A and bar chart in Figure 6B), the SDI exceeded 60 on Day 21 and 70 on Day 24, at which point the electrode was replaced. In the case of NO ₃− electrode 2 (open circles, Figure 6A and bar chart in Figure 6C), the SDI reached 68 on Day 9. On that day, the bubbles on the electrode membrane were removed by the experimenter, and the SDI recovered to its original value. On (approximately) Day 13, the electrode showed a decrease in its sensitivity and a change in the drift, as shown in Figure 7 (open circles). On (approximately) Day 22, the electrode 2 was replaced because the SDI was approximately 79. By contrast, the NO ₃− electrode 3 (filled triangles, Figure 6A and bar chart in Figure 6D) exhibited a sensitivity of approximately 37 on Day 1, which indicates a change in the sensitivity of the membrane compared to those of the other electrodes ( Figure 7). Consequently, the electrode was replaced when the SDI exceeded 75. The electrode sensitivity momentarily decreased 11 days after the replacement. Accordingly, the electrode was replaced again.

These laboratory test results show that the electrodes can be replaced on time based on the proposed implemented self-diagnostic algorithm. Consequently, nitrate ions can be continuously and effectively monitored.

3.2. Learning Results of Deep Neural Network-Based Diagnostic Model for Electrode Status

As shown in Figure 8, the web-based nitrate monitoring page was constructed and tested to monitor the measurement data and provide the following three types of information: (i) the current nitrate concentration ( Figure 8B) was monitored and plotted as a continuous graph over time ( Figure 8C); (ii) the change in the sensitivity of each electrode in Figure 8E indicates the SDI ( Figure 8D); (iii) based on this value, an alarm message was generated when the electrode had to be replaced ( Figure 8A). In Figure 8C, a relatively rapid change in NO ₃− concentration is observed for a short period of time, which might be affected by the detection of an abnormality in the N3 electrode.

3.3. Field Verification Experiment

The application results of the developed online monitoring system and self-diagnostic algorithm for the on-site determination of the electrode status are presented in Figure 9. The variation in the measured nitrate concentration of each electrode is plotted in Figure 9A, and their average is compared with the concentration graph obtained from the standard analysis of real samples in Figure 10. As shown in Figure 9C-E, the SDI developed in the present study advised the user to replace an electrode during the field experiment; ten alerts were issued (excluding redundancy), and seven electrodes were replaced. Among the ten alerts, two were generated because of an external factor rather than a problem with the electrode itself. The external factor that includes an ambient temperature below zero might affect a system malfunction. The times of these erroneous alerts are indicated in Figure 9A. During system fault (1), the prepared standard solution with the lower concentration was exhausted, which resulted in an erroneous fault prediction. During system fault (2), the temperature of the tank was between 0 °C and −7 °C according to Figure 9B. Hence, it was assumed that the measurement setup was frozen. Consequently, the nighttime measurements were not conducted, and the daytime measurements were conducted twice to collect sufficient data. During those times, the SDI sent warnings about abnormally working electrodes to the user who immediately replaced the respective electrodes. The nitrate concentration significantly increased to values between 150 and 300 mg/L over an interval of three to five days before returning to its original level (20 mg/L). Because the real standard analysis results in Figure 10 reveal similar trends at the same time point, the concentration data collected by the proposed monitoring system excellently reflect the change in the water quality.

In this field experiment, the verification results of the electrode replacement timing and the classification of the abnormal state of the actual electrode through SDI can be confirmed through Table 3. The overall classification accuracy was 0.77, and the recall for determining the actual replacement time was 0.87. However, although the SDI algorithm predicted that the ISE was an actual replacement, there were many cases where the electrode state was not in the replacement state, therefore the precision was 0.67, which was lower than other indicators.

The collected samples were determined as actual concentrations using ion chromatography as a standard analysis method. The linear relationship between the ion concentrations determined by the ISEs and standard analyzers is presented in Figure 11; the curve has a slope of 0.724 and an offset of 10.162. This indicates that the two sets of measurements are similar; nevertheless, the ISE measurement results tend to be lower than the actual concentrations. Furthermore, the high coefficient of determination (R ²) of 0.904 indicates that the performance of the electrodes can be improved via compensation.

In summary, the laboratory and field results indicate that the electrodes could be used for approximately 20 days without replacement in the laboratory test; the field monitoring experiment resulted in more frequent replacements. This was attributed to the climate conditions in winter; for example, the periodic freezing of the electrode surface adversely affected the durability of the membrane. Accordingly, the electrode status reacted sensitively to the temperature, which indicates the importance of a self-diagnostic algorithm for the determination of the electrode status.

4. Discussion

Although ISEs have received great attention as water quality measurement devices owing to their fast reaction and user friendliness, many researchers have reported frequent failures in field applications [ 28, 29, 30]. When the measurement target is not sufficiently accessible, the sudden failure of the ISE cannot be dealt with immediately, which may result in the loss of effective data and problems in the water quality management owing to false information. This paper proposes an algorithm that diagnoses the status of ISEs, displays the replacement time, and verifies its own effectiveness. It has been reported that an error in the measurement system may occur due to changes in external temperature or changes in the electrode itself when the actual ISE electrode is applied in the field [ 6, 7]. The proposed SDI is an empirical index that reflects electrode drift, sensitivity change, and precision of expected values and obtained 77% accuracy for electrode conditions as a result of field experiments. In addition, the recall for the probability of an electrode malfunction was about 87%, which was used as an indicator to deliver an effective alarm. In addition, the two-point normalization method was applied to record the SDI index and to compensate for changes in the sensitivity of the electrode. It could additionally be expected to reduce the temperature change that affects the potentiometric measurements with ISEs.

In this study, an online wireless nitrate monitoring system was installed at a water supply source, and the variations in the concentration were monitored online in real time. Despite the sudden changes in the concentration, highly concentrated reagents could be accurately measured to provide the user with accurate information. According to the results of the field experiment, the measured region exhibited drastic increases in the concentration at two- to three-day intervals owing to the discharge of waste water from a nearby agricultural facility. This facility uses a hydroponic system that supplies nutrients in the form of an aqueous solution. When the amount of nutrient solution reaches a certain level, the waste nutrient solution is automatically exhausted through a drain. In particular, although the facility has a primary nitrate purifier, the developed monitoring system demonstrated that the waste nutrient solution can be one of the major causes of water pollution in this region. In Europe, because of the Nitrates Directive, the nitrate levels in water have been decreasing since 1990 [ 31], and the emission of nutrient solutions from agricultural facilities has been further restricted. In addition, researchers have tried to optimize the management of nutrient solutions to mitigate environmental pollution [ 32]. Although no relevant policies have been implemented in South Korea, the awareness of water pollution due to green algae has prompted public agencies and authorities to establish suitable measures for managing the nitrate concentrations in water. Against this background, the online nitrate monitoring system developed in this study is a useful tool for many application fields.

In addition, an algorithm for diagnosing the status of nitrate electrodes was proposed and used in a field experiment to operate electrodes. A more intuitive method for identifying and indicating the electrode replacement time was established with the help of SDIs, which gradually increased up to the proposed threshold (SDI ≥ 70). Accordingly, SDIs can be good indicators for determining the status of an electrode before its failure. There were two system faults during the field experiment, and the diagnostic electrode status algorithm issued the same warning for each event that could lead to the abnormal status.

5. Conclusions

In this study, an online measurement system was developed to identify the reliability and durability of a nitrate-selective electrode for sensing and measuring nitrate concentrations in real time. The proposed diagnostic algorithm can determine the electrode replacement time with the help of SDIs, which reflect the sensitivity change and signal drift of the electrode. The SDI method was effective in the laboratory-scale repetitive measurements; it signaled that an electrode needed to be replaced when the SDI reached 70. This SDI-based diagnosis algorithm was applied in an actual experiment; the accuracy of classification was 0.77.

The developed online wireless nitrate monitoring system and electrode diagnostic algorithm were used in a field application test. The monitoring and web-based alarm systems were successfully implemented, the electrodes were successfully replaced, and unidentified high-concentration events were detected. Hence, the developed monitoring system and electrode diagnostic algorithm can help with identifying the causes of water pollution.

