UV-Vis, FT-IR and DLS of AuNPs
The UV-Vis spectrum of the AuNPs is shown in Fig. S1a, indicating that they have a maximum absorption at 525 nm. FT-IR spectroscopy was also used to identify functional groups present at the NP surface. To this end, FT-IR spectrum of the Satureja hortensis extract was compared with that of the synthesized AuNPs, and the results are shown Fig. S1b. As can be seen, the main peaks in the FT-IR spectrum of the extract are mostly observed in the AuNP spectrum. However, they have lower intensities, and are shifted toward higher wave numbers, confirming the synthesis of the AuNPs using the Satureja hortensis extract. The peak at 3400 cm–1 is attributed to the alcoholic and phenolic OH stretching vibrations, whereas NH amide stretching vibrations are responsible for the appearance of the peak at 3000 cm–1. Moreover, the peak at 2800 cm–1 arises from the CH stretching vibrations of alkanes. The emergence of the peaks in the wave number ranges of 2200–2100 cm–1, and 1630–1610 cm–1 can be assigned to C≡C and C≡N groups in aromatic and aliphatic compounds, and C=C and C=O groups in aromatic compounds and proteins, respectively. It should be noted that the peaks relating to the CO bending vibrations range from 1100 to 1200 cm–1, arising from alkanes, alcohols, carboxylic acids, esters and ethers 36.
Figure S1c shows hydrodynamic size distribution histogram of the AuNPs obtained by the DLS method. It is found that the average size of the NPs synthesized by the green approach is 38 nm. The surface charge of the AuNPs was also calculated by the zero potential method, and the results are shown in Fig. S1d. The corresponding load distribution histogram indicates that the surface of the NPs is negatively charged (–14 mV).
FE-SEM and EDX of the sensor
Surface modification of the Whatman paper with the AuNPs was investigated by FE-SEM and EDX analyses. As shown in Fig. S2a, the Whatman paper is made of a series of cellulose fibers. Following the immersion of the paper in the solution containing AuNPs, they become homogeneously distributed on the surface, according to Fig. S2b. The corresponding EDX spectrum depicted in Fig. S2d shows a strong peak at 2.3 keV, confirming the presence of the element Au in the nanoparticle structure coated on the paper.
To investigate the repeatability of the sensor fabrication, five cut pieces of paper were immersed in a solution containing AuNPs, and then stuck on a double-sided tape. The mean values of red, green and blue color components were calculated for each sensor. In the next step, the relative standard deviation (RSD %) value of each color component was obtained, and the results are presented in Table S1. The low RSD values indicate that the fabrication process of the distance-based paper sensor has good repeatability.
Optimization of the sensor
Since a mixture of the buffer and AuNPs was used in the fabrication of the sensor, some parameters such as the NP volume, solution pH, buffer type, buffer concentration, analyte volume, and the interaction time between the analyte and the sensor are expected to affect the sensor response. Therefore, the aforementioned parameters need to be optimized in order to obtain the highest sensor response for the lactate concentration measurement.
The optimization process of the sensor response in the present study was calculated for both the distance measurement and image analysis methods. In the first experiment, the proposed sensor was prepared by immersing a cut piece of paper in a mixture of the buffer and different NP volumes in the range of 2.0–10.0 µL. The interaction between lactate and AuNPs was investigated for each of the fabricated sensor, and the results are shown in Fig. 2a. As observed, the sensor response increases with increasing the NP volume, reaching a maximum value at the volume of 8.0 µl. Note that the sensor response is not affected by NP volumes higher than this volume. Therefore, the sensor was prepared by immersing the cut piece of paper in a mixture of 10.0 μL of buffer, 8.0 μL of Au NPs, and 2.0 μL of double-distilled water.
In the second experiment, the sensor response was evaluated under different acidic and alkaline conditions by changing the pH of the mixture from 4.0 to 12.0. According to Fig. 2b, the highest sensor response is obtained at pH= 8.0. Essentially, an undesirable sensor response may be caused by active site protonation at low pH values and/or hydroxyl ion interference and electron repulsion at more alkaline pH values 21.
The interaction between lactate and the sensor was investigated for three different buffer solutions, including Britton-Robinson buffer, Tris buffer and Borate buffer. The experiment was carried out at the optimal pH= 8.0. Figure 2c shows that, while the sensor response calculated by the distance measurement is the same for the different buffer types, the image analysis method indicates that the highest sensor response results from the Britton-Robinson buffer. In order to keep the experiment conditions the same for the both analyses, the Britton-Robinson buffer was also used for the subsequent analyses.
In continued, the effect of buffer concentration on the sensor response was evaluated by changing the selected buffer concentration in the range of 0.02–0.15 M. The corresponding results presented in Fig. 2d indicate that the interaction between lactate and the sensing element is enhanced with increasing the buffer concentration from 0.02 to 0.05 M. At higher concentrations, the sensor response is reduced due to the presence of other ionic species as well as the ion-ion interference 22.
At the end, the time required to perform a complete reaction between the analyte and the sensor was assessed. This time was calculated from the moment of analyte injection until reaching reaction equilibrium between lactate and AuNPs. This experiment was investigated only by Image analysis. However, according to Fig. 2e, the results indicate that the time required to reach the equilibrium equals to 1.5 min.
