Certain Methods of Electrode Pretreatment Create Misleading

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Article Cite This: ACS Omega 2019, 4, 5839−5847

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Certain Methods of Electrode Pretreatment Create Misleading Responses in Impedimetric Aptamer Biosensors Lance St. John Ho, Janice L. Limson, and Ronen Fogel* Biotechnology Innovation Centre, Rhodes University, P.O. Box 94, Grahamstown 6140, Eastern Cape, South Africa

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S Supporting Information *

ABSTRACT: Despite the widespread knowledge of the influence that electrode pretreatments have on electroanalysis, pretreatments of gold electrodes for impedimetric aptasensors remain study-specific. This may be a neglected reason behind the limited consensus in reports focusing on creation and testing of aptasensors. To investigate this, several commonly reported pretreatments were applied to gold electrodes that were subsequently used to fabricate thrombin-specific impedimetric aptasensors, which are widely reported in the literature. Layer-by-layer electrochemical impedance spectroscopy analyses were conducted to determine the effect the pretreatment selection has on impedimetric responses during fabrication and testing of the aptasensor. Pretreatments were evaluated using factors crucial for biosensor deployment: the repeatability of interelectrode measurements and whether biosensors produced statistically significant responses when exposed to 2 μM thrombin. Individual pretreatments were found to impart unique physicochemical properties to electrode surfaces, indicated by variations in measured capacitances (impedimetry) and electrode surface topographies (scanning electron microscopy). Impedimetrically measured resistivity (Rtotal) of electrodes increased during layering of the biosensor across all the investigated pretreatments: from 852 ± 830 Ω (bare electrodes) to 3117 ± 1199 Ω (fabricated aptasensors exposed to thrombin), consistent with prior literature. Crucially, the magnitude and reproducibility of Rtotal values measured during fabrication and testing of the assembled aptasensor were strongly contingent on the pretreatment. Depending on the pretreatment, interelectrode sensor responses exhibited relative standard deviations between 38 and 150%. These findings propose that the choice of gold-surface pretreatment protocols is an overlookedyet crucialfactor to consider when developing analytically valid impedimetric aptasensors.

1. INTRODUCTION Aptamersspecifically selected, single-stranded DNA or RNA moleculesprovide an alternate means for biorecognition over their conventional, protein-based, counterparts.1 As such, they have been intensively researched for their possible application within biosensor technologies. As a platform for aptamer-based biosensors (aptasensors), electrochemical impedance spectroscopy (EIS) is frequently used2−5 because it provides numerous advantages for the mass deployment and application of sensor devices. Being a technique known for its sensitivity, minor changes in the environment close to the electrode surface are detected through differences in surface charge density and electron tunneling distance.6,7 A crucial parameter to consider when developing a reliable biosensor is the overall repeatability of measurements produced by the system;8 particularly, when considering mass production of the sensor, the variation in measurements between individual sensors is the interelectrode variation. Because the topological and chemical properties of an electrode is directly linked to the pretreatment of that electrode, pretreatment strategies may affect the quality and integrity of the response of the final sensor, for example.9,10 © 2019 American Chemical Society

Optimizing surface pretreatments can thus minimize reproducibility shortfalls of EIS aptasensors. Low reproducibility of chemical compositions and topographies of gold surfaces prepared for electroanalysis is a wellknown limitation of this material,11−15 despite its widespread use in this field. In particular, the cleanliness of gold surfaces affects subsequent electroanalytical responses, as well as the chemical behavior of these surfaces, for example, the formation self-assembled monolayers (SAM), a fundamental route to functionalizing gold surfaces for biosensor fabrication.9−11,13 To enhance the sensitivity and reproducibility of electrochemical measurements at gold electrodes, a wide variety of pretreatment and cleaning procedures are routinely applied to gold electrodes in laboratories.9,12,13 A comparison of reports on impedimetric aptasensors indicates that a similar variety of these methods are being used when constructing aptasensors at gold electrodes (e.g., refs2,16,18) despite the noted variation in properties imparted by these pretreatments. This is of Received: January 14, 2019 Accepted: March 6, 2019 Published: March 26, 2019 5839