Author Contributions

Conceptualization, H.-J.K.; methodology, D.-H.J. and W.J.C.; software, D.-H.J. and J.Y.K.; resources, H.-J.K.; writing-original draft preparation, D.-H.J.; writing-review and editing, H.-J.K. and S.H.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Rural Development Administration (RDA), Republic of Korea. (Project number: PJ0149112021).

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Acknowledgments

The authors express their gratitude to all members in Biosystems Control and Precision Agriculture Laboratory in Seoul National University.

Conflicts of Interest

The authors declare no conflict of interest.

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Jung, D.-H.; Kim, H.-J.; Kim, J.Y.; Park, S.H.; Cho, W.J. Water Nitrate Remote Monitoring System with Self-Diagnostic Function for Ion-Selective Electrodes. Sensors 2021, 21, 2703. https://doi.org/10.3390/s21082703

Jung D-H, Kim H-J, Kim JY, Park SH, Cho WJ. Water Nitrate Remote Monitoring System with Self-Diagnostic Function for Ion-Selective Electrodes. Sensors. 2021; 21(8):2703. https://doi.org/10.3390/s21082703

Chicago/Turabian Style

Jung, Dae-Hyun, Hak-Jin Kim, Joon Yong Kim, Soo Hyun Park, and Woo Jae Cho. 2021. "Water Nitrate Remote Monitoring System with Self-Diagnostic Function for Ion-Selective Electrodes" Sensors 21, no. 8: 2703. https://doi.org/10.3390/s21082703

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

# data[].attributes.attachment_link.title
Water Nitrate Remote Monitoring System with Self-Diagnostic Function for Ion-Selective Electrodes

# data[].attributes.attachment_link.description
The detection of nitrate pollutants is a widely used strategy for protecting water sources. Although ion-selective electrodes (ISEs) have been considered for the determination of ion concentrations in water, the accuracy of ISE technology decreases owing to the signal drift and decreasing sensitivity over time.

# data[].attributes.attachment_link.readable_text
Mourad N. El-Gamal, Academic Editor and Mohannad Y. Elsayed, Academic Editor

Abstract

Multi-parameter water quality monitoring is crucial in resource-limited areas to provide persistent water safety. Conventional water monitoring techniques are time-consuming, require skilled personnel, are not user-friendly and are incompatible with operating on-site. Here, we develop a multi-parameter water quality monitoring system (MWQMS) that includes an array of low-cost, easy-to-use, high-sensitivity electrochemical sensors, as well as custom-designed sensor readout circuitry and smartphone application with wireless connectivity. The system overcomes the need of costly laboratory-based testing methods and the requirement of skilled workers. The proposed MWQMS system can simultaneously monitor pH, free chlorine, and temperature with sensitivities of 57.5 mV/pH, 186 nA/ppm and 16.9 mV/°C, respectively, as well as sensing of BPA with <10 nM limit of detection. The system also provides seamless interconnection between transduction of the sensors' signal, signal processing, wireless data transfer and smartphone app-based operation. This interconnection was accomplished by fabricating nanomaterial and carbon nanotube-based sensors on a common substrate, integrating these sensors to a readout circuit and transmitting the sensor data to an Android application. The MWQMS system provides a general platform technology where an array of other water monitoring sensors can also be easily integrated and programmed. Such a system can offer tremendous opportunity for a broad range of environmental monitoring applications.

Keywords: electrochemical sensors, free chlorine, heavy metal, pH, pharmaceutical contaminants, potentiostat, temperature sensor, water quality monitoring, water sensors

1. Introduction

Water quality monitoring is vital for water safety determination and associated public health [ 1, 2, 3, 4, 5]. Water quality parameters are decided by mutually dependent chemical, physical, and microbial features. Typical water quality parameters include pH, free chlorine, conductivity, dissolved oxygen, turbidity, and bacterial contamination [ 6, 7]. Some of the most important but simple water quality parameters are pH, free chlorine and temperature due to their direct relationship with their water disinfection efficiency, as well as their influence to other parameters [ 8]. Although many water treatment plants use chloramines as disinfectants, the use of free chlorine is still the most common method for disinfection in isolated and resource-limited areas [ 9]. The presence of organic micropollutants such as bisphenol A (BPA) in water is another emerging water problem due to industrial effluents, and widespread use and disposal of plastics in the environment [ 10]. Standard water quality monitoring systems are complex, time-consuming and expensive due to transport of samples, use of sophisticated equipment and trained personnel [ 8]. Many conventional systems require independent collection of samples and assessment, and fail to deliver water quality parameters in real-time [ 11]. Real-time water quality monitoring technologies have made considerable advancement in recent years [ 11]. However, these technologies have limited applications monitoring source water or the operation of water treatment plants. Also, the high expenses related with the equipment, maintenance and calibration of water sensors have prevented them from being used in large distribution systems and for end-users. Furthermore, most of these sensor systems measure only one parameter at a time [ 12, 13, 14]. Therefore, it is critical to monitor key water quality parameters with high sensitivity and accuracy in a reliable and easy-to-use manner, which can be achieved with a multi-parameter sensing platform.

Recently, integrated wearable sensing systems were developed to monitor sweat biomarkers and the treatment of diabetes [ 15, 16, 17]. These systems provide accurate sensing and measurement data with high spatial and temporal resolution. They can also perform simultaneous and on-site signal processing with their integrated electronics. However, most of these systems are complex and expensive. Thus far, many integrated water quality sensor systems have been reported for pH, dissolved oxygen, conductivity, temperature, and microorganism including bacterial and organic nutrients/metabolites monitoring [ 18, 19, 20, 21, 22]. Some of these parameters, including pH and temperature, were identified as Key Performance Indicators (KPI) towards developing a Smart Water Quality Monitoring System (SWQMS) [ 23]. Smart and cost-effective water quality monitoring systems have been demonstrated, that utilize Internet of Things (IoT) [ 24] and Information and Communications Technology (ICT) [ 25] to enable connections between different sensors and devices. Currently available integrated sensing systems typically include free chlorine. Standard free chlorine sensing system involve the use of N, N ′-diethyl-p-phenylenediamine (DPD), which is an optical absorption-based method that is not very user-friendly, susceptible to human error and costly. On the other hand, amperometric free chlorine sensors require frequent calibration as the stability of the sensing electrodes degrades over time. Moreover, BPA is commonly measured by conventional analytical techniques based on chromatography methods such as liquid chromatography, liquid chromatography-mass spectrometry, and immunoassays, which require skilled personnel and expensive equipment. Therefore, a new design approach is required to develop an integrated, low-cost and accurate sensing system where many of the water quality parameters can complement one another to evaluate the overall quality of the water. Such an integrated sensing system, that can monitor pH, free chlorine (Cl), temperature and organic micropollutants for hand-held applications such as drinking water and swimming pool water monitoring, needs to be developed.

In this research, we develop a Multi-Parameter Water Quality Monitoring System (MWQMS) that can simultaneously measure pH, free Cl concentration, and temperature, and electrochemically measure (i.e., linear sweeping voltammetry) BPA in drinking water. The proposed MWQMS system is designed to accomplish a seamless interconnection between sensing electrodes, sensor signal transduction, signal conditioning, data processing and low-power wireless transmission of the sensors signals to a smartphone interface. The MWQMS system also integrates the potentiometric, amperometric and voltametric sensors, providing a more ubiquitous platform for different electrochemical sensing approaches. A custom-designed, small-form-factor and multi-layered printed circuit board (PCB) was designed with off-the-shelf integrated-circuit components, microcontrollers and open-source software programming. Also, low-cost electrochemical sensors were fabricated on glass substrates that were connected to the PCB readout circuit board. The MWQMS system has significantly reduced the complexity and associated high costs of the sensor as well as the design of the electronic data acquisition system.

