2.1 Assay Principle of the Sensor

Electrochemical sensors for detecting ALP typically exploit the enzyme’s ability to convert a substrate into an electrochemically active product that can be measured. Common substrates used for this purpose include disodium phenyl phosphate, hydroquinone diphosphate, and ferrocene-based compounds. Among these, disodium phenyl phosphate is frequently chosen.^[12] In this reaction, ALP hydrolyzes disodium phenyl phosphate, cleaving the phosphate group and producing phenol, the electroactive species detected by the sensor. In the present study, the ALP-containing sample is applied to a screen-printed working electrode on a flexible, cost-effective, and portable polyester substrate (Figure 1).

The phenol generated undergoes electrochemical oxidation, producing a measurable current signal. Techniques such as cyclic voltammetry and differential pulsed voltammetry are employed to quantify the phenol concentration, which directly correlates with ALP activity in the sample. The greater the ALP activity, the more phenol is produced, leading to a stronger electrochemical signal.

2.2 Optimization Studies

To determine the optimal experimental conditions for the flexible screen-printed electrodes (SPEs), optimization studies were conducted, assessing key experimental factors influencing enzyme’s activity and biosensor performance. Various buffers with pH values ranging from 7.0 to 7.6, similar to those found in biological samples^[22] (e.g., serum), were tested. Among the buffers evaluated, Tris-HCl buffer provided the highest and most reproducible biosensor response. Statistical analysis via t-tests revealed that the reaction in Tris-HCl buffer showed a significant difference compared to both phosphate buffer saline and phosphate buffer (Figure 2A). This indicates that the use of Tris-HCl buffer significantly enhanced sensor performance, making it the optimal choice for subsequent experiments.

As the goal of the proposed study was the development of a point-of-care device, we also evaluated the effect of temperature on biosensor’s performance. Since the optimal enzymatic reaction temperature is 37 °C, with 25 °C showing up to 80% of the enzymatic activity,^[23, 24] we investigated the sensor’s performance at room temperature (RT) to explore a more equipment-free approach. Statistical t-tests were performed via Python, and the results showed no statistically significant difference (p = 0.07, >0.05) between the signal responses at RT and 37 °C (Figure 2B). This indicates that temperature does not have a strong effect on the signal response. Therefore, we proceeded with the optimization and development of the biosensor at RT to enhance practicality and accessibility, without compromising its performance.

Various reaction times ranging from 2 to 15 min were tested using 85 U/L of ALP enzyme and 5 mM of the specific substrate. A characteristic peak was increased by increasing incubation time at 0.65 V in the CV measurements. The highest current response was observed at a 15-min incubation time, which was therefore selected as the optimal reaction duration (Figure 3A).

A paired t-test was conducted to compare the 2-, 5-, and 10-min incubation times with the 15-min interval. The statistical analysis was performed using a Python script to conduct paired t-tests for each time interval against the 15-min reaction time. The results indicate that the 15-min incubation time provided the highest current response, with no statistically significant differences among the other time intervals (Figure 3B). Based on these findings, the 15-min incubation time was selected for further studies, as it ensures optimal performance while minimizing analysis time. Therefore, no additional time intervals were explored, making this condition particularly suitable for point-of-care applications and on-field testing.

After selecting the optimal incubation time, the substrate concentration was further investigated. Current responses were recorded for increasing concentrations of the specific substrate and compared to a blank sample without substrate addition. The signal increased with higher substrate concentrations until reaching a plateau at 10 mM (Figure 3C), as the enzyme was unable to further dephosphorylate the substrate. The study was conducted using 85 U/L of ALP with a 15-min incubation time. The investigation of substrate concentration revealed a typical Michaelis–Menten behavior,^[25] as expected. As the substrate concentration increased, the reaction rate also increased, eventually reaching a plateau in the biosensor’s response. This saturation point indicates that further increases in substrate concentration did not lead to a significant increase in the reaction rate. Based on these findings, a final concentration of 5 mM was selected as the optimal substrate concentration, as shown in Figure 3D.

