<figure><img src="image_1.png" /></figure>

# Development of a flow analysis system based on an electrochemical sensor for high frequency on site monitoring of cobalt and nickel in a river water

Corinne Parat* (1), Estelle Ricard, Jordan Lacoste, Hervé Pinaly, Séverine Le Faucheur, Isabelle Le Hécho

Université de Pau et des Pays de l'Adour, CNRS, IPREM, 2 avenue Pierre Angot, Pau 64000, France

## ARTICLE INFO

### Keywords:

- On site analysis

- Electrochemical sensor

- Monitoring

- River water

- Trace metals

## ABSTRACT

A conventional 3-electrode system comprising a working electrode, a reference electrode and a counter electrode has been transformed into an all-in-one sensor for use in a flow measurement system for on-site trace metal monitoring. Its design has been thought carefully not only to facilitate handling and maintenance, but also to modify the surface of the working electrode to adapt it to the target to be detected. For this reason, a two-part sensor has been developed, comprising a main part and a removable part on which the working electrode and the counter electrode are stencilled. The working electrode surface was modified with a bismuth film electrode for simultaneous detection of Co and Ni by linear scan voltammetry (LSV). In parallel, a flow analysis system and a Python code were developed for on-site analysis and data processing. The system was first optimised in the laboratory, then experiments were carried out on the pilot rivers of Lacq in France. This campaign demonstrated the robustness of the sensor in a real environment, its stability over a 24-hour period and its sensitivity to variations in concentration from 5 to 60 µg L⁻¹ of trace metals in a river water. Additionally, it highlighted a significant temperature effect on the cobalt signal in particular. This campaign also highlighted several specific short-lived events that wouldn't have been observed under conventional monitoring based on spot analyses.

## 1. Introduction

Water resources, whether surface or groundwater, are under increasing quantitative and qualitative stress from human activities. The various forms of pollution resulting from human lifestyles and consumption patterns, and the worrying synergies that can occur in wastewater due to the wide range of pollutants (pathogens, emerging substances, metals, etc.), represent major threats to water, with significant risks for human health and the environment [1]. At the regulation level, the monitoring of aquatic environments is mainly carried out by taking punctual samples of water, which are then sent to an accredited analysis laboratory. Current analytical techniques are expensive and the incompressible time between sampling and the analysis report do not allow continuous monitoring of the water, even though many contaminants are listed as priority substances under the Water Framework Directive [2]. In the case of urban stormwater, which can also be contaminated by a wide range of substances whose concentrations vary considerably from place to place, as well as between and during rainfall events, characterization of pollutant concentrations is only be possible

through the use of indirect measurements with site-specific relationships of pollutants to basic physical-chemical parameters [3]. In view of the health, environmental and economic issues related to this problem, metrological tools adapted to the monitoring of polluting substances in time and space are essential [4].

Although progress has been made in the detection of contaminants in the aquatic environment under laboratory conditions, major efforts are still required to achieve on-site application [5-7]. Indeed, there are a number of portable systems on the market for in situ analysis of metals (Klearia, Metrohm, Palintest, Trace2O), or ELISA kits dedicated to the analysis of pesticides (Abraxis, Biosense, Novakits). However, none of them can yet be integrated into a monitoring system. Today, only the IDRONAUT VIP system [8] and the TracMet system [9] allow in situ and high-frequency monitoring of metals, but there are more suitable for research work than for monitoring aquatic environments. Most current detection technologies are not suitable for continuous monitoring because they are either single use, or require specific reagents and conditions of use (temperature, pH), or even technical expertise for operation of maintenance, which considerably increases the cost of

<footnote>* Corresponding author.
E-mail address: corinne.parat@univ-pau.fr (C. Parat).</footnote>
environmental monitoring [7]. According to Ateia et al. (2024), the remaining scientific challenges in bringing these technologies to the field include improving sensitivity, selectivity, robustness, and performance in real aquatic environments [6]. From our point of view, there are two main reasons for the lack of such a technology. The first is the difficulty of designing a robust and sensitive sensor for the targets of interest, which can be easily handled by unskilled personnel. The second reason is the cost of the sensor and its maintenance. Indeed, whatever the surface modification used, the surface (and/or the film deposited on it) will probably have to be renewed in the medium or long term in order to maintain a sufficient level of sensitivity for trace element analysis.

The challenge of this work was therefore to design a robust sensor for on-site monitoring, paying attention to its performance but also to its cost and ease of maintenance. Among the available analytical techniques, only a few approaches allow the implementation of suitable in situ devices in terms of analytical performance, autonomy, miniaturization feasibility, and ease of transport [10]. Due to their high sensitivity, electrochemical techniques, such as anodic stripping voltammetry and stripping chronopotentiometry using disposable sensors, have proven to be promising approaches for the analysis of metals in freshwater [11]. Among one single-use sensors, screen-printed electrodes have been increasingly studied due to their performance and ability to be mass-produced at very low costs [12–15]. In addition, their surface can be easily modified, which has led to different improvements in the final sensor, such as increased selectivity and/or sensitivity [16–21].

Taking all these criteria into account, we met this challenge by transforming a conventional 3-electrode system into an all-in-one sensor, featuring a customizable and easily replaceable working electrode, for use in a flow measurement system for on-site trace metal monitoring. The first part of this work focused on modifying the 3-electrode system to provide a robust and easy-to-handled sensor that could be used for sensitive methods such as voltammetry, with low maintenance costs. The second part was devoted to optimising the sensor for applications in a flow system. For this purpose, Co and Ni, two elements critical to low-carbon technologies driven by the energy transition, were selected. In a final part, the results of a one-month on site campaign were presented.