2. Materials and Methods

2.1. Fabrication of the Sensors

The fabrication processes of the array of electrochemical sensors for the MWQMS were based on our recent study [ 26]. In ref. [ 26], we developed materials synthesis, sensor fabrication and their performance characterizations. In contrast, in this study, we developed a complete sensing system through the integration of the sensors with our custom-designed readout system, smartphone-controlled operation and real samples analysis. Although the sensors were developed and calibrated in our previous study [ 26], the sensors were calibrated again in this study with our custom-designed readout circuit and smartphone application in order to validate the sensors performance with the integrated MWQMS system. In brief, the pH sensor was a potentiometric sensor that was fabricated with Palladium (Pd) ink on polyimide film substrate. The free Cl sensor was an amperometric sensor that was fabricated with a carbon-based electrode with subsequent electrochemical modification by ammonium carbamate. The reference electrode was made of Ag/AgCl with commercial Ag/AgCl paste. The temperature sensor was based on resistance-change that was fabricated with p-type Si and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) film in a Wheatstone bridge configuration. The sensors for the organic micropollutants (i.e., BPA) was fabricated by attaching a commercial paper-based screen-printed carbon electrode (SPE) onto the substrate containing the pH and free Cl sensors, followed by modification with graphene oxide (GO) and β-cyclodextrin functionalized multi-walled carbon nanotubes (GO-MWCNT-βCD). The synthesis of the chemically functionalized MWCNT-βCD is based on our previous study [ 27]. The MWCNT-βCD is further mixed with GO with a 1:1 ratio, to enhance the electrochemical sensing performance towards detecting BPA. The SPE consists of a carbon-based counter electrode and an Ag/AgCl based printed reference electrode. The commercial Ag/AgCl pastes are composed of polymer resins such as polyvinyl butyral (PVB), which facilitates maintaining a stable potential over a long period of time in samples with different ionic strengths [ 17]. A photograph of the fabricated sensors on two glass substrates are shown in Figure 1 . The pH, free Cl and BPA sensors are fabricated on one glass microscope slide, whereas the temperature sensor is fabricated on another glass slide. The two glass slides are then placed slide-by-side and connected to the readout printed circuit boards (PCB). The sensors are cleaned with tap water and stored in a dry place when not in use.

2.2. Readout System and Smartphone Application

The MWQMS readout circuit board was developed on top of an Arduino Uno R3 (ATmega328P) 8-bit microcontroller. Additionally, a Water Quality Monitor (WQM) board and a potentiostat board was vertically staked on top of the Arduino board, as shown in Figure 1 . The WQM and potentiostat boards contain the circuits associated with analog and mixed signal sensors. The WQM board was created and programmed to read pH and temperature data in real time (e.g., one second interval), and free chlorine every 50 s. The potentiostat board was intended to operate independent voltametric sensing using the smartphone application. A Bluetooth transceiver was also connected to the microcontroller unit for data transmission wirelessly to our custom app on an Android smartphone. The Android app contains two main units that communicate with the WQM and potentiostat circuit boards.

The WQM module can display real-time pH, free Cl and temperature data, and it also has settings options to input calibration information. The potentiostat module can run, save or create different types of voltametric sensing experiments. The total amount of time needed to run a single voltametric sensing experiment (i.e., BPA sensing) is the sum of pre-treatment time and scanning time (i.e., scan-rate × potential-range). The WQM and the potentiostat PCB boards are designed separately because of the fundamental difference of the sensor's operation. In the WQM board, the signal from the sensor is transmitted only in one direction, which is from the sensors towards the PCB. On the other hand, the potentiostat board has two-way communication, in which a sweeping potential is being applied towards the electrochemical sensor, and the resulting voltametric current signal is being read almost at the same time, while it is being transmitted towards the potentiostat PCB. Because of these differences in sensor operation, the WQM and potentiostat cannot run at the same time.

3. Results

3.1. Design of the MWQMS System

The MWQMS system is designed for simultaneous in situ pH, free Chlorine (Cl) and temperature measurement, as well as click-on-demand BPA measurement in water. The fabrication of the sensors was based on two microscope glass slides, which offered an inexpensive sensors integration, as shown in Figure 1 . The printed circuit boards (PCBs) were stacked together to integrate signal conditioning, processing and wireless data transmission on the same platform. This was possible with the ubiquitous and affordable integrated-circuits (ICs) and optimization of software and hardware designs [ 28]. The water quality parameters (i.e., pH and free Cl, temperature and BPA) were proposed to estimate the overall condition and drinkability of the water being tested. For instance, accurate monitoring of free Cl concentration is critical for the safety of public health, which is also advised by the World Health Organization (WHO). In contrast, free Cl concentration depends on the pH of water. Also, free Cl, and pH of water both depend on temperature. Therefore, temperature measurement is also important to compensate for the pH and free Cl sensor measurements. The selection of BPA was due to the increased presence of emerging organic micropollutants in water cycles, which may become a huge water problem worldwide, therefore requiring a user-friendly and inexpensive detection technique. Thus, the electrochemical sensing of BPA was chosen as an example of the versatile nature of our proposed MWQMS system.

3.2. Signal Flow and PCB Design

The design of the signal flow of sensors is an important step towards designing suitable printed circuit boards (PCBs) for sensor data acquisition and interfacing. Figure 2 illustrates the block diagram of the signal flow paths that consist of signal transduction, conditioning, processing and wireless transmission routes. The signal flow is designed to facilitate simultaneous (pH, free Cl and temperature) and click-on-demand (BPA) monitoring of these water quality parameters. In summary, this diagram shows the signal-conditioning route for every single sensor, which are employed with analogue circuit components such as low-pass filter, buffer, transimpedance amplifier, and potentiostat corresponding to the transduced sensor signals.

The circuits are designed to ensure fine resolution of the sensor signal while keeping the signal amplitude in the input range of the analogue-to-digital converter (ADC). After that, the conditioned signals are compensated, and relayed to the Bluetooth wireless transceiver by the serial communication protocols of the Arduino microcontroller. The Bluetooth transceiver facilitates wireless data transmission to a Bluetooth-supported smartphone and a custom-designed app. The smartphone application consists of an interface to upload sensor measurement data to online storage. The signal flow diagram was used to design the PCBs of the MWQMS system. For example, the upper-left, upper-right and lower blocks of the signal flow ( Figure 2 ) diagram represent the potentiostat, the WQM and the Arduino Uno microcontroller PCBs, respectively. A photograph of the MWQMS system is shown in Figure 3 a, depicting the PCBs that are connected to the sensors through a sensor connection assembly.

The potentiostat and WQM PCBs are shown in detail in the photographs of Figure 3 b,c, respectively. The size of the PCBs is designed with respect to the size of an Arduino Uno microcontroller, so that they can be vertically stacked easily. This approach significantly reduced the overall size of the system. The major functional circuit components are shown with yellow dashed rectangles. Each PCB consisted of their own power supply unit (1 and 8). The potentiostat PCB consisted of a potentiostat Circuit IC (2), a low-pass filter (LPF) (3) connected to digital-to-analog converter (DAC) (4) output filter, a transimpedance amplifier (TIA) (5), and another LPF (5) connected to an ADC input filter (7). The WQM PCB consisted of buffers (9), an LPF for the pH sensor (10), a TIA (11) and LPF (12) for the free Cl sensor, an ADC IC for both the pH and free Cl sensors (13), and an ADC for the temperature sensor (14).

The signal quality of the potentiostat was verified by comparing the cyclic voltammetry (CV) measurement of a standard redox probe (5 mM concentration of K ₃[Fe(CN) ₆]) with a screen printed carbon electrode (SPE) obtained from CH Instruments Inc. with EMStat 3 potentiostat from PalmSens.com. The comparative CV curves are shown in Figure 4 . The curves show that the CV obtained from the MWQMS system is very similar to the one obtained from EMStat 3, as the redox peak positions and peak intensities are almost the same. There is a slight vertical shift of the curves, which corresponds to a shift in the baseline. However, the baseline shift did not change the peak positions and their absolute intensities. Such variations are very common in electrochemical measurements, as the two curves are obtained from two different measurement with two devices and one sample. The signal-to-noise ratio of the CV curves obtained from the MWQMS system is also very high (>100).