DOI: 10.1021/acsomega.9b00075 ACS Omega 2019, 4, 5839−5847

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Figure 1. Impedance responses for the individual layers comprising the biosensor, using electrodes pretreated using the combined (alkaline) methodology. (A) Nyquist plot. Experimental data depicted by dotted symbols and model-fitted data by overlaid lines. Au: pretreated gold surface, MPA: mercaptopropionic acid, SA: streptavidin, TBA15: thrombin binding aptamer, and THR: thrombin. Biosensor response represents ΔRtotal = APT RTHR total − Rtotal . (B) Bode magnitude plot. (C) Bode phase plot. (D) Equivalent circuit model used for fitting of experimental data. Rs: electrolyte resistance; Cdl: interfacial capacitance; Rct: charge-transfer resistance, Zw: Warburg diffusion, Cf and Rf: additional contributions from capacitive and resistive elements of the film.

extensively validated,3,5,26,27 providing a useful basis for immobilization of this aptamer for subsequent impedimetric analysis. Although studies comparing the influence of various pretreatments on electroanalysis exist, the suitability of pretreatment is often evaluated by direct electroanalysis at unmodified pretreated surfaces13 or at simple layers overlaying the electrode.10 We propose that the influence of pretreatment extends to the electroanalytical performance of more complex overlaid layers (such as the multiple functional layers which are common in biosensors), and that a pretreatment assessed to be most suitable at a particular level of biosensor fabrication might not be suitable once the biosensor is fully assembled. To this end, we report on a fundamental study to investigate the impedimetric responses obtained at gold electrodesat different stages of biosensor fabrication and testingas a function of electrode pretreatment strategies. Elemental and topological properties imparted on the electrode surface by different surface pretreatments were evaluated by scanning electron microscopy (SEM) and energy-dispersive X-ray (EDX) spectroscopy. Subsequently, a layer-by-layer analysis of the impedance spectra of TBA15 aptasensors was fabricated over electrodes pretreated, using several common mechanical,

particular concern because fundamental investigations into impedimetric biosensing of aptamer−protein interactions identified that EIS responses are highly sensitive to surface contamination, as well as other experimental factors not caused by biomolecular interactions.19 Promisingly, gold pretreatments have been shown to improve the electron transfer of redox-coupled double-stranded DNA SAMs on gold surfaces20 and lessen the nonlinear drift of charge-transfer resistance, often the cause of false positive and negative tests in EIS-based measurements.21 This highlights the need for a more unified approach to pretreatment during the creation and measurement of these types of biosensors. The thrombin binding aptamer, a 15-mer DNA oligonucleotide sequence (TBA15), has become one of the most studied aptamers in the current field.4 With the consensus of 5′GGTTGGTGTGGTTGG-3′, it binds specifically to the fibrinogen binding site of human α-thrombin.1,22 Because of the extensive research on this aptamer−target complex, it has become the standard model for proof-of-concept aptasensor construction and testing studies. Although various means of fabricating thrombin-sensing electrochemical biosensors have been reported,23−25 the biosensing ability of biotin-modified TBA15 attached to streptavidin (or its derivatives) has been 5840


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2.2. SEM and EDX Analysis. Physicochemical variations in the state of the surface caused by the tested pretreatments were assessed via SEM/EDX analyses of freshly pretreated electrode surfaces. Figure 2 provides illustrative examples of the variation in surface homogeneity across pretreated electrode surfaces as

chemical, electrochemical, and combined pretreatment methods to determine their influences on the impedimetric sensor response.

2. RESULTS AND DISCUSSION 2.1. Layer-By-Layer Analysis of an Antithrombin Impedimetric Biosensor. Figure 1 below illustrates typical electrochemical impedance spectra recorded for each layer of the assembled biosensor and the subsequent testing of the biosensor’s response to the presence of thrombin. The example shown here is for biosensor layers constructed using electrodes pretreated using the combined (alkaline) method. Previous reports on impedimetric testing of aptasensors report a general trend of increasing Rct with successive layers during biosensor fabrication,5−28 corroborated in this work as an increase in the diameter of the semicircular portion of the Nyquist plot (Figure 1A) and as increases in the Bode magnitude plot (Figure 1B). Frequency-dependent capacitive properties are demonstrated by the Bode phase plot (Figure 1C). The formation of the MPA SAM onto pretreated electrodes’ surfaces is anticipated to increase the overall resistivity. This is due to the combination of electrostatic repulsion and diffusional restriction that the negatively charged β-mercaptopropionate layer places between the redox probe molecules and the electrodes’ surfaces.29 For similar reasons, both the covalent immobilization of negatively charged streptavidin to the MPA surface when creating the SA surface3 and the subsequent attachment of negatively charged biotinylated DNA aptamer to the SA surface (APT surface) were also anticipated to substantially increase the measured Rct of the redox probes.30 Aptasensors investigating the interaction of thrombin to antithrombin aptamers (as in the THR surfaces in this work) reported an increase in Rct upon binding,19,30 attributed to conformational changes of the aptamer and subsequent redistribution of negative charges close to the electrode surface during biorecognition events. Of the fitted equivalent-circuit elements, only two parameters varied substantially between various layers and treatments investigated: the capacitive elements associated with the overlaid film (Cf) and charging of the electrode− electrolyte double layer (Cdl); and the resistance elements associated with charge transfer and the overlaid film. The Rtotal parameter was calculated as the sum of the charge-transfer resistance (Rct) and the film resistance (Rf) (Figure 1D). The obtained values for all of the modeled parameters of all of the tested surfaces and pretreatments are provided in Supporting Information, Table S1. The fit of the Warburg impedance element, Zw, visible as the straight ∼45° line on the right-hand side of the Nyquist plots in Figure 1A is noted to deviate from the experimental data with successive layers (THR, APT, and SA surfaces). This indicates that more complex mass transport is occurring at lower frequency ranges for these surfaces compared to the simpler layers examined in this study. However, this does not appear to strongly influence the high- and mid-frequency ranges, where charge-transfer resistance and double-layer capacitance values were drawn from; given the stability of the measured Warburg modulus across the different treatments and layers [% relative standard deviation = 0.09%] for the entire global population, this was not thought to strongly influence the overall measurements which are discussed in this article.