## 2. Experimental

### 2.1. Chemicals and reagents

A screen-printable carbon commercial ink (Loctite® EDAG PF-407A E&C), designed for low voltage circuitry on polyester film and solvent-sensitive substrate such as polycarbonate, was purchased from Henkel Technologies France. Acetic acid (CH₃COOH, Trace select), sodium acetate trihydrate (CH₃COONa, Trace select) were obtained from Fluka. Ammonia solution (NH₃, 28–30 %, Emsure® for analysis) and potassium hexacyanoferrate (K₃Fe(CN)₆) were obtained from Supelco. Hydrochloric acid (HCl, 37 %, ACS reagent) was obtained from Sigma Aldrich. Nitric acid (HNO₃, 69–70 %, Baker Instra-Analysed for trace metal analysis) and sodium hydroxide (NaOH, Baker Analysed) were obtained from J.T. Baker. Ethanol (99.8 %, HiPerSolv Chromanorm®, HPLC gradient grade) was purchased from VWR. Dimethylglyoxime (DMG) was purchased from Acros Organics. Bismuth (Bi), cobalt (Co), nickel (Ni), copper (Cu), lithium (Li) and manganese (Mn), all for ICP-AES/MS standard was purchased from SCP SCIENCE. Ultrapure water (resistivity of 18 MΩ cm) was used for dilutions. All the optimisations were performed by using either a milliQ-water or a commercial mineral water (Cristaline) with the following composition: calcium 68 mg L⁻¹, potassium 11 mg L⁻¹, magnesium 2 mg L⁻¹, sodium 21 mg L⁻¹, bicarbonates 219 mg L⁻¹, chlorides 28 mg L⁻¹, sulphates 39 mg L⁻¹, nitrates 1 mg L⁻¹, pH 7.6, conductivity 476 µS cm⁻¹ (equivalent to 285 mg L⁻¹). Acetate buffer solution (0.1 mol L⁻¹, pH 4.6) was prepared by mixing appropriate amounts of CH₃COOH and CH₃COONa. Ammonia buffer

solution (0.1 mol L⁻¹, pH 9.7) was prepared by mixing appropriate amounts of ammonia solution and HCl. Dimethylglyoxime (DMG) 0.1 mol L⁻¹ was prepared by dissolving 58 mg of DMG in 5 mL of ethanol.

### 2.2. Experimental section

#### 2.2.1. Analytical equipment

Cobalt and Ni concentrations were measured with an inductively coupled plasma atomic spectrometer (ICP-AES) (ICAP 6005, Thermo-Fisher Scientific) or an inductively coupled plasma mass spectrometer (ICP-MS) (Agilent 7500, Agilent Technologies). Electrochemical measurements were performed with an Origaflex 10 A running on Origamaster 5 from Origalys Electrochem SAS.

#### 2.2.2. Electrochemical sensors

The development of this sensor was based on the 3-electrode system used in the laboratory consisting of a reference electrode (RE) and a counter electrode (CE) from Origalys Electrochemistry SAS, and a screen-printed carbon (SPCE) as working electrode (WE) that can be easily modified to adapt to the target to be detected [17,21]. The sensor consists of a WE with a surface area of 9.6 mm² and a CE manually stenciled onto the sensor head, as well as an AgCl reference electrode inside the sensor. The stencilled head was dried for 1 hour at room temperature, then cured for 1 hour in an oven at 60°C before use. A full description of the sensor is given in the discussion section.

#### 2.2.3. Electrochemical analysis methods

Simultaneous detection of Co and Ni after complexation with dimethylglyoxime (DMG) has been widely reported on a mercury film electrode [22–25] and on a Bi film electrode [26–29]. Whatever the working electrode surface, the DMG appeared to be the most suitable ligand for detecting Co or Ni. However, as the pKa of DMG is 10.1, higher pH values favor its complexation with both metals. That is the reason why, the detection of Co and Ni were performed in ammonia buffer at pH 9.7. In addition, as the objective here was to implement on-site monitoring, a Bi film electrode was preferred to a mercury film electrode due to the high toxicity of mercury. The surface modification of the WE by a Bi film was carried out using a 0.1 mol L⁻¹ acetate buffer solution (pH 4.6) containing Bi at a concentration of 100 mg L⁻¹. The Bi film was deposited by chronoamperometry applying a potential of –0.8 V for 1000 s with stirring. The sensor was then washed with deionized water and stored under nitrogen before use.

After several optimisation tests, Co and Ni measurements were performed in a solution buffered at pH 9.7 with ammonia buffer containing 0.5 mmol L⁻¹ DMG. Although electrochemical pH control offers the possibility of dynamically adjusting the local pH based on bulk measurements, we chose to fix the pH at 9.7 using a chemical buffer. This approach ensures stable complexation conditions and minimizes experimental variability, particularly in the context of field applications where system simplicity and reliability are crucial.

Simultaneous detection of Ni and Co was achieved in 3 steps: a 10 s cleaning step at –1.3 V, an adsorption step at –0.8 V for 60 s, and a stripping step using linear scan voltammetry (LSV) from –0.87 to –1.2 V at 200 mV s⁻¹. Before each analysis, a cyclic voltammetry (CV) from 0.1 V to –0.2 V at 100 mV s⁻¹ (2 scans) was carried out in order to completely remove any compounds adsorbed on the surface of the working electrode.

Electrochemical impedance spectroscopy was used to characterise the two kinds of electrodes, the SPCE and the sensor. Experiments were recorded in 0.1 mol L⁻¹ acetate buffer and 5 mmol L⁻¹ FeK₃(CN)₆ with a frequency range from 10 kHz to 50 mHz. The surfaces of different electrodes were studied: SPCE, Bi film modified SPCE (Bi-SPCE), sensor (stencilled sensor), Bi film modified sensor (Bi-stencilled sensor).
### 2.2.4. Surface characterisation

Surface characterisations were performed by means of an Apreo 2 field emission gun scanning electron microscope (SEM, Apreo 2 FEG) from Thermo Fisher Scientific equipped with a XFlash 6–100 energy dispersive detector (XFlash 6–100 EDS) from Brucker. This technique was used to produce images of the working surface electrode of the sensor, before and after Bi film modification, and before and after Co and Ni monitoring.

