Electrochemical Ultrasensitive Sensing of Uric Acid on Non-Enzymatic Porous Cobalt Oxide Nanosheets-Based Sensor

Transition metal oxide (TMO)-based nanomaterials are effectively utilized to fabricate clinically useful ultra-sensitive sensors. Different nanostructured nanomaterials of TMO have attracted a lot of interest from researchers for diverse applications. Herein, we utilized a hydrothermal method to develop porous nanosheets of cobalt oxide. This synthesis method is simple and low temperature-based. The morphology of the porous nanosheets like cobalt oxide was investigated in detail using FESEM and TEM. The morphological investigation confirmed the successful formation of the porous nanosheet-like nanostructure. The crystal characteristic of porous cobalt oxide nanosheets was evaluated by XRD analysis, which confirmed the crystallinity of as-synthesized cobalt oxide nanosheets. The uric acid sensor fabrication involves the fixing of porous cobalt oxide nanosheets onto the GCE (glassy carbon electrode). The non-enzymatic electrochemical sensing was measured using CV and DPV analysis. The application of DPV technique during electrochemical testing for uric acid resulted in ultra-high sensitivity (3566.5 µAmM−1cm−2), which is ~7.58 times better than CV-based sensitivity (470.4 µAmM−1cm−2). Additionally, uric acid sensors were tested for their selectivity and storage ability. The applicability of the uric acid sensors was tested in the serum sample through standard addition and recovery of known uric acid concentration. This ultrasensitive nature of porous cobalt oxide nanosheets could be utilized to realize the sensing of other biomolecules.


Introduction
Uric acid (UA) plays an important role in various biological processes and physiological functions in humans and higher species [1]. Bodily fluids (i.e., serum, urine, and saliva) contain UA. Mainly, the liver and intestines are the primary sites of UA production. However, the majority of UA is removed by urate transporters in the kidneys and intestines. Unusually low, excessive, or variable UA concentration is an indicator of various diseases (i.e., pneumonia, gout, leukemia, type 2 diabetes, chronic renal diseases, toxemia during pregnancy, multiple sclerosis, metabolic disorders, and hypertension) [2][3][4][5]. Therefore, monitoring UA concentration becomes crucial for early disease diagnosis since UA acts as a diagnostic marker for UA-concentration associate diseases.

Porous Cobalt Oxide Nanosheets Synthesis
A simple low-cost hydrothermal process is used to synthesize porous cobalt oxide nanosheets (Scheme 1). For synthesis, first, a precursor solution of Co(NO 3 ) 2 .6H 2 O (0.58 g) was prepared in 10 mL DI water, and then NaOH (0.2 g) solution was dropwise added while vigorous stirring for 30 min to obtain a homogenous mixture. The prepared solution was transferred into an autoclave vessel (Teflon-lined stainless steel) and put into a hot air oven at 150 • C for 6h. On completion of the reaction, the autoclave was cooled down and the powder sample was washed multiple times with DI and ethanol. Finally, the black precipitate was dried (at 60 • C) and annealed (at 500 • C) for 3h before characterizing it in detail.
when analyzed with the DPV technique. Additionally, selectivity, stability, and applicability in serum samples were evaluated. This porous cobalt oxide nanosheet-based nonenzymatic UA sensor offers better sensitivity when compared to CV-measured sensitivity (470.4 µ AmM −1 cm −2 ).

Porous Cobalt Oxide Nanosheets Synthesis
A simple low-cost hydrothermal process is used to synthesize porous cobalt oxide nanosheets (Scheme 1). For synthesis, first, a precursor solution of Co(NO3)2.6H2O (0.58 g) was prepared in 10 mL DI water, and then NaOH (0.2 g) solution was dropwise added while vigorous stirring for 30 min to obtain a homogenous mixture. The prepared solution was transferred into an autoclave vessel (Teflon-lined stainless steel) and put into a hot air oven at 150 °C for 6h. On completion of the reaction, the autoclave was cooled down and the powder sample was washed multiple times with DI and ethanol. Finally, the black precipitate was dried (at 60 °C) and annealed (at 500 °C ) for 3h before characterizing it in detail.

Scheme 1.
Schematic showing the synthesis process of porous cobalt oxide nanosheets, sensor fabrication process, and measurement techniques used. Scheme 1. Schematic showing the synthesis process of porous cobalt oxide nanosheets, sensor fabrication process, and measurement techniques used.