The sensor response mechanism
Figure 1(c) shows the response of the proposed sensor to lactate with a concentration of 4.0 mM. As can be seen, the color of AuNPs changes from red to purple as a result of their interaction with lactate. The sensor response mechanism can be explained by the fact that the summer savory aqueous extract is rich in the phenolic acids, comprising phenolic and carboxylic groups. Since lactate consists of an alcohol group and an acidic group, an esterification reaction can occur between the acidic group of lactate and the alcohol groups of the acids in the extract. Similarly, an esterification reaction may take place between the lactate alcohol group and the extract acidic groups, thus forming a bridge between the AuNPs 34. In this way, the distance between the NPs is reduced, leading to their aggregation on the paper surface. In this regard, FE-SEM image depicted in Fig. S2c evidences the aggregated AuNPs due to their interaction with lactate.
Quantitative measurement of lactate
The proposed sensor was used to measure lactate with different concentrations. In this regard, an aqueous solution of lactate was initially prepared with the different concentrations in the range of 0.0–30.0 mM. Afterwards, 40.0 μL of each solution was added to the pad separately, and lactate and the sensor were allowed to interact with each other. The corresponding sensor response is shown in Fig. 3a. As observed, the interaction between lactate and AuNPs causes the sensor to change color from red to purple. The length of the purple stain produced on the sensor substrate is proportional to the analyte concentration. With an increase in the concentration, lactate can move further on the surface of the paper. The calibration curve shown in Fig. 4a indicates a linear relationship between the distance moved and the lactate concentration in the range of 1.0–30.0 mM. It is worth noting that the theoretical detection limit for this measurement was calculated to be 0.6 mM.
Using the image analysis, the color change of the sensor was calculated before and after the interaction with lactate, and subtracted from each other. Figure 3b presents the subtracted values as a color map, comprising two distinct parts: a colored part, and a black part. The former represents the occurrence of interaction, and the latter indicates the absence of interaction between lactate and AuNPs. The color maps confirm the results obtained by the distance measurement analysis. For each color map, the Euclidean norm was calculated, representing the net sensor response. Accordingly, a calibration curve was obtained by plotting the Euclidean norm against lactate concentration, as shown in Fig. 4b. The calibration curve is found to be linear in the range of 0.5–30.0 mM, giving rise to a detection limit of 0.4 mM. Table 1 presents the analytical information obtained from both the distance measurement and image analysis methods. As inferred, the image analysis shows a better sensitivity rather than the distance analysis. Moreover, a better correlation is found between the lactate concentrations and the results obtained using the image analysis method. Nevertheless, the distance measurement method is a faster, simpler and more user-friendly method compared to the image analysis.
To ensure that the sensor can provide reproducible responses, 5 separate sensors were prepared, and injected with lactate with a concentration of 4.0 mM. The results were calculated in the form of stain length and Euclidean norm, as presented in the bar graphs of Fig. S3. The RDS values of the distance measurement and image analysis methods are obtained to be 4.60% and 4.39%, respectively. In this way, the paper sensor response is reproducible, arising from the low RSD values.
Since interfering species might be present in real samples, the selectivity of the proposed sensor to lactate was investigated as follows: Initially, solutions of different compounds such as ions [e.g., sodium, potassium, magnesium (II) and calcium (II)], anions (e.g., chloride, nitrate, nitrite, carbonate, phosphate, thiocyanate and sulfate), amino acids (e.g., histidine, lysine, cysteine, tyrosine, Glycine and alanine) and other chemicals (e.g., glucose, creatinine and ascorbic acid) were prepared. The solutions were then mixed separately with lactate at concentration ratios of 100, 75, 50 and 25. It should be noted that the lactate concentration in all the prepared mixtures was constant to 4.0 mM. The sensor responses for the mixtures were compared with those for only lactate, and the results are presented in Table 2. Evidently, at least up to 25 times of the lactate concentration, most of the species used in the present study were not interferences for lactate determinations.
Evaluation of the sensor stability
The effect of environmental factors on the stability of the proposed sensor was investigated by monitoring the color changes for a specific period of time using the image analysis method. As shown in Fig. S4, no changes in the sensor color is observed for up to 15 days. However, after that period of time, the sensor is not stable against temperature and chemical changes, leading to the progressive aggregation of the AuNPs.
In another experiment, the response of an as-fabricated sensor was compared with that of a 15 day old sensor in the presence of lactate using the distance measurement and image analysis methods. The results obtained are presented in Table S2. According to the statistical calculations, no significant difference is found between the responses of the two sensors, so that one can use them for a period of 15 days in order to efficiently analyze the lactate concentration.
As previously explained in section 2.7, the proposed sensor was used to measure lactate in a plasma sample. Table 3 presents the comparison between the results obtained from the paper sensor and the standard method. The recovery and relative error of the sensor are indicative of its high accuracy and validity for measuring lactate in plasma samples, giving rise to a reliable diagnostic method.
On the other hand, Table S3 presents the analytical parameters obtained by different methods for measuring lactate. Although the enzymatic methods show higher sensitivity, the enzyme-free method proposed in the present study utilizes NPs synthesized with the plant extract, considerably lowering the preparation cost of the sensing element compared to the enzymes. Also, in addition to the simple coating of the NPs on the paper substrate, the proposed sensor does not require specific keeping conditions. Compared to other methods, the distance measurement and image analysis methods used here do not need laboratory conditions, operators and expensive reading devices, providing an almost wide linear range along with a better detection limit.