Figure 2. Representative scanning electron micrographs of freshly pretreated gold electrode surfaces. The preparation of each of these treatments is detailed in Section 4.2 of the Experimental Section.

measured by SEM. Visually, substantial differences in the micrographs of the electrodes are evident between the tested pretreatments. The large amount of surface imperfections present on the untreated surface (Figure 2A) is visible as darkened areas of the micrographthese correlate with either holes in the surface of the electrode31 or the local presence of electron-transmissive materials.32 Either of these correspond to a lack of surface uniformity for an electrode. Some of these imperfections are decreased during surface cleaning using mechanical polishing (Figure 2B), which removes contaminants but results in a surface with significant surface roughness (Figure 2B). Overall, surface roughness appears to decrease with further chemical/electrochemical pretreatments under acidic conditions (Figure 2C,D). Conversely, combining acidic pretreatments (Figure 1E) appears to increase surface roughness relative to either chemical or electrochemical pretreatment, whereas alkaline pretreatment provided the most visually uniform surface achieved with the tested pretreatments. The extent of this surface inhomogeneity was quantified by the standard deviation, or distribution, of pixel intensity estimated by the Fiji software’s histogram function. These observations are 5841


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polishing, chemical, and electrochemical (acidic) pretreatments. However, the electrochemical (17.18 ± 2.75) and combined (alkaline) (13.62 ± 5.67) pretreatments resulted in significant improvements to surface homogeneity (Figure 3, †). 2.3. Influence of Electrode Pretreatment on the Capacitive Component (Ctotal) of a Bare Au Surface. Only bare Au surfaces, (Supporting Information, Table S1), displayed considerable changes to Ctotal across the tested electrode pretreatments. This indicates that different chemical and topographical compositions of gold surfaces were imparted by these pretreatments, which are well-documented in literature and supported by our SEM (Figure 2) and EDX (Figure 3) findings. Surface roughness,10,33,34 oxide layer formation,10−13,35 and elemental composition13,14 are strongly influenced by the electrode pretreatment strategy employed. Further modifications of the electrode surfaces produced Ctotal values that were not significantly different (ρ > 0.05) from one another for the MPA through to THR surfaces regardless of the pretreatment or sensor layer. Overall, Ctotal measurements were considered to be an insensitive measurement of the impedance characteristics of the tested layers. 2.4. Influence of Electrode Pretreatment on the Resistive Component (Rtotal) of the Sensor Layers. Table 1 summarizes the modeled Rtotal values from the various electrode pretreatments and layers investigated in this study. In addition to the average Rtotal values (Table 1), the absolute standard deviation was used to determine repeatability that is the reproducibility of the impedimetric responses across different biosensor electrodes. This is a key measure of the analytical performance of a biosensor:8 consistency of interelectrode responses will influence the accuracy, sensitivity, and ease-of-use of any deployed biosensor. Ultimately, this metric measures the consistency of the experimental conditions under which testing occurs, for example, whether consistent concentrations of redox probes were employed between studies but also measures the consistency in the fabricationthat is surfacing and layeringof the biosensor undergoing testing. In general, the expected increases in Rtotal with successive layering of the thrombin-binding biosensor (Figure 1A) were observed across most of the electrode pretreatments: a progressive increase in Rtotal from 852 ± 830 Ω (Table 1, Au layer) to 3117 ± 1199 Ω (Table 1, THR layer) is apparent when tracking this parameter across all electrodes used in this study (Table 1, “All datasets” row). However, the electrode pretreatment protocol was found to strongly influence both the

combined with EDX measurements and presented in Figure 3 below:

Figure 3. Elemental analysis and estimated surface inhomogeneity of the untreated and various pretreated gold surfaces. Gold weight % (black bars) of freshly pretreated electrode surfaces was conducted by EDX. Surface inhomogeneity (gray bars) was estimated by the distribution of pixel intensity for the relative SEM images. Error margins represent standard deviation of the mean; n = 3. * indicates significant difference in the composition (Au % weight) at the electrodes’ surface of a pretreatment, relative to untreated electrodes. This was determined by Tukey’s HSD from the untreated surface (ρ < 0.05). Distribution of the pixel intensity of each surface was estimated from SEM images by Fiji software, taking into account approximately 280 000 pixels per image. † indicates a significant difference in the electrodes’ surface inhomogeneity, relative to the untreated electrodes. This was determined by Tukey’s HSD from the untreated surface (ρ < 0.05).

EDX determined that the gold content on the surface of the electrodes was, to a lesser extent, dependent on the pretreatment (Figure 3, black). Gold content ranged between 62.9 ± 2.34% (untreated) and 77.5 ± 5.17% (electrochemical). Of the tested pretreatments, only techniques using electrochemical polishing demonstrated significantly higher gold compositions (Figure 3, *). X-ray photoelectron spectroscopy has previously demonstrated that gold composition of an electrode’s surface can be increased significantly by electrochemical13 and combined (alkaline)13,14 pretreatments when compared to an untreated gold surface. Decreased surface inhomogeneity was further supported by the distribution of pixel intensity for the relative SEM images (Figure 3, gray). The untreated surface (51.04 ± 6.69) was considerably more variable compared to the mechanical

Table 1. Rtotal Responses for Each Surface Pretreatment and Biosensor Layera,d total measured circuit resistance of biosensor layer, Rtotal (Ω) electrode pretreatment c

all datasets mechanical chemical electrochemical combined (acidic) combined (alkaline)

Au

MPA

SA

852 ± 830 326 ± 164 364 ± 191 960 ± 367† 1821 ± 1673† 303 ± 146

1063 ± 347 718 ± 186 972 ± 240 977 ± 308 1022 ± 594 689 ± 132*

1362 ± 401 1093 ± 178 1299 ± 255 946 ± 384† 746 ± 266† 1691 ± 168*

APT 2004 1139 1027 2166 2458 2302

± ± ± ± ± ±

841 359 266† 704* 1016* 432*,†

THR

biosensor responseb

3117 ± 1199 2623 ± 446* 3204± 1240* 1604 ± 472† 3678 ± 1564† 3556 ± 735*,†

1112 ± 1208 1483 ± 573 2177 ± 1268 −562 ± 847 1220 ± 1865 1255 ± 852

APT c Uncertainties represent standard deviations from the mean (n = 6). bBiosensor response is calculated as RTHR total − Rtotal . Calculated as the average ± standard deviation of all electrodes of the biosensor layer, regardless of electrode pretreatment. d*Indicates p < 0.05 when testing whether the mean of the annotated layer’s Rtotal is significantly different from the preceding layer of the biosensor for the same electrode pretreatment. †Indicates p < .05 when testing whether the mean of the annotated layer’s Rtotal is significantly different from the same layer, compared across the tested electrode pretreatments. a

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Table 2. Ordinal Comparison of the Influence of Pretreatment Selection on the Reproducibility and Repeatability of Biosensor Layers order of reproducibility (most to least) category

1st

SEM/EDX: Au (gold content)

electrochemical

SEM/EDX: Au (homogeneity) EIS: Au EIS: MPA EIS: SA EIS: APT EIS: THR EIS: biosensor response (standard deviation) EIS: biosensor response (relative standard deviation)a

2nd

3rd

4th

5th

mechanical

combined (alkaline)

chemical

combined (alkaline) combined (alkaline) combined (alkaline) combined (alkaline) chemical mechanical mechanical

combined (acidic) electrochemical mechanical mechanical mechanical mechanical electrochemical electrochemical

chemical chemical chemical chemical combined (alkaline) combined (alkaline) combined (alkaline)

mechanical electrochemical electrochemical combined (acidic) electrochemical chemical chemical

combined (acidic) combined (acidic) combined (acidic) electrochemical combined (acidic) combined (acidic) combined (acidic)

mechanical(39%)a

chemical(58%)a

combined (alkaline) (68%)a

electrochemical (150%)a

combined (acidic) (153%)a

a

Calculation of the biosensor’s variation in response (standard deviation) as a percentage of the average biosensor response for the specified pretreatment.