3.3. Sensor Performance and Calibration

3.3.1. pH Sensor

The Pd/PdO based potentiometric pH sensor had high sensitivity (57.5 mV/pH) and stability, as shown in Figure 5 a [ 26]. The pH measurement range for calibration was from pH 4 to 10; however, the sensor is capable of measuring pH from 2 to 12. Also, the spin-coating fabrication required a very small amount (<10 µL) of the Pd ink precursor solution resulting in a lower cost (<10 cent). The pH sensing was based on the following redox reaction [ 4]:

The redox potential is determined using the Nernst equation:

where E ₀ is the standard electrode potential, R is the gas constant (8.31 J/mol/K), T is the absolute temperature and F is Faraday's constant (96,485.33 C/mol). The sensor's output voltage was between 0-400 mV, stable and with low-noise, allowing direct interfacing with the input of the ADC. The fast response time (~20 s) of the sensor enabled real-time pH monitoring. The MWQMS system can perform single-point (pH 7) and three-point calibration (at pH 4, 7, and 10) to accommodate sensors with different sensitivity and linear range. The temperature dependence of the pH sensor was compensated using [ 26, 29]:

pH=7+E c a l−E m e a s 57.5+(T m e a s−27)×0.22,

Finally, Equation (3) was programmed into the MWQMS system, where Ecal is the recorded voltage (in mV) while the sensor uses calibration solution of pH = 7, Emeas is the measured voltage (in mV) throughout pH sensing, and Tmeas (in °C) is the temperature of the water. The pH sensor resolution was 0.17 pH which was calculated from its hysteresis value of 9.8 mV [ 26]. The pH sensor showed negligible interference with common ions as discussed in our previous study [ 26].

3.3.2. Free Cl Sensor

The free chlorine sensor was fabricated by amine-modification of carbon electrode [ 30]. The amperometric free Cl sensing involves electrochemical reduction of HOCl (free Cl) corresponding to the following chemical reaction:

The resultant current value (at 50 s in each measurement) is related to the HOCl concentration [ 26]. The HOCl concentration is subsequently employed to measure the free chlorine concentration (both HOCl and OCl −) that corresponds to the Cl ₂ concentration [ 30]. As shown in Figure 5 b, the sensor output current range was between 0 and −5 µA, with free chlorine concentrations of 1 to 8 ppm. The sensor output was transformed into a voltage signal that ranged between 0 to 1 V utilizing a transimpedance amplifier. A low-pass filter was used to suppress the low-frequency noise in the transimpedance amplifier. The sensitivity of the free Cl sensor was 186 nA/ppm ( Figure 5 b). The temperature dependence of the free chlorine sensor was determined to be 6.2 nA/ppm/°C, which was used in the calibration equation of the free chlorine sensor [ 26]:

I o u t=[186+(T m e a s−27)×6.2]C N a O C l+64.7,

where Iout (in nA) represent the output current, Tmeas (in °C) represent the temperature of water, and CNaOCl (in ppm) represent NaOCl concentration. The transfer function was calculated based on the transimpedance amplifier gains and low-pass filter as follows:

where V (in mV) represent the output voltage of the signal conditioning circuit. Lastly, the free chlorine concentration equation with pH and temperature corrected calibration equation was programmed into MWQMS system [ 26]:

The free chlorine sensor resolution was calculated by changing the free chlorine concentration between 1 and 8 ppm and measuring the hysteresis. The hysteresis value was estimated to be 11 nA equivalent to a resolution of 0.06 ppm [ 26].

3.3.3. Temperature Sensor

The temperature sensor was developed based on a Wheatstone bridge configuration. Among the four arms of the bridge, two were made of p-type silicon wafer (1 cm × 10 cm) with a positive TCR of 1%/°C and two were fabricated by drop-casted PEDOT:PSS films with a negative TCR of −0.32%/°C [ 26, 31, 32]. The high TCR values of the silicon and PEDOT:PSS film provided high temperature sensitivity with negligible drift. The resistances of the four resistors were selected in such a way that the output voltage remains within the ADC input range of −1 to +1 V, towards measuring 0 °C-50 °C. The resistances were also optimized for highest sensitivity, reduced self-heating-induced drift, and the overall reduction of the area of the sensor. The measured sensitivity of the temperature sensor was 16.95 mV/°C as shown in Figure 5 c. A calibration equation was used in the MWQMS system for the determination of temperature as follows [ 26]:

where Tmeas (in °C) represent the temperature of water and Vout (in mV) represents the temperature sensor output voltage.

3.3.4. BPA Sensor

The fabricated BPA sensor was based on a screen-printed electrode (SPE), which was integrated on the same glass substrate of the pH and free Cl sensor, followed by drop-cast modification with GO-MWCNT-βCD. The GO-MWCNT-βCD solution was prepared based on our previous study [ 27]. Briefly, MWCNTs were covalently modified with βCD through a one-step Steglich esterification method. Then, a 2 mg/mL MWCNT-βCD suspension was combined with 1 mg/mL GO suspension with a 1:1 volume ratio. After that, the solution was ultrasonicated for 15 min. Due to contamination of the electrode surface with residues of oxidized BPA, a freshly prepared GO-MWCNT-βCD/SPE electrode was used only once for BPA sensing. However, the SPE electrode was reused by cleaning the GO-MWCNT-βCD(SE) with cleaning solvents and re-drop-casting GO-MWCNT-βCD solution on the SPE working electrode. Therefore, the SPE electrode is reusable for multiple BPA sensing experiments, which can significantly decrease the cost of the sensor. A typical BPA sensing experiment in this study requires ~30 min, in which ~30 min is required for electrode pre-treatment (magnetic stirring of samples) and ~30 s is required for the actual voltametric potential sweeping [ 33].

Figure 5 d shows the BPA sensing characteristics using the GO-MWCNT-βCD(SE)/SPE electrode with the MWQMS system. The BPA sensing was performed by Linear Sweep Voltammetry (LSV) from 50 nM to 5 µM BPA. The inset of Figure 5 d illustrates the linear calibration curve for BPA with a slope of 10.3 µA/µM. The limit of detection (LoD = 3 s/ m), was calculated to be 6 nM based on our previous study [ 27]. In this LoD calculation, s represents the standard deviation of the blank solution (30 nA) and m represents the slope of the calibration curve.

It is worth mentioning that a significant novelty of the MWQMS system stems from the integration of GO-MWCNT-βCD(SE)/SPE electrode with a custom-designed potentiostat board, that resulted in a very inexpensive and easy-to-use system for water monitoring. In addition to BPA sensing, the same electrochemical sensor can also be used for detecting pharmaceutical contaminants such as acetaminophen and estrogen, and heavy metal such as lead and arsenic, which can be accomplished by simply using different voltametric parameters in the potentiostat. This allows the MWQMS system to operate in a multi-modal sensing capability that is very cost-effective.

3.4. Smartphone Application

An Android-based smartphone application called "Water Testing Suite" was designed for the MQWMS system. The application was first developed in Java and then compiled into an Android application. The application is designed to acquire the sensor data, perform calibration, and display and save the data in a cloud-based webserver. Figure 6 shows screenshots of different measurement units of the application. The opening screen of the application (screenshot 1) prompts the user to select the type of measurement to Run, such as Water Quality Monitor (for pH, free Cl and Temperature sensing), and potentiostat electroanalysis (for BPA sensing).

The selection of the Water Quality Monitor option in the opening screen leads to a screen containing a real-time display of measurement parameters for pH, free Cl and temperature of the water sample (screenshot 1). This screen also contains three graphs (to be scrolled down) for the corresponding sensor outputs. Therefore, this screen provides real-time update of the temporal measurement data. The sensor output is updated every second and the historical data is accumulated in the temporal graphs.

The pH sensor output graph (screenshot 2) can be calibrated using an on-screen one-point calibration button at a pH 7. This allows faster calibration of the pH sensor when the sensitivity of the sensor is very stable. However, direct calibration can also be done by going to the "Settings" option, where the user can put calibration information in terms of the sensor sensitivity and y-axis intercept potential values. This enables the ubiquitous nature of the MWQMS system to be used with other types of potentiometric pH sensors. The free Cl and temperature sensor output graphs (screenshots 3 and 4) are also updated at the same time with the pH sensor every second. The pH and temperature sensor data are used to calibrate the free Cl concentration measurement.