### 2.2.5. Flow analysis system

The flow analysis system contains a sensor, a pH sensor and a temperature sensor (Figure S1). A Chauvin Arnoux data logger L452 connected to the SYCLOPE INDIG'O controller was used to monitor temperature using a CAT2905 glass temperature sensor and pH using a CAA2503 glass pH sensor from Sycope Electronique. The sensor was connected to OrigaFlex potentiostat. A ProMinent Dulcoflex DF4A peristaltic pump (pump 1) was used to sample the analysis solution through a 4 mm tube connected to an inner filter to prevent the aspiration of large particles when natural waters were analysed. An Ismatec REGLO Digital 4-channel tubing pump (pump 2) was also used to buffer the analysis solution at pH 9.7 and to add DMG for Co and Ni detection. After analysis, all the solutions were collected in a waste tank for further treatment.

### 2.2.6. Optimisation

Various parameters were optimised for on-site analysis in order to obtain a stable signal over time: the DMG concentration from 0.13 to 1.5 mmol L⁻¹, representing a pump 2 flow rate from 20 to 240 mL h⁻¹, the flow rate of pump 1 from 0.2 to 1.2 L h⁻¹, the deposition time from 30 s to 900 s, and the electrochemical procedure. All optimisations were performed in a milliQ-water solution doped with Co and/or Ni at 30 µg L⁻¹, using the flow analysis system. The solution to be analysed was injected using pump 1, and ammonia buffer and DMG were added to the solution using pump 2.

### 2.3. On site analyses

On-site analyses were carried out at the pilot rivers (Pôle d'Etudes et de Recherche de Lacq, TotalEnergies One Tech) in France, an open-air laboratory consisting of 16 artificial canals filled with the Gave de Pau river (Fig. S2A). For the 28-day experiment, 12 canals (length 40 m, width 0.5 m, depth 0.5 m, flow rate 7.5 m³ h⁻¹) were randomly assigned to 4 Co and Ni concentrations (0, 6, 30 and 60 µg kg⁻¹) in triplicates. All on-site monitoring equipment (flow system device, computer, and potentiostats) were placed under a tent at the end of the canal (Fig. S2B). The Co(II) and Ni(II) injection was carried out at the start of canal (Fig. S3A). A 30 m sampling line, with an inlet filter to limit clogging of the flow analysis device, was used so that all the canal could be collected under same conditions (Fig. S3B). Cobalt and Ni analyses were performed using the flow system shown in Fig. S1, under optimized conditions. The sensor was connected to the Origalys potentiostat. Water was sampled at a flow rate of 800 mL h⁻¹ and simultaneously, DMG was added to the sampled water using the pump n°2 at a flow rate of 80 mL h⁻¹. The two solutions were mixed in the cell containing the temperature sensor, before reaching the analysis cell. All the water that passed through the system was stored in a waste tank for further treatment. On-site maintenance is described in the supplementary information.

### 2.4. Data treatment

The Co and Ni analysis program, described in the electrochemical method section, was repeated without interruption between sensor head changes. A Python program was developed to process the large volume of data generated. The complete Python code is provided in supplementary information. A set of data is available on the French

government's data repository [30]. Python's libraries and tools enable complex data to be processed efficiently. The key steps are as follows:

1. Function read and process file (Section 2.1 of Python code): this function is used to collect all the data from the CRV files generated by the software of the potentiostat Origalys and are required for the data treatment: the dataframe, called df, contains the voltammogram data (potential and current values), date_exp is the time of the experiment and fname is the name of the file. To make voltammograms easier to read, all the currents are presented in absolute values.

2. Function Baseline_arPLS for baseline correction (Section 2.2 of Python code): this stage involves correcting the signal to make it suitable for further analyses. The baseline correction is performed by the asymmetrically reweighted penalized least squares smoothing (arPLS) [31]. Three parameters have to be adjusted:

- lambda (lam) to adjust the smoothings of the baseline. It can be set between 10 and $10^6$. The greater lambda, the greater the smoothing of the baseline.

- Ratio: is the weight deviation that can be set between 0 and 1. The closer the value is to zero, the fewer negative values there are.

- number of iterations (niter): is the maximum number of iterations allowed.

These parameters are first tested on one file and adjusting accordingly (Section 3.1 of Python code).

3. Additionally, the Savitzky Golay filter can be used to smooth the data and reduce noise. This function is available in the module signal in the SciPy library. Two parameters have to be adjusted, the window lengths that is the length of the filter window and the polyorder that is the order of the polynomial used to fit the data. (Section 3.2 of Python code).

4. Path to folder containing CRV files (section 4.1 of Python code): this code is used to specify the path to the folder where the files are located.

5. Creation of a loop to merge all the data into a single dataframe (section 4.2 of Python code): during this step, a loop is created to read each file, correct the baseline and smooth the data, and to determine the height of the Co and Ni peaks. Finally, the voltamgram data are collected in a single dataframe (common DF) and the peak heights and peak potentials in another dataframe (common DF peak). This step enables the data from one day's analysis (900 files) to be brought together in a common dataset in around 30 seconds.

6. Search for Ni and Co outliers using the interquartile range method (sections 4.3 and 4.4 of Python code): this stage allows to remove the outliers due, for example, to the passage of a bubble at the time of measurement.

7. Data visualization and reporting (Section 5 of Python code): finally, the results of the data analysis are presented in a format that is easily understandable and actionable. This may involve creating visualizations, reports, or dashboards that highlight key findings and trends in the data.

### 2.5. Performances

The linearity and the sensitivity were obtained from the calibration curve based on 3 replicate analyses for each standard concentration. The statistical function scipy.stats (stats.linregress(x,y)) in the SciPy library of Python was used to calculate the linear least-squares regression and determine the slope (m), the intercept, the Pearson correlation coefficient (r), the coefficient of determination ($R^2 = r^2$), and the standard error of the estimated intercept (Sb). The limit of detection (LOD) and the limit of quantification (LOQ) were calculated from the calibration curve according to Eqs. (1) and (2):
$$LOD = 3 \times S_b/m \qquad (1)$$