Ω (Table 1). This is attributed to the normalization of surface charge and chemistry through the deposition of the SAM.29 In this study, although clustering of the values is notable for SAM formation (Table 1, MPA layer), no statistically significant changes in Rtotal were found between different pretreatments selected. This indicates that any influence that the surface pretreatment possesses on the structure of MPA-modified surfaces may be too subtle to be measured by EIS. Despite this, pretreatment methods that produced the least-repeatable responses at Au surfaces [such as combined (acidic) pretreatment] tended to produce MPA surfaces with low repeatability (Table 1). On the order of decreasing interelectrode repeatability (absolute deviation), the pretreatment techniques were ranked: combined (alkaline) > mechanical > chemical > electrochemical > combined (acidic), identical to that of Au surfaces (Table 2). An identical repeatability trend to the MPA layer is maintained during the functionalization of the SA surface (Tables 1 and 2). The pretreatment selection appears to again have a pronounced influence on the impedimetric behavior of the tested electrodes once TBA15 was immobilized. Overall, although a general increase in Rtotal was observed across all tested pretreatments (from 1362 ± 401 to 2004 ± 841 Ω [Table 1, “All datasets”)], the extent of this increase is markedly different between various electrode pretreatments. Significant differences in Rtotal values between chemical (1027 ± 266 Ω) and combined (alkaline) (2302 ± 432 Ω) pretreatments were identified at this stage (Table 1, APT layer “†”), indicating substantial variation in the composition of this layer between different pretreatments. Pretreatment repeatability for the APT layer was ranked as chemical > mechanical > combined (alkaline) > electrochemical > combined (acidic) (Table 2). All other pretreatments indicated the addition of aptamers to the surface following the immobilization procedure (Table 1, APT surface “*”), caused by the repulsion of charge by the negatively charged phosphate backbone of the ssDNA.30 The varying response of this layer when comparing each pretreatment is important to note, particularly concerning the increased complexity of the surface when adding the sensing layer of the biosensor. Indications of successful inclusion of the aptamer that is statistically significant increase in Rtotal between SA and APT layers that were not obvious for every pretreatment, despite the relatively

average R total and the repeatability of this response. Considerable variation in the modeled resistance of different surfaces is apparent when comparing the layer-by-layer responses of the biosensor between different electrode responses. To further illustrate the influence of pretreatment on sensor fabrication, Table 2 ranks the electrode pretreatments by their surface reproducibility as established by SEM/ EDX (Figures 2 and 3) together with the repeatability of impedimetric responses of the biosensor layers (Table 1). Because of the low repeatability of individual measurements within layers, several of the individual layers of the combined (acidic) pretreatment were identified to have non-normal distributions of the measured Rtotal via Shapiro−Wilk W testing: as a result, significant difference in Rtotal values across the biosensor layers for that set was evaluated using nonparametric statistical methods (Kruskal−Wallis H test and Dunn’s multiple comparison test). Diverse morphological surface changes34 and the formation of passivating layers15 have been documented from the use of repeated potential cycling in H2SO4. Initially, electrode pretreatment appears to strongly influence the average Rtotal of the electrodes: Rtotal values at bare Au surfaces ranged from 303 Ω to nearly sixfold greater, 1821 Ω (Table 1, Au surface), for combined (alkaline) and combined (acidic), respectively. On the order of decreasing repeatability (i.e., increasing absolute deviation between independent measurements), the pretreatment techniques were ranked: combined (alkaline) > mechanical > chemical > electrochemical > combined (acidic) (Table 2). The repeatability of Rtotal recorded for the Au surfaces corresponded well to the estimated surface homogeneity of the pretreatment, as established by SEM/EDX, but not the apparent elemental composition of the treated surface (Table 2). Together, this indicates a substantial variation in the surface properties imparted by various pretreatments, which was anticipated to occur and is supported by the variances in topology (Figures 2 and 3) and Ctotal (Table S1). The formation of the MPA SAM initially appears to stabilize impedimetric responses across all of the investigated treatments. Obtained Rtotal values between different pretreatments distribute themselves around an average of 1063 ± 347 Ω (Table 1, MPA surface “All datasets”). Simultaneously, the repeatability of obtained Rtotal improved, with the majority of repeatability measurements ranging between ±132 and ±308 5843