Sensor Fabrication
The porous cobalt oxide nanosheets-based non-enzymatic UA sensor was fabricated using a conductive binder along with cobalt oxide nanosheets (Scheme 1). In brief, a slurry of 0.01 g porous cobalt oxide nanosheets and 50 µL conductive binder (2-(2-Butoxyethoxy)ethyl acetate) was prepared using mortar and pestle. The slurry was sonicated for 10 min before fixing onto the working electrode surface. Different amounts of slurry were fixed on the working electrode to optimize the most suitable amount of slurry, which gives the best sensing performance (for example, 4 µL was the optimized amount). The porous cobalt oxide nanosheet-based sensor was dried at 60 • C for 6h and kept at room temperature.

Materials Characterization and Electrochemical Analysis of Sensor
The FESEM (field-emission-scanning electron microscope; Zeiss, Sigma) was utilized to analyze porous sheet-like cobalt oxide nanostructures morphology. The structural analysis was examined using XRD (Rigaku), where Cu-Kα X-ray radiation (λ = 1.5418 Å), current (30 mA), and voltage (40 kV) were used. A more detailed study of porous sheet-like cobalt oxide nanostructures was characterized with TEM (TECNAI G20; accelerating voltage = 200 kV). The ASAP 2010 analyzer and Barrett-Joyner-Halenda (BJH) method were utilized for nitrogen adsorption-desorption analysis (at 77 K) and pore size distribution determination, respectively.
For the electrochemical measurements of the fabricated porous cobalt oxide nanosheetsbased non-enzymatic UA sensor, a compact and portable potentiostat/impedance analyzer "PalmSens4" was used. The CV and EIS were used to evaluate the best-performing sensor using the optimum amount of porous cobalt oxide nanosheets in a redox probe solution ([Fe(CN) 6 ] 3−/4− ). During EIS measurement, frequency range and applied potential were set to 0.01 Hz to 100 kHz and 0.25 V, respectively. The sensing performance of the fabricated porous cobalt oxide nanosheet-based non-enzymatic UA sensor was evaluated using CV and DPV techniques.

Characterizations of Porous Cobalt Oxide Nanostructures
The surface morphology and crystallinity of as-synthesized cobalt oxide nanostructure were characterized using FESEM, TEM, and XRD. Figure 1a-c displays the surface morphology of the as-synthesized cobalt oxide nanostructure. The low-magnification images of cobalt oxide nanostructures reveal that the synthesized nanomaterial is obtained in bulk amounts with irregular shapes and sizes (Figure 1a,b). The high-magnification image shows that cobalt oxide nanostructures bear porous sheet-like morphology (Figure 1c). Only the surface morphology is smooth, with uniform pores present on the surface. Figure 2d shows the XRD pattern of as-synthesized cobalt oxide nanomaterial. The XRD pattern confirms the crystalline nature of nanomaterial, and the obtained pattern is well indexed (JCPDS card no. 42-1467) [30].  Figure 2). The TEM images show the porous sheet-like nanostructure of cobalt oxide. Additionally, the SAED pattern (Figure 2c) of the porous cobalt oxide sheet-like nanostructure suggests the crystal nature. These observations are supported by FESEM images. The surface area and pore size of the porous cobalt oxide sheet-like nanostructure was analyzed using BET (Brunauer-Emmett-Teller) analysis, shown in Figure 2d. The isotherm pattern indicates the porous nature of nanomaterial. The obtained BET surface area of porous cobalt oxide sheet-like nanostructure is around~166 m 2 /g. A narrow pore size distribution from 3 to 7 nm was obtained with an average pore size of~5 nm (Inset of Figure 2d). The high surface area and small pore size of the porous cobalt oxide sheet-like nanostructure will offer a better catalytic for electrochemical reactions.