$$LOQ = 10 \times S_b/m \qquad (2)$$

### 3. Results and discussion

#### 3.1. Design of the sensors

The first objective of this task was to design an electrochemical sensor, which requires 3 electrodes (WE, CE and RE), in order to achieve concentrations in the µg/L range. Developing such a sensor posed two major difficulties. The first concerned the arrangement of these 3 electrodes on a single support, which must allow easy handling and maintenance of each electrode by non-specialist personnel. The second difficulty was related to the conditions of use of the sensor. Prolonged immersion of the sensor is likely to generate drift of the reference electrode, a well-known phenomenon due to the release of chloride ions into solution, and drift of the working electrode due to degradation of its surface. To overcome these difficulties, a two-part sensor was developed, consisting of a main part to which a removable part is screwed, (Fig. 1A) [32]. The main part is a plastic tube containing 3 electrical connectors. One of these is connected to a silver (Ag) ring coated with AgCl, which forms the RE, while the other two are the electrical connectors for the WE and CE. The removable part consists of 3 pieces: (i) a reservoir for the electrolyte reference (i.e. KCl gel) with a small hole covered by a flexible plastic ring to allow ion exchanges with the aqueous medium and prevent gel leakage; (ii) a head on which the WE and CE are stencil-printed and into which spring contacts (pogo pins) are inserted to allow electrical connection between the WE and CE and the connectors of the main part, and (iii) a threaded ring which surrounds the head and is screwed onto the reservoir. This design allows easy and inexpensive replacement of the WE and CE in the event of loss of sensor sensitivity or signal degradation due to electrode damage. This extends the life of the sensor and its manufacturing cost, which is relatively high (around 2000 €) because of the materials used (Ag for the RE and gold-plated copper wire for the electrical connectors).

The principle of the reference electrode is the same as that of a conventional electrode, except that the Ag/AgCl wire has been replaced by an Ag/AgCl ring. The silver ring is coated with AgCl to maintain a stable electrode potential. This electrode is immersed in a 3 M KCl gel. The contact between the reference electrode and the solution being analysed is made via a small hole covered by a flexible plastique ring (Fig. 1A).

#### 3.2. Characterization of the sensor surface

To assess the impact of electrode design on electrical behaviour, the

WE of the sensor was compared with a laboratory SPCE. Scanning electron microscopy (SEM) observations revealed no significant morphological differences between the two WEs (data not shown). Before Bi electrodeposition, SEM images clearly show the heterogeneous structure of the carbon ink, composed of graphite sheets and carbon nanoparticles (Fig. 2A), confirmed by EDS analysis (Fig. 2C). After Bi electrodeposition, the carbon surface appears to be well covered by a continuous film of Bi (Fig. 2B). This homogeneity was confirmed by EDS analysis (Fig. 2D), which revealed the presence of Bi over the entire electrode surface, confirming the reproducibility and consistency of the modification protocol.

On the other hand, the electrochemical impedance spectroscopy study, before and after Bi film electrodeposition, showed clear differences between the sensor surface and the SPCE used in a classical 3-electrode system (Fig. 3). Nyquist plot obtained with the two systems showed that two parameters influence impedance: the thickness of the working electrode and the arrangement of the three electrodes. Randles' equivalent circuits were used to fit the electrical parameters of the electrochemical impedance spectroscopy results for each system (Table 1). These circuits include the resistance of the electrolyte between the WE and the RE (R<sub>S</sub>), the double layer capacitance (C<sub>dl</sub>) generated at the electrode-electrolyte interface, and the charge transfer resistance (R<sub>CT</sub>) caused by diffusion of ions from the electrolyte to the interface [33].

A very smaller R<sub>S</sub> value, corresponding to the starting point Z, was obtained with the sensor (between 100 and 200 Ω) in comparison with the SPCE (6617 Ω) (Table 1). This value depends on the resistance of the electrolyte support but also the distance between the working electrode and the auxiliary electrode, which are very close together in the sensor (Fig. 1C) compared with a conventional-electrode system.

A higher R<sub>CT</sub> value was obtained with the sensor despite the gold-coated electrical contacts. In fact, this part was found to be extremely sensitive to corrosion. To counteract this, the carbon ink was deposited by stencil rather than screen-printing, leading to a thicker layer of ink to protect the electrical contacts from corrosion. The electrodeposition of a Bi-based conductive film on the WE improves charge transfer, reducing R<sub>CT</sub> value from 49.8 to 9.1 kohm for the Bi-stencilled sensor, and from 9.7 to 3.70 kohm for the Bi-SPCE. This result suggests that the Bi particles are non-only deposited on the surface of the WE but also in the carbon ink, leading to a lower resistance of the WE, regardless of the electrode (SPCE or stencilled sensor).

The double-layer capacitance, C<sub>dl</sub>, results from charge being stored in double layer at the interface. Higher values were obtained for SPCE than for stencilled sensor (Table 1). These differences can be explained by the sensor design as the shape and the surface of the working surface, or the position of the reference electrode.

These great variations between the two electrochemical systems

<figure><img src="image_2.png" /><figcaption>Fig. 1. A - Photo of the electrochemical sensor, B - Head with electrical connector in gold-coated copper wire, and C - WE (black disc - 9.6 mm²) and CE (black crescent) stencil-printed on the sensor head.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Fig. 2. MEB images of: A - Unmodified carbon surface of the head with carbon nanoparticles and graphite sheets and B - carbon surface modified by a Bi film (Magnification: 10 000). And analysis by SEM-FEG Apreo 2 @TFS/EDS XFlash 61100 @Bruker of C-unmodified surface and D-carbon surface modified by a Bi film.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 3. Nyquist plots of impedance spectra (with Zi, the imaginary part and Zr the real part of impedance Z) performed on SPCE (green dot) and sensor where the carbon ink was stenciled (red dot), and on Bi-SPCE (yellow dots) and Bi-stenciled sensor (blue dots). Analytical conditions: acetate buffer solution, K3Fe(CN)6 5 mmol L-1, frequency from 10 kHz to 50 mHz.</figcaption></figure>

(stenciled sensor vs conventional 3-electrode system) shows all the difficulties to be overcome in grouping 3 electrodes on a single support. The relative position of the electrodes has a significant impact on the interaction between the working electrode and the electrolyte and therefore on the RCT value, as well as on the electrochemical processes that occur at the electrode/solution interface and therefore on the Cdl value.