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uniform impedimetric response of the underlying SA layers (Table 1). 2.5. Biosensor Responses to Thrombin. Similar to the aptamer layering, EIS responses of THR layers appeared to be strongly influenced by pretreatment selection. Comparison of different pretreatments indicates that almost all biosensor ), caused by the exposure of responses (Table 1, ΔRTHR−APT total thrombin to the biosensor (comparing the THR and APT layers in Table 1) produced an increased impedimetric response. Of the tested pretreatment methods, three produced statistically significant biosensor responses in which the THR layer was significantly different to the APT layer: mechanical, chemical, and combined (alkaline) pretreatment (Table 1), indicating their suitability for biosensor fabrication. These were the three noted for having greater reproducibility at the Au layer (Tables 1 and 2). ) by their Ranking the sensor response (Table 1, ΔRTHR−APT total average responses in Table 1 reveals the following trend in apparent sensitivity: chemical > mechanical > combined (alkaline) ≈ combined (acidic) > electrochemical (acidic) treatment, with the electrochemical treatment producing a statistically insignificant negative response to THR. Cursory observation would conclude that chemical pretreatment THR−APT (Table 1, biosensor resulted in the highest ΔRtotal response). However, this pretreatment also exhibited large variations in the biosensor response (2177 ± 1268 Ω) (Table 1). Ranking biosensor responses on the order of repeatability (Table 2) indicated that repeatability decreases on the order of: mechanical > electrochemical > combined (alkaline) > chemical > combined (acidic). Calculation of the sensors’ response repeatability in the form of relative standard deviation of the response ranged from 38% (ΔRtotal of 1483 ± 573, recorded for mechanically pretreated electrodes) to 153% [ΔRct of 1220 ± 1865 Ω, for electrochemical (acidic) Table 2]. The overall sensor response of ∼1100 Ω recorded for all datasets (Table 1) is reasonably similar to maximal biosensor responses reported by other authors using a broadly similar apparatus and experimental conditions (∼1500 Ω).19,25,28,30 In summary, despite utilizing a common and wellestablished aptasensor design throughout this study, substantial variations in the impedimetric responses and response repeatability evidently depend on the pretreatment elected. These changes are apparently more closely related to characteristics imparted to the surface topology and chemical homogeneity compared to the surface elemental composition of the electrode surface or the biological components of the aptasensor.

Notably, treatments that resulted in surfaces exhibiting poor impedimetric repeatability at unmodified bare electrode surfaces led to responses with low interelectrode repeatability during testing of the biosensor response to the thrombin target. This indicates that pretreatment efficacy can be assessed at this stage of electrode fabrication, allowing the selection of a pretreatment method to produce a suitable gold surface. Evaluation of the influence of pretreatment during the layerby-layer fabrication of the biosensors indicated that these layers produced mainly consistent responses across the tested pretreatments (such as those occurred during the MPA monolayer or the SA layer). Information obtained from these layers, therefore, poorly predicts the response reproducibility of fully assembled biosensors. Of the tested methods, alkaline treatment demonstrated high response repeatability, and a significant Rtotal increases throughout the layer-by-layer fabrication, validating successful biosensor fabrication. Although the findings by Fischer et al. (2009),13 demonstrated that the combined (alkaline) pretreatment improves electrode cleanliness and increases elemental gold at a bare electrode, our study suggests that the selection of pretreatment also provides beneficial properties toward interelectrode repeatability and reliable biological monitoring for an impedimetric aptasensor, which satisfies key requirements concerning mass production of impedimetric sensor devices.