The selection of the Potentiostat Electroanalysis option in the opening screen leads to a screen containing options (screenshot 5) to Run, Edit or Create a new voltametric measurement, such as Linear Sweep Voltammetry (LSV), Cyclic Voltammetry (CV), Differential Pulse Voltammetry (DPV) and Square Wave Voltammetry (SWV) (screenshot 6). A selection of previously saved experimental parameters can be accessed from a drop-down menu, as shown in screenshot 6. A new experiment with custom parameters can also be created, as shown in screenshot 7. A representative CV curve is shown in screenshot 8.

3.5. Drift and Interference

The pH sensor does not need calibration for a continuous measurement of one hour. The drift of the pH sensor was investigated in our previous study, which showed a drift of 4 mV in pH 4 in an 8 h period that corresponded to a drift of 0.009 pH/h, as displayed in Figure 7 a. The interference of the pH sensor was determined in the presence of interfering ions (CaCl ₂, KNO ₃, Na ₂(SO ₄), (NH ₄) ₂SO ₄, NaCl, and KCl). A maximum of a 0.24 pH change was observed with 0.1 M CaCl ₂ that was, in fact, due to transformation of the sample pH with the high concentrations of CaCl ₂.

The temporal response of the free chlorine sensor also showed negligible drift (<0.1 ppm), as shown in Figure 7 b. The selectivity of the free chlorine sensor was also investigated for 2 ppm free chlorine solutions with ~400 ppm of interfering ions. A negligible change was observed with the interfering solution of KNO ₃, Na ₂(SO ₄), NaCl, and KCl. However, the free chlorine response became 0 ppm when CaCl ₂ and (NH ₄) ₂SO ₄ were added, as they chemically reacted with the free chlorine. The time-dependent drift performance of the temperature sensor was studied. The drift study showed negligible drift of ~0.6 mV over a 24 h period, as indicated in Figure 7 c. The high selectivity of the BPA sensor was also demonstrated with other interfering species such as Na ²⁺, K⁺, ascorbic acid, dopamine, and acetaminophen, as shown in Figure 7 d.

The lifetime of the sensors was studied by performing long-term analysis with at least 6-month old sensors. A 7 day long measurement of pH, free chlorine, and temperature of real water samples taken from a lake, tap and pool was done by the MWQMS system [ 26]. The pH sensor showed a highest drift of 0.12 pH for tap water. The temperature sensor also showed a drift of ±0.25 °C. The free chlorine sensor showed gradual decay of free chlorine due to interaction with the sensor as well as exposure to the external environment.

3.6. Real Sample Analysis

The MWQMS was used to measure real samples from tap water, lake water, and swimming pool water. A comparison of the pH, free chlorine, and temperature measured by the MWQMS system and using reference methods are shown in Table 1 . A commercial glass electrode-based pH meter (HANNA HI98128) was utilized as the reference for pH and temperature sensing. A commercial DPD-based kit (LaMotte 2056 ColorQ PRO 7) was used as a reference for free chlorine sensing. Table 2 shows the difference of the BPA measurements done by the MWQMS system after spiking tap water with BPA. The measurement results of the MWQMS system were closely comparable to those of the commercial methods. Differences below 5% were observed for pH, free chlorine, and temperature sensors, while the BPA measurements showed recovery between 96% and 106%. The differences in our measurement and reference values are, therefore, reasonable in practical application scenarios. These differences may be attributed to errors/deviations in the commercial pH, free chlorine and temperature sensors themselves, or human errors.

Table 1

Tap Water (Hamilton)

Lake Water (Lake Ontario)

Pool Water
(Hamilton)

MWQMS

Ref.

MWQMS

Ref.

MWQMS

Ref.

pH

8.0 ± 0.1

8.2 ± 0.1

7.4 ± 0.1

7.5 ± 0.1

7.1 ± 0.2

7.2 ± 0.1

Free Cl, ppm

1.1 ± 0.1

0.95 ± 0.05

0.00

0.00

2.6 ± 0.1

2.4 ± 0.1

Temp., °C

15.6 ± 0.2

15.3 ± 0.1

14.5 ± 0.2

14.8 ± 0.1

24.6 ± 0.2

24.8 ± 0.1

Table 2

Prepared Concentration (µM)

Found (µM)

Recovery (%)

BPA

0.50

0.48 ± 0.02

96.0 ± 4.2

1.00

1.06 ± 0.03

106.0 ± 2.8

5.00

4.97 ± 0.10

99.4 ± 2.0

3.7. Limitations and Future Improvements

The MWQMS system can be further improved by tackling some of its limitations that are related to both the sensors and readout systems. For example, the sensors were mostly hand-made by lab-oriented fabrication methods. However, these methods can be tailored for mass production such as screen-printing and solution processing, for scaling up the fabrication and reducing overall costs of the sensor. Such processes will improve sensor reproducibility and thus sensing performance. Also, reusability of the sensors can be improved by careful choice of sensing materials and sensing methods. Further, the readout system used the Arduino Uno based microcontroller, which can be re-designed with smaller microcontroller unit to reduce the footprint of the system towards a hand-held device configuration.

4. Conclusions

We developed an integrated multi-parameter water quality monitoring system (MWQMS) that can concurrently determine water parameters such as pH, free chlorine concentration and temperature, as well as determine bisphenol A on-demand. The MWQMS system was comprised of a Pd/PdO-based pH sensor, a carbon-based free chlorine sensor, a hand-drawn temperature sensor, and a graphene oxide, carbon nanotubes and β-cyclodextrin bisphenol A sensor, all of these fabricated on two glass slides. The sensors can measure water quality parameters such as pH and temperature in real time, free chlorine in every 50 s, and BPA in half an hour, with a high sensitivity of 57.5 mV/pH (pH), 186 nA/ppm (free chlorine), 16.95 mV/°C (temperature) and 10.3 µA/ppm (bisphenol A), in a user-friendly manner. The MWQMS system is small and a simple-to-use measurement unit with a smartphone application. It is a promising step towards practical applications such as on-site water quality monitoring. The system also has the flexibility to be modified to accommodate additional water quality monitoring sensors including conductivity, dissolved oxygen and different types of ions. Finally, improvements are already underway in our ongoing research efforts towards commercialization of the water quality monitoring system and more extensive testing in application environments.

Author Contributions

The manuscript was written through contributions from all authors. A.U.A. and M.J.D., conceived of the idea, and together with D.C. defined the detailed contents of the paper. A.U.A. wrote the first draft of the article which was critiqued by all authors. The article was then revised by A.U.A., D.C. and M.J.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by NSERC Green Electronics Network (GreEN, Grant No. 508526-7), a Discovery Grant from the Natural Science and Engineering Research Council (NSERC) of Canada of Canada and the Canada Research Chair program.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

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A Low-Cost Multi-Parameter Water Quality Monitoring System

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Multi-parameter water quality monitoring is crucial in resource-limited areas to provide persistent water safety. Conventional water monitoring techniques are time-consuming, require skilled personnel, are not user-friendly and are incompatible with operating ...

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Chloride Ion-Selective Electrode User Manual

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https://www.reddit.com/r/arduino/comments/zhhk92/how_can_i_measure_salinity_of_water/

https://www.reddit.com/r/arduino/comments/5uxpgg/measuring_salinity_by_the_resistance_of_water/

# data[].attributes.attachment_link.description
A little backstory... Our school team need to measure salinity as a part of project that we are working on, but our teacher already "spent too much money" (school's budget for this project), even if sensor for salinity is cruical for this project, and he knows that!

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Also, I'm not worried about rusting or long term effects from electrolysis because the water is not used for plants or drinking or anything like that. I think the sensor would last long enough before having to change its probes as I'm gonna have its voltage source enabled only when a reading is needed.

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Measuring Salinity with a TDS Meter - Aero-Gro

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Saltwater pools, spas, and aquariums need accurate salinity measurement. There are various measurement scales used to measure salinity.

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Salinity

Salinity is a measure of the mass of dissolved salts (ionic constituents) in a given mass of solution and usually expressed as parts per thousand (ppt).

Ions commonly found in water include calcium, magnesium, potassium and sodium cations and bicarbonate, carbonate, chloride, nitrate, and sulfate anions.