In addition, the heterogeneous rate constant k0 was estimated using Eq. (3), based on the classical expression derived from electrochemical

impedance spectroscopy for a simple electron-transfer reaction [34]:

$$k^0 = \frac{RT}{n^2F^2R_{CT}A} \qquad (3)$$

where R is the gas constant, T the temperature (298 K), n the number of electrons involved in the redox process (n = 1), F the Faraday constant, and A the geometrical area of the working electrode (9.6 mm²). The standard rate constants (k0) for Bi film modified electrodes are significantly higher than that of unmodified carbon electrodes, making them
<caption>**Table 1**</caption>

Randles's values and heterogeneous electron transfer rate constants ($k^0$) obtained for the two electrodes: SPCE where the carbon ink was deposited by screen-printing and stenciled electrode (stenciled sensor) where the carbon ink was deposited by stencil printing.

<table><thead><tr><th></th><th>R<sub>s</sub> / ohm</th><th>R<sub>GT</sub> / ohm</th><th>C<sub>dl</sub> / nF</th><th>k<sup>0</sup> / cm s<sup>-1</sup></th></tr></thead><tbody><tr><td>SPCE</td><td>6617</td><td>9647</td><td>656.8</td><td>5.8 &times; 10<sup>-5</sup></td></tr><tr><td>Bi-SPCE</td><td>9665</td><td>3699</td><td>430.3</td><td>1.5 &times; 10<sup>-4</sup></td></tr><tr><td>Stenciled sensor</td><td>194</td><td>49753</td><td>159.9</td><td>1.1 &times; 10<sup>-5</sup></td></tr><tr><td>Bi-stenciled sensor</td><td>110</td><td>9119</td><td>174.5</td><td>6.1 &times; 10<sup>-5</sup></td></tr></tbody></table>

more efficient in electrochemical reactions, results which are consistent with the known behaviour of Bi-modified electrodes [35]. This parameter primarily reflects the general electron transfer kinetics of the Bi-modified surface when interacting with outer-sphere redox systems such as ferri/ferrocyanide. However, the detection of Co and Ni in the present system relies on the adsorption of their DMG complexes followed by their electrochemical reduction. As such, the sensor performance is governed more by surface affinity and adsorption efficiency than by diffusion-controlled electron transfer kinetics. Therefore, while the relatively low $k^0$ value confirms the moderate activity of the Bi film toward outer-sphere probes, they are not directly predictive of the sensor's performance for Co and Ni detection.

### 3.3. Optimisation of Co and Ni detection with sensor in laboratory

#### 3.3.1. DMG concentration optimisation

Fig. 4 shows the variation in the Co and Ni signals for a concentration of 30 µg L⁻¹ as a function of the variation in DMG flow rate from 20 to 240 mL h⁻¹, controlled by pump n°2, which corresponds to a DMG concentration variation from 0.125 to 1.5 mmol L⁻¹. As expected, increasing the DMG flow rate induces a higher DMG concentration and consequently a higher signal as more complexes are formed. The Ni signal stabilises for a DMG flow rate greater than 80 mL h⁻¹, corresponding to a DMG concentration of 0.5 mmol L⁻¹. In contrast, the height of Co peak increases almost linearly up to a DMG concentration of

<figure><img src="image_5.png" /></figure>

Fig. 4. Voltammograms obtained for a Co and Ni concentration of 30 µg L⁻¹ in an ammonia buffer (pH 9.7) with DMG flow rate from 20 to 240 mL h⁻¹. Electrochemical parameters: conditioning step 10 s at -1.3 V, deposition step 120 s at -0.8 V, stripping in LSV from -0.8 V to -1.3 V at 200 mV s⁻¹. The line shadow represents the 95 % confidence interval for each flow rate. The pumps were never stopped for the duration of the analysis.

1 mmol L⁻¹. A similar trend is observed when the analysis is performed in a conventional electrochemical cell, with a stabilisation of the Ni signal from 0.6 mmol L⁻¹, while the Co signal still increases at a DMG concentration of 2 mmol L⁻¹ (not shown). It is interesting to note that this behaviour has been already observed by various authors, whatever the substrate of the working electrode [25,26,36,37]. In aquatic media, Co²⁺ is the main form due to the extremely low solubility of Co³⁺ [38], suggesting a metal/ligand ratio of 1:2, as for Ni. The study of the molecular configuration of several DMG-derived complexes, including those of Co and Ni, showed that the bond length, bond angle and chelate configuration were all different, allowing specific recognition of one of these complexes by a molecular imprinted polymer [39]. This explains why Co(DMG)₂ and Ni(DMG)₂, behave differently even though Co and Ni are similar metals. They probably don't bind to the same adsorption sites and, unlike Co(DMG)₂, saturation adsorption sites of Ni(DMG)₂ occurs rapidly. A flow rate of 80 mL h⁻¹, corresponding to a DMG concentration of 0.5 mmol L⁻¹, was therefore chosen as the best compromise between the quantities of DMG used and the signal intensity.

#### 3.3.2. Deposition time optimisation

The concentrations to be detected in the different artificial canals range from 6 to 60 µg L⁻¹. The deposition time was therefore optimised for the lowest concentration. Fig. S4 shows that Co signal linearly increases when increasing deposition time, while a plateau is rapidly reached for Ni. It appears that all the analytical conditions that favour the accumulation of the complex, such as longer accumulation times or a higher DMG concentration, improve the adsorption of the Co(DMG)₂ complex compared with the Ni(DMG)₂ complex. Given the Co and Ni concentrations in the artificial canals, a deposition time of 60 s has been selected for the subsequent experiments.

#### 3.3.3. Flow rate optimisation

Fig. 5 shows the variation of Co and Ni analytical signals as a function of flow rates from pump n°1, which controls water sampling. In this experiment, DMG was directly added to the Co and Ni solution to keep its concentration constant as the flow rate increased. As expected, increasing the flow rate results in a higher mass-transfer coefficient and therefore a higher signal as more compounds reach the electrode surface. A rapid stabilisation of the Ni signal from a flow rate of 1000 mL h⁻¹ was observed, indicating saturation of the adsorption sites of the Ni-DMG complexes. Poorer repeatability, resulting in a wider range of points, and higher waste production are also observed at higher flow rates. This poor repeatability can be explained by the fact that the pump is never stopped during the stripping step, which leads to disturbances during measurement. Higher flow rates can also lead to the formation of bubbles that disrupt detection. Since the aim here was to carry out high-frequency analysis, which meant keeping pump n°1 running, a flow rate of 800 mL h⁻¹ was considered as the best compromise between the quantities of waste produced and the intensity of the signal.