4. EXPERIMENTAL SECTION 4.1. Materials. Unless otherwise stated, all reagents had purity of analytical grade (≥95%) or higher. Water was purified by a Millipore Milli-Q System and was of double-distilled quality (≥18.2 MΩ cm). Sodium phosphate, potassium phosphate, sulfuric acid, potassium hydroxide, hydrogen peroxide, sodium hydroxide, and potassium ferrocyanide (K4[Fe(CN)6]) were sourced from Merck. Potassium ferricyanide (K3[Fe(CN)6]), Nhydroxy-succinimide (NHS), N-(3-dimethylaminopropyl)-N′ethylcarbodiimide hydrochloride (EDC), ethylenediaminetetraacetic acid (EDTA), β-mercaptopropionic acid, Trizma base, potassium chloride, sodium chloride, magnesium chloride, calcium chloride, 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), and aluminum oxide microparticles (≤10 μm) were sourced from Sigma-Aldrich. The binding buffer was prepared using 20 mM Tris, 140 mM NaCl, 5 mM KCl, 1 mM MgCl2, and 1 mM CaCl2, adjusted to pH 7.4 with NaOH. Phosphate-buffered saline (PBS) was prepared using 10 mM Na2HPO4, 1.8 mM KH2HPO4, 2.7 mM KCl, and 137 mM NaCl at pH 7.4. Streptavidin (category number: S4762) and human αthrombin (category number: T1063) were sourced from Sigma-Aldrich. Stock solutions of streptavidin were prepared in PBS at a dissolved concentration of ∼13 U mL−1, whereas thrombin stocks were formulated to concentrations of 4 μM using 10 mM HEPES (adjusted with NaOH to pH 6.8). Protein stocks were prepared fresh on a daily basis. TBA15, 5′-TTTTTTTTTTGGTTGGTGTGGTTGG-3′, with biotin conjugated to the 5′ end, was synthesized by WhiteSci IDT, formulated to a stock solution of 100 μM in TE buffer (10 mM Tris, 1 mM EDTA, pH 8.0) and stored at −20 °C until use. 4.2. Working Electrode Pretreatment Procedures. The influence of electrode pretreatment on the biosensor performance was tested using six different gold stalk working electrodes

3. CONCLUSIONS The selection of the electrode pretreatment regimen resulted in significant differences in impedimetric responses, despite uniform methods of aptasensor construction and apparent unity of these responses at several layers during fabrication of the biosensor. This effect appears to be a dominant factor in biosensor responses even though care was taken to select a well-characterized model aptasensor system and despite the use of uniform protocols in constructing various layers comprising the biosensor. SEM/EDX investigations indicate that differences in surface composition and topography imparted by the tested pretreatments significantly influenced the impedimetric characterization of the aptasensor assembly and performance. 5844


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with NOVA 1.9 software. A conventional three-electrode cell was employed, using a gold working electrode, platinum auxiliary electrode, and silver/silver chloride reference electrode, all of which were sourced from Bioanalytical Systems Inc. Measurements were cenetred on the open-circuit potential (∼0.28 V vs Ag/AgCl for all datasets). A sinusoidal potential with an amplitude of 10 mV (root-mean square) was applied, with an oscillating frequency range between 10 kHz and 0.01 Hz. Obtained spectra were fitted to a modified Randles equivalent circuit, containing an additional time constant, to include impedimetric contributions for a protein film contacting the electrode.37 4.6. Data Analysis. The response of a biosensor layer (APT) to the presence of the target molecule (THR) is the measure of success of an impedimetric biosensor and is the most commonly reported metric of these types of sensor. To calculate this, the impedimetric responses of freshly prepared THR layers were contrasted with responses of freshly prepared APT layers, ensuring the independence of measurements and eliminating the possibility of impedimetric responses arising because of repetitive testing of the same electrode.19 The biosensor response was calculated as described by eq 1