2.3 Analytical Characterization of the Flexible Screen-Printed Electrochemical System

The analytical performance of the optimized method was evaluated using increasing concentrations of ALP, prepared in 50 mM Tris-HCl at pH 7.00. To enhance sensitivity, calibration curve measurements were performed using differential pulse voltammetry (DPV), which offers greater sensitivity compared to cyclic voltammetry (CV).^[26] Measurements were conducted in triplicate after a reaction time of 15 min in the presence of 5 mM of the specific substrate, as determined through optimization.

The resulting calibration curve is described by the Equation y = 6.33x + 0.06, with an R² = 0.99, where y-axis values represent the change in signal intensity measured between the solution containing only the substrate at a 5 mM concentration, i.e., the blank sample, and the solution where the enzyme is present, leading to substrate dephosphorylation and subsequent oxidation. The x-axis values correspond to the ALP concentration, expressed in U/L (Figure 4).

The LOD was calculated as the ratio of three times the standard deviation of the blank to the slope of the calibration curve, resulting in a value of 0.03 U/L, demonstrating the assay’s ability to detect even highly diluted samples without requiring additional pretreatment to reduce matrix effects. The repeatability of the developed assay was assessed in terms of the relative standard deviation (RSD%). The RSD% was calculated by dividing the standard deviation by the mean value of the measurements and multiplying the result by 100. This calculation demonstrates the repeatability of the assay, which in this case yielded an RSD% up to 8.5% for all calibrators in solution.

2.4 Matrix Effect Investigation

Following the analytical performance evaluation of the biosensor, attention was focused on assessing ALP behavior and the sensor’s detection capability in a system that mimics the biological environment. In undiluted serum samples, various chemical species and biomolecules are present, potentially interfering with measurements and altering the detected signal. This phenomenon, known as the matrix effect, can lead to a reduction in the observed current response. To mitigate this effect, different serum dilutions, namely, 1, 10, 20, and 50%, were prepared using 50 mM Tris-HCl buffer at pH 7.0 and systematically evaluated to determine the optimal conditions for accurate ALP detection (Figure 5A).

Despite the histogram clearly showing that the current signal difference is higher at a serum dilution of 1%, as expected due to the lower matrix effect, this dilution was not chosen as the optimal condition for subsequent experiments. The increase in current observed at this dilution was not significant enough to justify such a high dilution of the sample. Additionally, a 20% serum dilution was selected to avoid excessive sample dilution while staying within the clinically significant range. After evaluating these considerations and examining the system’s behavior at other dilution levels, a 20% serum dilution was chosen as a balanced compromise between sensitivity and maintaining a sufficiently high current signal.

2.5 Analytical Characterization in Serum Samples

Commercial human serum (Sigma-Aldrich, St. Louis, MO, USA) was used in this experimental section. The analytical performance of the system was further evaluated in 20% serum at increasing concentrations of ALP, ranging from 0.5 to 100 U/L. After adding the substrate, DPV was utilized to record the current responses of the sensor, as described previously. A linear range was obtained (Figure 5B) following a linear regression fitting described by the Equation y = 0.44x + 0.005 with R² = 0.98, where x represents the amount of spiked ALP in U/L in the 20% diluted serum samples, and y represents the current signal change between the blank reference and the measurement of the solution where dephosphorylation occurs. Finally, the LOD was found to be 0.08 U/L in the fivefold diluted samples, which corresponds to 0.4 U/L in the undiluted samples, considering the dilution factor. This demonstrates the potential of the proposed flexible biosensor for real clinical applications. The %RSD was also calculated for all spiked serum samples and was up to 9%, ensuring the sensor’s repeatability.

Despite consisting of a homemade SPE without any surface modification, our biosensor achieved a LOD comparable to previously reported electrochemical biosensors for alkaline phosphatase detection^[18, 27-32] (Table 1). Notably, our approach does not rely on complex labeling strategies, nanomaterial modifications, incubators, or lengthy preparation protocols, making it a simpler and more cost-effective alternative for ALP detection. The ability to reach a clinically significant LOD using an unmodified electrode highlights the potential of our sensor for rapid and accessible diagnostics, particularly in point-of-care applications.