#### 3.3.4. Optimising the analytical procedure

The stability of the sensor over time is a prerequisite condition for on-site analysis. In order to limit the amount of DMG used during the experiment, the analytical procedure initially consisted of 10 minutes of analysis every 50 minutes of pause. A linear decrease of Co and Ni signals with repetitions and a bad repeatability were obtained during the analysis phases (Fig. S5). This behaviour suggests a strong accumulation of reaction products on the working surface during the pause, leading to artificially high signal intensity when analysis resumes, and consequently to poor repeatability and stability of the measurements. To avoid this trend, the experiment was carried out without pauses.

Fig. 6 shows the evolution of Co and Ni signals over time under two analytical procedures. First of all, the LSVs were carried out continuously without interruption (empty marks). A rapid decrease in the Co
<figure><img src="image_6.png" /><figcaption>Fig. 5. Variation of the peak current intensity in absolute values of Co (orange circles) and Ni (blue squares) with the flow rate. Inset figure: voltammograms obtained for each flow rate with Co peak at -1.18 V and Ni peak at -1.05 V. Analytical conditions: Co and Ni concentrations 30 µg L⁻¹, ammonia buffer pH 9.7, DMG concentration 0.5 mmol L⁻¹. Electrochemical parameters: 10 s at -1.3 V, 60 s at -0.8 V, stripping in LSV from -0.8 V to -1.3 V.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 6. Variation of Co (circle marks) and Ni (square marks) signals according to the treatment between each measurement: empty marks: without CV, full marks: with 2 CV scans from 0.1 to -0.2 V at 100 mV/s between each measurement. Analytical conditions: Co and Ni concentrations 30 µg L⁻¹, flow rate 0.8 L h⁻¹, 10 s at -1.3 V, 60 s at -0.8 V, stripping in LSV from -0.8 V to -1.3 V.</figcaption></figure>

signal occurred in less than 2 h, while the Ni signal remained relatively stable. This suggests a passivation of the Bi film electrode, likely due to the adsorption of electrochemical reaction products on its surface.

According to Baxter [23], the detection of Co and Ni on Hg electrodes involves complexation by DMG, surface adsorption of the complex, and electrochemical reduction with the formation of by-products such as 2, 3-bis(hydroxylamino)butane (DHAB). Although this mechanism was described for Hg electrodes, a comparable pathway may occur with Bi film electrodes, adapted to reflect the absence of amalgam formation. It likely involves: (i) complexation of the metal ions by DMG (Eq. 4), (ii) adsorption of the resulting complexes onto the Bi surface (Eq. 5), and

(iii) reduction of the adsorbed complexes during the voltammetric scan (Eq. 6), possibly leading to metallic deposition and formation of adsorbed by-products contributing to passivation.

$$M^{2+} + 2dmgH_2 \rightleftharpoons [M(dmgH)_2] + 2H^+ \quad (4)$$

$$[M^{II}(dmgH)_2] \rightleftharpoons [M^{II}(dmgH)_2]_{ads} \quad (5)$$

$$[M(dmgH)_2]_{ads} + 2e^- \rightarrow M^0 + byproducts_{ads} \quad (6)$$

Where M represents Co or Ni.
Although DHAB formation is reported in Hg-based systems, its presence has not been confirmed in the Bi-based configuration used in this work. However, the observed passivation supports the hypothesis that electrochemical by-products are adsorbing onto the Bi surface. To address this issue, a regeneration step was introduced between each LSV measurement: a cyclic voltammetry scan from 0.1 V to -0.2 V (full marks). Fig. S6 shows a decrease in current between the first and second CV scans, suggesting that the cleaning scan partially oxidises the Bi surface, and desorbs adsorbed species. This regeneration significantly improves signal stability and repeatability.

### 3.4. Analytical performance of the sensors

Fig. S7A shows the calibration curves obtained for Co and Ni by LSV. A good repeatability was observed across all tested concentrations, with a coefficient of variation below 5 %. Under these conditions, LOD of 2.3 and 0.3 µg L⁻¹ and LOQ of 7.6 and 1 µg L⁻¹ were obtained for Co and Ni, respectively. These performances are compatible with the monitoring of spiked canal waters at 6, 30, and 60 µg L⁻¹. For Co, further sensitivity enhancement was achieved by increasing the deposition time to 300 s, leading to a LOD of 0.3 µg L⁻¹ and a LOQ of 0.9 µg L⁻¹ (Fig. S7B). In contrast, no improvement was observed for Ni, likely due to the saturation of Ni-DMG adsorption sites.

Although these measurements were conducted in synthetic media, they were carried out using a continuous flow system specifically designed for real-time, on-site measurements, which is a configuration rarely explored in the literature. Most studies reporting similar or lower LODs are based on laboratory conditions in batch-mode, with minimal matrix complexity and no continuous regeneration. A comparison with representative examples is presented in Table 2. Despite the more challenging conditions of our experimental setup, the analytical performances obtained remain highly competitive particularly for on-site applications.