sourced from Bioanalytical Systems (1.6 mm electrode diameter). Following the pretreatment methodologies detailed below, all electrodes (n = 6) were rinsed in ddH2O and stored in absolute ethanol until use. 4.2.1. Mechanical Polishing. Electrodes were polished using alumina oxide slurry on a Buehler pad for 2 min. Thereafter, electrodes were rinsed and sonicated in ddH2O for 3 min.2,10 4.2.2. Chemical. Polished electrodes (Section 4.2.1) were exposed to ∼100 °C piranha solution (1:3 of 30% H2O2/50% H2SO4) for 1 min.10−13 4.2.3. Electrochemical (Acidic). Polished electrodes (Section 4.2.1) were treated by cyclic voltammetry in 0.5 M H2SO4 electrolyte, sweeping the working electrode potential from −0.1 to +1.6 V for 30 cycles at a scan rate of 0.3 V s−1.10,12 4.2.4. Combined (Acidic). Electrodes were sequentially pretreated using mechanical, chemical, and electrochemical treatments, as detailed in Sections 4.2.1; 4.2.2; and 4.2.3.10,18 4.2.5. Combined (Alkaline). Polished electrodes (Section 4.2.1) were exposed to 50 mM KOH/H2O2 (3:1) for 10 min and thereafter rinsed with water. A single linear potential sweep between −0.2 and −1.2 V was applied at a sweep rate of 50 mV s−1 in 50 mM KOH electrolyte.13,14,36 4.3. Layer-By-Layer Biosensor Construction. A thrombin-sensing biosensor was constructed using a layer-by-layer approach to surface gold electrodes with TBA15. Electrode surfaces pretreated as detailed in Sections 4.2.1 to 4.2.5 are designated as (Au). Au surfaces were immersed in a 10 mM βmercaptopropionic acid ethanolic solution for 2 h (MPA) and thoroughly rinsed using ethanol. The resulting β-mercaptopropionate SAM was activated for 10 min using 40 mM EDC and 10 mM NHS in 10 mM HEPES, pH 5.5. Thereafter, streptavidin (0.013 U) was immobilized to the active surface for 20 min (SA). Subsequently, 1 μL of a 1 μM solution of the TBA15 aptamer, prepared in the binding buffer, was dropped onto the surface of the electrodes for 45 min (APT). To test the impedimetric aptasensors fabricated, 1 μL of a 2 μM solution of THR was introduced onto the surface and incubated for 15 min for binding (THR). Between layering, the electrodes were rinsed thoroughly with PBS. To avoid surface contamination of the sensors by repetitive EIS measurements on the same electrode,19 each surface was freshly prepared, characterized by EIS and cleaned by mechanical polishing. 4.4. Scanning Electron Microscopy and Energy Dispersive X-ray. Imaging and elemental characterization of freshly pretreated electrode surfaces was performed by a Vega Tescan with a retractable backscattered electron detector, equipped with EDX. SEM images were recorded at an acceleration voltage of 20 kV and a magnification of 2.00 k×. Surface heterogeneity estimation was determined by the Fiji image-processing package (https://fiji.sc/) taking into account the distribution of pixel intensity of approximately 280 000 pixels per SEM image. EDX was calibrated to a pure copper disk standard prior to elemental analysis. A foreshortened gold stalk working electrode was separately pretreated with each protocol, and the analysis was performed on three different topographical areas of the pretreated electrode for statistical purposes. 4.5. Electrochemical Impedance Spectroscopy. EIS measurements were performed in PBS, pH 7.4 containing an equimolar solution of 10 mM [Fe(CN)6]3−/4−. Measurements were generated using an AutoLab PGSTAT302N, controlled

THR APT Biosensor response = X̅ (R total )−X̅ (R total )

(1)

APT where X̅ (RTHR total ) and X̅ (Rtotal ) are the averages of the sums of resistor-circuit equivalent elements modeled from the impedimetric data (Rtotal) obtained from the THR and APT layers, respectively. The standard deviation for biosensor response was computed as the maximum probable error, calculated using the standard deviations of the THR and APT datasets. All impedimetric measurements were performed with six individual electrode replicates, using six different electrodes. Presented values and error bars represent mean and standard deviation from the mean, respectively. No outliers were omitted to simulate mass production and determine the interelectrode reproducibility. Statistical tests were performed using Statistica 13, setting significance, ρ, to 0.05 for all statistical tests. The significance of normally distributed data was assigned using a one-way ANOVA, and significantly differing groups were identified using Tukey’s Honest significant difference post hoc test. Where appropriate, non-normally distributed groups were identified via the Shapiro−Wilk W test; for datasets containing these, a significant difference was tested using the Kruskal− Wallis H test and significantly different datasets were subsequently identified using Dunn’s Multiple Comparison post hoc test.

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S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsomega.9b00075. Mean and variance (standard deviation) obtained for modeled parameters (Warburg impedance, double-layer capacitance, and charge-transfer resistance) of the equivalent electrical circuit for all of the tested surfaces and pretreatments (PDF) 5845


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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Ronen Fogel: 0000-0002-5564-946X Author Contributions

L.S.J.Ho: first author; conducted the investigation; collected the data; performed the analyses from which data for this publication has been drawn; prepared the original draft of the published work. J.L.L.: supervised research reported in this publication; reviewed and edited subsequent drafts of the published work; acquisition of the financial support to conduct the research. R.F.: conceived and designed the original research direction; designed experiments; supervised research reported in this publication; performed data analyses; reviewed and edited subsequent drafts of the published work. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors acknowledge the study supported by the DST/ Mintek Nanotechnology Innovation Centre (NIC). R.F. acknowledges the South African Medical Research Council (SAMRC) for their Career Development Award. Research reported in this publication was supported by the South African Medical Research Council. The SAMRC was not involved in the design, analysis, interpretation, or preparation of this manuscript. J.L.L. acknowledges the DST/NRF South African Research Chair in Biotechnology Innovation & Engagement.

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Certain Methods of Electrode Pretreatment Create Misleading

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