Conductivity is a good measure of salinity in water. Other indirect measures are water density, sound speed, and refractive index. All can be used to calculate salinity.

Calculations

1. Collect conductivity (uS/cm) and water temperature (C) data.

2. Use spreadsheet or calculator to convert conductivity and temperature data to salinity value.

3. Equations used are based on the practical salinity scale and are valid for surface waters with salinity between 2 and 42 ppt. Please view the source to this document (in most browsers, you can right-click on this page and select "View Source" or "View Page Source" from the menu) and consult Standard Methods for the Examination of Water and Wastewater if you have questions or concerns.

More on Water Quality Monitoring

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Water Quality Monitoring: Conductivity to Salinity Conversion

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Salinity is a measure of the mass of dissolved salts (ionic constituents) in a given mass of solution and usually expressed as parts per thousand (ppt). Ions commonly found in water include calcium, magnesium, potassium and sodium cations and bicarbonate, carbonate, chloride, nitrate, and sulfate anions.

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Three Dollar EC - PPM Meter [Arduino] | Details | Hackaday.io

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This Blog will Cover How to build a cheap EC meter for your aquaponics/Hydroponics or water quality related projects. We are not going to get into what the ideal value of PPM or EC is, Just cover how to measure and quantify a fluid.

We will be using this for the Urine based aquaponics unit,…

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Build your own IoT-based water quality monitoring system WITHOUT any TDS/pH meter !!

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#define TdsSensorPin A0
#define VREF 5.0              // analog reference voltage(Volt) of the ADC
#define SCOUNT  30            // sum of sample point

int analogBuffer[SCOUNT];     // store the analog value in the array, read from ADC
int analogBufferTemp[SCOUNT];
int analogBufferIndex = 0;
int copyIndex = 0;

float averageVoltage = 0;
float tdsValue = 0;
float temperature = 24;       // current temperature for compensation

// median filtering algorithm
int getMedianNum(int bArray[], int iFilterLen){
int bTab[iFilterLen];
for (byte i = 0; i<iFilterLen; i++)
bTab[i] = bArray[i];
int i, j, bTemp;
for (j = 0; j < iFilterLen - 1; j++) {
for (i = 0; i < iFilterLen - j - 1; i++) {
if (bTab[i] > bTab[i + 1]) {
bTemp = bTab[i];
bTab[i] = bTab[i + 1];
bTab[i + 1] = bTemp;
}
}
}
if ((iFilterLen & 1) > 0){
bTemp = bTab[(iFilterLen - 1) / 2];
}
else {
bTemp = (bTab[iFilterLen / 2] + bTab[iFilterLen / 2 - 1]) / 2;
}
return bTemp;
}

void setup(){
Serial.begin(115200);
pinMode(TdsSensorPin,INPUT);
}

void loop(){
static unsigned long analogSampleTimepoint = millis();
if(millis()-analogSampleTimepoint > 40U){     //every 40 milliseconds,read the analog value from the ADC
analogSampleTimepoint = millis();
analogBuffer[analogBufferIndex] = analogRead(TdsSensorPin);    //read the analog value and store into the buffer
analogBufferIndex++;
if(analogBufferIndex == SCOUNT){
analogBufferIndex = 0;
}
}

static unsigned long printTimepoint = millis();
if(millis()-printTimepoint > 800U){
printTimepoint = millis();
for(copyIndex=0; copyIndex<SCOUNT; copyIndex++){
analogBufferTemp[copyIndex] = analogBuffer[copyIndex];

// read the analog value more stable by the median filtering algorithm, and convert to voltage value
averageVoltage = getMedianNum(analogBufferTemp,SCOUNT) * (float)VREF / 1024.0;

//temperature compensation formula: fFinalResult(25^C) = fFinalResult(current)/(1.0+0.02*(fTP-25.0));
float compensationCoefficient = 1.0+0.02*(temperature-25.0);
//temperature compensation
float compensationVoltage=averageVoltage/compensationCoefficient;

//convert voltage value to tds value
tdsValue=(133.42*compensationVoltage*compensationVoltage*compensationVoltage - 255.86*compensationVoltage*compensationVoltage + 857.39*compensationVoltage)*0.5;

//Serial.print("voltage:");
//Serial.print(averageVoltage,2);
//Serial.print("V   ");
Serial.print("TDS Value:");
Serial.print(tdsValue,0);
Serial.println("ppm");
}
}
}

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https://www.multcomercial.com.br/chave-gangorra-com-3-terminais-6a-250v-verde-com-neon-kcd1-102n.html

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chave gangorra com 3 terminais 6a/250v verde com neon - kcd1-102n

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Chave Gangorra com 3 Terminais 6A/250V Verde com Neon - KCD1-102N

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Chave Gangorra com 3 Terminais 6A/250V Verde com Neon - KCD1-102N

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#include <LiquidCrystal.h>
LiquidCrystal lcd(12, 11, 5, 4, 3, 2); // (RS, E, D4, D5, D6, D7)

void setup() {
lcd.begin(16, 2);
Serial.begin(9600);
}

void loop() {
int adcVal = analogRead(A0); // read the ADC value from the LM35
float milliVolt = adcVal * (5000.0 / 1024.0);
float tempC = milliVolt / 10;

float tempF = (tempC * 9 / 5) + 32;

Serial.print("Temperature (°C): ");
Serial.println(tempC);
Serial.print("Temperature (°F): ");
Serial.println(tempF);

lcd.setCursor(0, 0); // set the cursor position to the first line, first column
lcd.print("Temperature: ");
lcd.print(tempC);
lcd.print("°C");

lcd.setCursor(0, 1);
lcd.print("Temp(F): ");
lcd.print(tempF);
lcd.print("°F");

delay(500);
}

# data[].attributes.body
https://www.hackster.io/GAURAVK5/dfrobot-tds-meter-sensor-with-arduino-and-lcd-measure-92853d

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Introduction

TDS (Total Dissolved Solids) indicates how many milligrams of soluble solids are dissolved in one liter of water. In general, the higher the TDS value, the more soluble solids are dissolved in water, and the less clean the water is. Therefore, the TDS value can be used as one reference point for reflecting the cleanliness of water.

In this project, we will interface Gravity Analog TDS Sensor with Arduino Microcontroller and read the value in a 16×2 LCD Display (with i2c module)

For More:- https://techeonics.com/dfrobot-tds-meter-sensor-with-arduino-lcd-measure-water-quality-in-ppm-techeonics/

Website :- https://techeonics.com/

Github:- https://github.com/gauravk5/Techeonics-Arduino-Cod...

Following are the components required for making this project.

DFROBOT Gravity Analog TDS Sensor

DFROBOT G ravity Analog TDS Sensor is an Arduino-compatible TDS sensor/Meter Kit for measuring TDS value of the water. It can be applied to domestic water, hydroponic and other fields of water quality testing. This product supports 3.3 ~ 5.5V wide voltage input, and 0 ~ 2.3V analog voltage output, which makes it compatible with 5V or 3.3V control systems or boards.

The excitation source is an AC signal, which can effectively prevent the probe from polarization and prolong the life of the probe, meanwhile, it can help increase the stability of the output signal. The TDS probe is waterproof, it can be immersed in water for long time measurement.
Interfacing TDS Sensor with Arduino
Source Code/Program

Youtube Video

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DFROBOT TDS Meter Sensor With Arduino and LCD || Measure...

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In this project, we will interface Gravity Analog TDS Sensor with Arduino Microcontroller and read the value in a 16×2 LCD Display. Find this and other hardware projects on Hackster.io.

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https://circuitdigest.com/microcontroller-projects/arduino-ph-meter

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pH scale is used to measure the acidity and basicity of a liquid. It can have readings ranging from 1-14 where 1 shows the most acidic liquid and 14 shows the most basic liquid. 7 pH is for neutral substances that are neither acidic nor basic. Now, pH plays a very important role in our lives and it is used in various applications. For example, it can be used in a swimming pool to check the quality of water. Similarly, pH measurement is used in a wide variety of applications like agriculture, wastewater treatment, industries, environmental monitoring, etc.