<table><caption>**Table 2**
Comparison of selected electrochemical sensors for Co and Ni detection using a Bi-film modified electrode.</caption><thead><tr><th>Working Electrode</th><th>Technique</th><th>Sample matrix</th><th>LOD Co (µg L<sup>-1</sup>)</th><th>LOD Ni (µg L<sup>-1</sup>)</th><th>Ref</th></tr></thead><tbody><tr><td>CPSE</td><td>SWAdCSV</td><td>Aerosol</td><td>1</td><td>5</td><td>[29]</td></tr><tr><td>Bi-MEA</td><td>SWAdCSV</td><td>Mineral water</td><td>0.18</td><td>n.d.</td><td>[40]</td></tr><tr><td>mpBiF</td><td>SWAdCSV</td><td>Sewer water</td><td>0.027</td><td>0.94</td><td>[41]</td></tr><tr><td>Bi-AuE</td><td>SWAdCSV</td><td>Spring water</td><td>0.058</td><td>0.098</td><td>[42]</td></tr><tr><td>BiFE</td><td>SWAdCSV</td><td>River water</td><td>0.09</td><td>n.d.</td><td>[37]</td></tr><tr><td>BiFE</td><td>SWAdCSV</td><td>River water</td><td>n.d.</td><td>0.1</td><td>[36]</td></tr><tr><td>BiFE</td><td>SWAdCSV / FIA</td><td>Iron ore leachate</td><td>1</td><td>1</td><td>[43]</td></tr><tr><td>BiFME</td><td>SWAdCSV</td><td>Biological fluid</td><td>0.069</td><td>0.056</td><td>[28]</td></tr><tr><td>BiFME</td><td>SWAdCSV</td><td>Ammonium buffer</td><td>0.070</td><td>0.090</td><td>[27]</td></tr><tr><td>RdBiFE</td><td>SWAdCSV</td><td>River water</td><td>0.070</td><td>0.100</td><td>[26]</td></tr><tr><td>BiFE</td><td>SWAdCSV, AdSCP</td><td>Ammonium buffer</td><td>0.08</td><td>0.26</td><td>[44]</td></tr><tr><td>BiFE (this work)</td><td>LAdCSV / FIA</td><td>River water in flow</td><td>0.3</td><td>0.3</td><td>This work</td></tr></tbody></table>

**Techniques:** SWAdCSV: Square wave adsorptive cathodic stripping voltammetry; AdSCP: Adsorptive striping constant current chronopotentiometry; LAdSV: Linear adsorptive cathodic stripping voltammetry; FIA: Flow injection analysis.

**Electrodes:** SPCE: screen-printed carbon electrode; DMG-SPCE: SPCE modified with DMG; CPSE: carbon stencil-printed electrode; Bi-MEA: bismuth micro-electrodes array; mpBiF: macroporous bismuth film SCPE; Bi-AuE: Bismuth modified gold electrode; BiFE: Bismuth film electrode; BiFME: Bismuth film microelectrode; RdBiFE: rotating-disc bismuth film electrode

### 3.5. Interferences

Interferences from Cu, Li and Mn were studied because they are frequently present with Co and Ni. The addition of Li and Mn at concentrations of 0–100 µg L⁻¹ did not result in a decrease in the Ni signal, while a slight decrease of Co signal was observed (data not shown). The Ni signal was not affected by the presence of Cu in the solution up to 2 mg L⁻¹, while a 10% decrease in the Co signal was observed at 100 µg L⁻¹ Cu and 25% at 2 mg L⁻¹ (Fig. S8). The first explanation for this behaviour lies in the stability constants of the DMG complexes. No values were found for Li and Mn, but experimental results suggest that their stability constants are close to or lower than those of Co and Ni. On the other hand, Cu has a higher stability constant (log K Cu(DMG)₂ = 19.24 [45] than Co and Ni (log K Co(DMG)₂ = [12.72 – 13.28] [46], log K Ni(DMG)₂ = 17.24 [45]) which is in agreement with the obtained results. Experiments have shown that this interference only occurs when the Cu/Co ratio exceeds 66, an unusual situation in natural water. Otherwise, this interference could be mitigated by increasing the DMG concentration, making competition between metals for the formation of DMG complexes negligible. In cases where DMG is in excess, this suggests that interference is due more to the affinity of the metal-DMG complexes for the electrode surface and the number of adsorption sites dedicated to each type of complexes. Under these conditions, the sample must be diluted to lower the metal concentration and thus avoid saturation of the adsorption sites.

### 3.6. On site analysis

#### 3.6.1. Reactivity and sensitivity of sensor in the field

In order to validate the analytical system under natural environmental conditions, the sensor was deployed on the pilot rivers for one month, and Co and Ni concentrations were continuously monitored. Firstly, an analysis was carried out in three different canals (RP), with different Co and Ni concentrations, in order to verify the reactivity and sensitivity of the sensor to Co and Ni concentrations. Fig. 7 shows signal variations when the sensor is immersed in a new canal. The stabilisation time corresponds to the time needed to change the solution throughout the sampling line. As the sampling line is 30 m, this time was estimated at 30 minutes. Taking these parameters into account, the sensitivity curve was based on the signals recorded in the zone delimited by the grey arrows (Fig. 7). It appears that the sensor responds differently depending on the monitored canal with a good linearity (r² = 0.98 for Co and 0.95 for Ni) between the concentration of Co and Ni in the canal and the peak height, with a better sensitivity for Co than for Ni.

#### 3.6.2. Sensor stability in the field

Co and Ni signals were then recorded in a canal containing 30 µg L⁻¹ of each metal over 24 h periods. A field calibration (river solution spiked with Co and Ni) was carried out each morning with a new head to ensure that the head was working properly despite the time lag between preparation and use, and that reproducibility between heads was satisfactory (Fig. S9).

These monitoring highlighted several phenomena. First, after a short stabilisation period of the signal, a slight continuous decrease in signal intensity is observed over time, suggesting a degradation of the Bi film on the working surface of the sensor (Fig. 8A). Indeed, SEM observations, made on a sensor that had been operating continuously in the laboratory for 70 hours in a 5 L flow system, show a complete reorganisation of the Bi on the surface of the WE (Fig. S10). After 70 h of analysis, the distribution of Bi still matches that of Cl but not that of O suggesting a reduction of the film over many repetitions and in parallel a redistribution of Bi on surface (Fig. S11).

This redistribution might be due to the CV performed between each analysis to clean the surface and could explain the slow sensitivity decrease over time. That's why, a short maintenance, described in supplementary information, was carried out every morning.