In this project, we are going to make an Arduino pH Meter and learn how to measure the pH of a liquid solution using a gravity pH sensor and Arduino. A 16x2 LCD is used to show the pH value on the screen. We will also learn how to calibrate the pH sensor to determine the accuracy of the sensor. So let's get started!

Required Components

Arduino Uno

16*2 Alphanumeric LCD

I2C Module for LCD

Gravity Analog pH sensor

Connecting wires

Breadboard

What is pH Value?

The unit that we use to measure the acidity of a substance is called pH. The term "H" is defined as the negative log of the hydrogen ion concentration. The range of pH can have values from 0 to 14. A pH value of 7 is neutral, as pure water has a pH value of exactly 7. Values lower than 7 are acidic and values greater than 7 are basic or alkaline.

How Does Gravity Analog pH Sensor Work?

Analog pH sensor is designed to measure the pH value of a solution and show the acidity or alkalinity of the substance. It is commonly used in various applications such as agriculture, wastewater treatment, industries, environmental monitoring, etc. The module has an on-board voltage regulator chip which supports the wide voltage supply of 3.3-5.5V DC, which is compatible with 5V and 3.3V of any control board like Arduino. The output signal is being filtered by hardware low jitter.
Technical Features: Signal Conversion Module:

Supply Voltage: 3.3~5.5V

BNC Probe Connector

High Accuracy: ±0.1@25°C

Detection Range: 0~14

PH electrode:

Operating Temperature Range: 5~60°C

Zero (Neutral) Point: 7±0.5

Easy calibration

Internal Resistance: <250MΩ

pH Signal Conversion Board: Pin Description:
V+: 5V DC input

G: Ground pin

Po: pH analog output

Do: 3.3V DC output

To: Temperature output
pH Electrode Construction:
The construction of a pH sensor is shown above. The pH Sensor looks like a rod usually made of a glass material having a tip called "Glass membrane". This membrane is filled with a buffer solution of known pH (typically pH = 7). This electrode design ensures an environment with the constant binding of H+ ions on the inside of the glass membrane. When the probe is dipped into the solution to be tested, hydrogen ions in the test solution start exchanging with other positively charged ions on the glass membrane, which creates an electrochemical potential across the membrane which is fed to the electronic amplifier module which measures the potential between both electrodes and converts it to pH units. The difference between these potentials determines the pH value based on the Nernst equation.
Nernst Equation:
The Nernst equation gives a relation between the cell potential of an electrochemical cell, temperature, reaction quotient and the standard cell potential. In non-standard conditions, the Nernst equation is used to calculate cell potentials in an electrochemical cell. The Nernst equation can also be used to calculate the total electromotive force (EMF) for a full electrochemical cell. This equation is used to calculate the PH value of a solution as well. The glass electrode response is governed by the Nernst Equation can be given as:

E = E0 - 2.3 (RT/nF) ln Q Where Q= Reaction coefficient E = mV output from the electrode E0 = Zero offset for the electrode R = Ideal gas constant= 8.314 J/mol-K T = Temperature in ºK F = Faraday constant = 95,484.56 C/mol N = Ionic Charge

Arduino pH Meter Circuit Diagram

Circuit diagram for this Arduino pH meter project is given below:
Connection of pH Signal Conversion Board with Arduino:
The connection between Arduino and PH signal conversion board is shown in the table below.

Programming Arduino for pH Meter

After successful hardware connections, now it's time for programming the Arduino. The complete code for this pH meter with Arduino is given at the bottom part of this tutorial. The stepwise explanation of the project is given below.

The first thing to do in the program is to include all the required libraries. Here in my case, I have included "LiquidCrystal_I2C.h" library for using the I2C interface of an LCD display and "" for using I2C functionality on Arduino.

#include <Wire.h>
#include <LiquidCrystal_I2C.h>
LiquidCrystal_I2C lcd(0x27, 16, 2);

Next, the calibration value is defined, which can be modified as required to get an accurate pH value of solutions. (This is explained later in the article)

float calibration_value = 21.34;

Inside setup (), LCD commands are written for displaying a welcome message on LCD.

  lcd.init();
  lcd.begin(16, 2);
  lcd.backlight();
  lcd.setCursor(0, 0);
  lcd.print("   Welcome to      ");
  lcd.setCursor(0, 1);
  lcd.print(" Circuit Digest    ");
  delay(2000);
  lcd.clear();

Inside loop(), read 10 sample analog values and store them in an array. This is required to smooth the output value.

for(int i=0;i<10;i++)
 {
 buffer_arr[i]=analogRead(A0);
 delay(30);
 }

Then, sort the Analog values received in ascending order. This is required because we need to calculate the running average of samples in the later stage.

 for(int i=0;i<9;i++)
 {
 for(int j=i+1;j<10;j++)
 {
 if(buffer_arr[i]>buffer_arr[j])
 {
 temp=buffer_arr[i];
 buffer_arr[i]=buffer_arr[j];
 buffer_arr[j]=temp;
 }
 }
 }

Finally, calculate the average of a 6 centre sample Analog values. Then this average value is converted into actual pH value and printed on an LCD display.

for(int i=2;i<8;i++)
 avgval+=buffer_arr[i];
 float volt=(float)avgval*5.0/1024/6;
 float ph_act = -5.70 * volt + calibration_value;
 lcd.setCursor(0, 0);
 lcd.print("pH Val:");
 lcd.setCursor(8, 0);
 lcd.print(ph_act);
 delay(1000);
}

Calibration of pH Electrode

Calibration of the PH electrode is very important in this project. For this, we need to have a solution whose value is known to us. This can be taken as the reference solution for the calibration of the sensor.

Suppose, we have a solution whose PH value is 7 (distilled water). Now when the electrode is dipped in the reference solution and the PH value displayed on LCD is 6.5. Then to calibrate it, just add in the calibration variable "calibration_value" in the code. i.e. make the value 21.34 + 0.5=21.84. After making these changes, again upload the code to Arduino and recheck the pH by dipping electrode in the reference solution. Now LCD should show the correct pH value i.e. 7 (Little variations are considerable). Similarly, adjust this variable to calibrate the sensor. Then check for all other solutions to get the exact output.

Testing Arduino pH Tester

We have tried this Arduino pH meter by dipping it into pure water and Lemon water, you can see the result below.
Pure Water: Lemon Water:
This is how we can build a pH sensor using Arduino and can use it to check the pH level of various liquids.

Complete code and demonstration Video are given below.

#include <Wire.h>
#include <LiquidCrystal_I2C.h>
LiquidCrystal_I2C lcd(0x27, 16, 2);
float calibration_value = 21.34;
int phval = 0;
unsigned long int avgval;
int buffer_arr[10],temp;
void setup()
{
 Serial.begin(9600);
  lcd.init();
  lcd.begin(16, 2);
  lcd.backlight();
  lcd.setCursor(0, 0);
  lcd.print("   Welcome to      ");
  lcd.setCursor(0, 1);
  lcd.print(" Circuit Digest    ");
  delay(2000);
  lcd.clear();
}
void loop() {
 for(int i=0;i<10;i++)
 {
 buffer_arr[i]=analogRead(A0);
 delay(30);
 }
 for(int i=0;i<9;i++)
 {
 for(int j=i+1;j<10;j++)
 {
 if(buffer_arr[i]>buffer_arr[j])
 {
 temp=buffer_arr[i];
 buffer_arr[i]=buffer_arr[j];
 buffer_arr[j]=temp;
 }
 }
 }
 avgval=0;
 for(int i=2;i<8;i++)
 avgval+=buffer_arr[i];
 float volt=(float)avgval*5.0/1024/6;
 float ph_act = -5.70 * volt + calibration_value;
 lcd.setCursor(0, 0);
 lcd.print("pH Val:");
 lcd.setCursor(8, 0);
 lcd.print(ph_act);
 delay(1000);
}

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pH Meter using Arduino Uno and LCD Display

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In this project we are going to make an Arduino pH Meter and learn how to measure the pH of a liquid solution using a gravity pH sensor and Arduino. A 16x2 LCD is used to show the pH value on the screen.