<footnote>*n.d.: not determined</footnote>
<figure><img src="image_8.png" /><figcaption>**Fig. 7.** Reactivity of the sensor according to the concentrations in 3 different canals: RP13 at 30 µg L⁻¹, RP8 at 6 µg L⁻¹, and RP9 at 60 µg L⁻¹. Inset figure: sensitivity curves of Co and Ni performed in the different canals. Peak heights are given in absolute values. Analytical conditions: analysis in flow (0.8 L h⁻¹) with the following electrochemical parameters: 10 s at -1.3 V, 60 s at -0.8 V, stripping in LSV from -0.8 to -1.3 V and 2 scans in CV from 0.2 to -0.1 V at 100 mV s⁻¹ between each measurement.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 8.** Co (orange line) and Ni (blue line) signal variations in absolute values, temperature variation (brown line), and pH variation (green line) over 24 h at two different periods: 14/04/2023 (Figures A, C and E) and 17/04/2023 (Figures B, D and F).</figcaption></figure>

The second phenomenon observed throughout these monitoring campaigns is the intensity of the Co signal, which can vary greatly (2 or 3 times) compared to that of Ni when temperature increases (Fig. 8B). Laboratory tests showed that temperature led to an increase in the signal

from both metals simultaneously (data not shown). As shown previously, two other parameters can affect the Co and Ni signals: higher DMG concentrations or higher deposition times. As the deposition time remains constant, the explanation must be sought in the DMG
concentration. Unlike the laboratory tests, the on-site experiments required DMG to be injected at a constant flow rate. However, this flow rate can vary with temperature due to the effect of temperature on the viscosity of water (from 1 mPa s at 20°C to 0.797 mPa s at 30°C) [47]. An increase in temperature therefore leads to an increase in the flow rate of the DMG and the flow rate of the river water. As the Co signal strongly depends on the DMG flow rate (Fig. 4) and on the river water flow rate (Fig. 5), it will therefore be temperature dependent.

### 3.6.3. Specific events recorded

Throughout these monitoring campaigns, several events were observed. A short-period event was recorded several times between 9 and 10 o'clock, with a slight decrease in the Co and Ni signals at first, then an increase before returning to the initial values (Fig. 9). These events are due to the pump checks carried out every morning, which involves removing the injection hose from the canal to check the test tube, before replacing it in the canal and returning the solution from the test tube to the canal. This observation shows that the sensor is a relevant tool for monitoring river water and highlighting specific events where the variation in concentration is less than 10 µg L⁻¹.

An exceptional event lasting half an hour was recorded on April 19 (Fig. 10), characterized by a rapid increase in the Co signal alone (from 10 to 40 µA, representing a variation in concentration from 30 to 100 µg L⁻¹) around 5 pm, with no concurrent variation in pH or temperature. The stability of the Ni signal observed during this period confirms that the fluctuation in the Co signal was not due to a malfunction of the injection pump, as Co and Ni share the same injection mechanism. In addition, this increase in the signal cannot be attributed to a failure of the analytical system pumps, as a high flow rate would not produce such a pronounced signal. This brief but significant event demonstrates the limitations of conventional water-sampling methods in capturing short-lived events. It underscores the importance of high-frequency monitoring systems in aquatic environments to detect specific, potentially recurring events with implications for both environmental health and aquatic organisms.

## 4. Conclusion

A new electrochemical sensor has been developed for monitoring

river water. This sensor, comprising 3 electrodes (a working electrode, a counter electrode and a reference electrode), was designed to be easily handled and incorporated in a monitoring station. To test this new sensor, the working surface area was modified with a bismuth film, to detect Co and Ni simultaneously. Many parameters had to be optimised to obtain a stable signal over time, such as the deposition time, DMG concentration, DMG flow rate, water sample flow rate and the electrochemical procedure. At the same time, a Python code was developed to rapidly process the huge amount of data collected every day. This sensor has been shown to be capable of detecting Co and Ni over a wide range of concentrations, from a few µg L⁻¹ to 60 µg L⁻¹ and to be sensitive and able to detect an event of short duration. It appeared to be a promising tool for monitoring river water, since it enabled the detection of a brief event, that would not have been detected by conventional approaches. However, there are still a few improvements to be made to ensure greater stability over time. In addition, a number of developments are also needed to make this system effective for monitoring aquatic environments, such as an automatic calibration of the sensor and automatic cleaning of the flow system. At the same time, work is underway to improve signal processing and more specifically, automatic correction of the signal as a function of the sensor's loss of sensitivity. These enhancements will make it possible to reduce human intervention and therefore maintenance costs, generate analysis reports and set up an alert system in the event of unusual variations in concentrations. While the cost of the system is high (sensor: 2000€, flow system 3000€), the cost of using is relatively low, since the only consumable is the head which costs 40€ to manufacture and has to be changed regularly, but can be reused after repackaging.

### CRediT authorship contribution statement

**Isabelle Le Hécho:** Writing – review & editing, Supervision, Resources, Methodology, Funding acquisition. **Séverine Le Faucheur:** Writing – review & editing, Resources, Methodology, Funding acquisition. **Hervé Pinaly:** Software, Methodology. **Jordan Lacoste:** Methodology, Investigation, Formal analysis. **Estelle Ricard:** Methodology, Investigation, Formal analysis. **Corinne Parat:** Writing – original draft, Visualization, Validation, Supervision, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization.

<figure><img src="image_10.png" /><figcaption>**Fig. 9.** Observation of a specific event during the monitoring of Co (orange line) and Ni (blue line) in a river water over time. Peak heights are given in absolute values. Inset figure: Variation in concentration over time. The analytical conditions are the same as in the previous figures.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>Fig. 10. Voltammograms recorded during a short-lived event observed during on-site monitoring. Inset figure: Variation in Co concentrations over time.</figcaption></figure>

## Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Corinne Parat reports financial support was provided by University of Pau and Pays de l'Adour. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

The authors would like to thank the Université de Pau et des Pays de l'Adour for the grants they received as part of their Energy and Environmental Solutions (e2s) project supported by the agence nationale pour la recherche (ANR OPE-2018-0020) and the communauté d'agglomération de Pau Béarn Pyrénées (CAPBP, OPE- 2020-0032). They would also like to thank Total Energie Environnement, and in particular Clémentine Gelber, for making this experiment possible at the pilot rivers (Pôle d'Etudes et de Recherche de Lacq; TotalEnergies One Tech) in France.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jece.2025.117320.

## Data availability

Data will be made available on request.

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