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https://arduinopoint.com/lm35-arduino-project/

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Arduino LM35 Temperature in degrees Celsius and Fahrenheit with LCD

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In this project, we’ll show you how to lm35 interfacing with Arduino and how to program it. Once we successfully lm35 interface with Arduino, We’ll use an Arduino and an LCD 16×2 module to construct a temperature display. The display constantly monitors the temperature surrounding the LM35 measuring…

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Nome do App: Ardutooth
https://drive.google.com/file/d/1BVRFt7rIyNK-loJ0W4JD0RdEtYOTk8xW/view

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How to print any Arduino Sensor values on Phone | Printing Real time temperature on phone using HC05

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To begin, copy and paste this code in to the Arduino IDE or download the attached file.

***************************************************

DFRobot Gravity: Analog TDS Sensor / Meter For Arduino

<https://www.dfrobot.com/wiki/index.php/Gravity:_Analog_TDS_Sensor_/_Meter_For_Arduino_SKU:_SEN0244>

Created 2017-8-22

By Jason <jason.ling@dfrobot.com@dfrobot.com>

GNU Lesser General Public License.

See <http://www.gnu.org/licenses/> for details.

All above must be included in any redistribution

/***********Notice and Trouble shooting***************

1. This code is tested on Arduino Uno and Leonardo with Arduino IDE 1.0.5 r2 and 1.8.2.

2. More details, please click this link: <https://www.dfrobot.com/wiki/index.php/Gravity:_Analog_TDS_Sensor_/_Meter_For_Arduino_SKU:_SEN0244>

****************************************************/

// also used https://www.youtube.com/watch?v=5Dp-XatLySM

#include <OneWire.h>

#include <DallasTemperature.h>

#include <SD.h>

#include <SPI.h>

#include "DFRobot_PH.h" //added from PH Code

#include <EEPROM.h> //Added from PH Code

File myFile;

#define ONE_WIRE_BUS 7

#define TdsSensorPin A1 //verify plugged in here

#define PH_PIN A2 //

#define VREF 5.0 // analog reference voltage(Volt) of the ADC 0ther option is 3.3/5.0

#define SCOUNT 30 // sum of sample point

int analogBuffer[SCOUNT]; // store the analog value in the array, read from ADC

int analogBufferTemp[SCOUNT];

int analogBufferIndex = 0,copyIndex = 0;

int pinCS = 53; // Pin 10 on Arduino UNO

int sensorPin = A0; //added from turbidity

float averageVoltage = 0,tdsValue = 0,temperature = 25;

float voltage,phValue;

float Celcius=0;

float Fahrenheit=0;

float volt; //added from turbidity

float ntu; //added from turbidity

DFRobot_PH ph;

OneWire oneWire(ONE_WIRE_BUS);

DallasTemperature sensors(&oneWire);

void setup()

{

Serial.begin(115200);

ph.begin(); //added from ph

sensors.begin();

pinMode (pinCS, OUTPUT);

pinMode(TdsSensorPin,INPUT);

// SD Card Initialization

if (SD.begin())

{

Serial.println("SD card is ready to use.");

} else

{

Serial.println("SD card initialization failed");

return;

}

}

void loop()

{

volt = 0; //beginning of added from turbidity

for(int i=0; i<800; i++)

{

volt += ((float)analogRead(sensorPin)/1023)*5;

}

volt = volt/800;

volt = round_to_dp(volt,1);

if(volt < 2.8){

ntu = 1540;

}else{

ntu = -576.12*square(volt)+3393.2*volt-3443.2;

// ntu = -1120.4*square(volt)+5742.3*volt-4353.8;

} //end of added from turbidity

static unsigned long analogSampleTimepoint = millis();

if(millis()-analogSampleTimepoint > 40U) //every 40 milliseconds,read the analog value from the ADC

{

analogSampleTimepoint = millis();

analogBuffer[analogBufferIndex] = analogRead(TdsSensorPin); //read the analog value and store into the buffer

analogBufferIndex++;

if(analogBufferIndex == SCOUNT)

analogBufferIndex = 0;

}

static unsigned long timepoint = millis(); //added from ph

if(millis()-timepoint>1000U){ //time interval added from ph

timepoint = millis(); //added from ph

voltage = analogRead(PH_PIN)/1024.0*5000; // read the voltage

phValue = ph.readPH(voltage,temperature); //added from ph

}

static unsigned long printTimepoint = millis();

if(millis()-printTimepoint > 800U)

{

printTimepoint = millis();

for(copyIndex=0;copyIndex<SCOUNT;copyIndex++)

analogBufferTemp[copyIndex]= analogBuffer[copyIndex];

averageVoltage = getMedianNum(analogBufferTemp,SCOUNT) * (float)VREF / 1024.0; // read the analog value more stable by the median filtering algorithm, and convert to voltage value

float compensationCoefficient=1.0+0.02*(temperature-25.0); //temperature compensation formula: fFinalResult(25^C) = fFinalResult(current)/(1.0+0.02*(fTP-25.0));

float compensationVolatge=averageVoltage/compensationCoefficient; //temperature compensation

tdsValue=(133.42*compensationVolatge*compensationVolatge*compensationVolatge - 255.86*compensationVolatge*compensationVolatge + 857.39*compensationVolatge)*0.5; //convert voltage value to tds value

// Serial.print("voltage:");

// Serial.print(averageVoltage,2);

//Serial.print("V ");

sensors.requestTemperatures();

Celcius=sensors.getTempCByIndex(0);

Fahrenheit=sensors.toFahrenheit(Celcius);

//Serial.print(compensationCoefficient);

//Serial.print("compensationCoefficient");

//Serial.print(temperature);

//Serial.print("temperature");

Serial.print(millis());

Serial.print("time since start in milliseconds");

Serial.print(" C ");

Serial.print(Celcius);

Serial.print(" F ");

Serial.println(Fahrenheit);

Serial.print(volt );

Serial.print(" V ");

Serial.print(ntu );

Serial.print(" NTU ");

Serial.print("pH:");

Serial.print(phValue);

Serial.print(" TDS Value:");

Serial.print(tdsValue,0);

Serial.println("ppm");

}

ph.calibration(voltage,temperature); // calibration process by Serail CMD

{

myFile = SD.open("test.txt", FILE_WRITE);

if (myFile){

myFile.print(millis());

myFile.print(",");

myFile.print(Celcius);

myFile.print(",");

myFile.print(Fahrenheit);

myFile.print(",");

myFile.print(phValue);

myFile.print(",");

myFile.print(volt);

myFile.print(",");

myFile.print(ntu);

myFile.print(",");

myFile.println(tdsValue,0);

//myFile.print(",");

myFile.close(); // close the file

}

//if the file didn't open, print an error:

else {

Serial.println("error opening test.txt");

}

} delay (300000); // use 500 for short term troubleshooting/lab testing,300000 for field testing, 300,000 milliseconds=5 minutes, 500 milliseconds=0.5 second

}

float round_to_dp( float in_value, int decimal_place ) //begin

{

float multiplier = powf( 10.0f, decimal_place );

in_value = roundf( in_value * multiplier ) / multiplier;

return in_value;

}

int getMedianNum(int bArray[], int iFilterLen)

{

int bTab[iFilterLen];

for (byte i = 0; i<iFilterLen; i++)

bTab[i] = bArray[i];

int i, j, bTemp;

for (j = 0; j < iFilterLen - 1; j++)

{

for (i = 0; i < iFilterLen - j - 1; i++)

{

if (bTab[i] > bTab[i + 1])

{

bTemp = bTab[i];

bTab[i] = bTab[i + 1];

bTab[i + 1] = bTemp;

}

}

}

if ((iFilterLen & 1) > 0)

bTemp = bTab[(iFilterLen - 1) / 2];

else

bTemp = (bTab[iFilterLen / 2] + bTab[iFilterLen / 2 - 1]) / 2;

return bTemp;

}

# data[].attributes.attachment_link.description
Arduino Water Quality Monitoring System: This guide will show you how to build, program, and deploy an environmental monitoring system that can monitor the Total Dissolved Solids (TDS), Temperature, pH, and Turbidity of a body of water and record the data to an SD card. This project has ap...