Electrochemical Studies using CV and EIS Techniques
The electrochemical properties of bare GCE and cobalt oxide/GCE electrodes were analyzed using EIS and CV techniques.  (Figure 3). The EIS was carried out at 0.25 V in the frequency range of 0.01 Hz to 100 kHz. Nyquist plots in Figure 3a show two frequency regions, one at a higher and another at a lower frequency region. Cobalt oxide/GCE electrode showed a small semicircle (at a higher frequency region) and a straight line (at a lower frequency region), which suggests a perfect diffusion-controlled process during the electron transfer reaction. The bare GCE electrode showed a higher charge transfer resistance (R ct ) compared to the modified GCE electrode. The bode plots of bare GCE and cobalt oxide/GCE electrodes are illustrated in Figure 3b,c, respectively. It can be seen from these bode plot curves that the modifying GCE with cobalt oxide decreased the interfacial impedance. Additionally, the bode angle was decreased after GCE surface modification with cobalt oxide. The shift of lower frequency peak for cobalt oxide modified GCE suggests the more prominent electron transfer process compared to the bare GCE electrode [31].   The CV data obtained for the bare GCE and cobalt oxide/GCE electrodes agree with the EIS data. The CV response curves were recorded from −0.2V to + 0.8V (vs. Ag/AgCl) at a fixed scan rate (50 mV/s) (Figure 4a). Clear redox peaks (i.e., oxidation and reduction peaks) can be seen in the obtained CV response curves, where the cobalt oxide/GCE electrode showed improved oxidation peak current value compared to bare GCE. Additionally, the effect of scan rate on the cobalt oxide/GCE electrode's electron transfer characteristics was investigated by measuring CV response curves at different scan rates (i.e., 10-250 mV/s) ( Figure 4b). It can be seen from the CV curves that the oxidation and reduction peak current values increase with the scan rate increase. The CV curve shape and the values of peak potential separation indicate a diffusion-controlled process in the redox probe solution.
To verify the diffusion-controlled process over the cobalt oxide/GCE electrode, a plot of current peak vs. square root of scan rate is plotted in Figure 4c. A perfect linear relationship was observed between the square root of the scan rate and the value of the peak current. This further confirms the diffusion-controlled process over the surface of the modified electrode [32][33][34].

Electrochemical Studies using CV and EIS Techniques
The electrochemical properties of bare GCE and cobalt oxide/GCE electrodes were analyzed using EIS and CV techniques. The electron transfer reaction of bare GCE and cobalt oxide/GCE electrodes for the redox probe solution of [Fe(CN)6] 3−/4− with KCl (0.1 M) determine the kinetic parameters (i.e., electron transfer rate and charge transfer resistance) ( Figure 3). The EIS was carried out at 0.25 V in the frequency range of 0.01 Hz to 100 kHz. Nyquist plots in Figure 3a show two frequency regions, one at a higher and another at a lower frequency region. Cobalt oxide/GCE electrode showed a small semicircle (at a higher frequency region) and a straight line (at a lower frequency region), which suggests a perfect diffusion-controlled process during the electron transfer reaction. The bare GCE electrode showed a higher charge transfer resistance (Rct) compared to the modified GCE electrode. The bode plots of bare GCE and cobalt oxide/GCE electrodes are illustrated in Figure 3b,c, respectively. It can be seen from these bode plot curves that the modifying GCE with cobalt oxide decreased the interfacial impedance. Additionally, the bode angle was decreased after GCE surface modification with cobalt oxide. The shift of lower frequency peak for cobalt oxide modified GCE suggests the more prominent electron transfer process compared to the bare GCE electrode [31].
The CV data obtained for the bare GCE and cobalt oxide/GCE electrodes agree with the EIS data. The CV response curves were recorded from −0.2V to + 0.8V (vs. Ag/AgCl) at a fixed scan rate (50 mV/s) (Figure 4a). Clear redox peaks (i.e., oxidation and reduction peaks) can be seen in the obtained CV response curves, where the cobalt oxide/GCE electrode showed improved oxidation peak current value compared to bare GCE. Additionally, the effect of scan rate on the cobalt oxide/GCE electrode's electron transfer characteristics was investigated by measuring CV response curves at different scan rates (i.e., 10-250 mV/s) (Figure 4b). It can be seen from the CV curves that the oxidation and reduction peak current values increase with the scan rate increase. The CV curve shape and the values of peak potential separation indicate a diffusion-controlled process in the redox probe solution. To verify the diffusion-controlled process over the cobalt oxide/GCE electrode, a plot of current peak vs. square root of scan rate is plotted in Figure 4c. A perfect linear relationship was observed between the square root of the scan rate and the value of the peak current. This further confirms the diffusion-controlled process over the surface of the modified electrode [32][33][34].

Sensing Performance Characterization using CV Technique
The response of the cobalt oxide/GCE sensor was characterized towards uric acid before a detailed analysis of sensing performance. The CV analysis of the cobalt oxide/GCE sensor was performed in PBS without and with uric acid (10 µ M) at 50 mV/s (Figure 5a). When CV analysis was done in PBS, there was no noticeable peak in the CV curve. However, in 10 µ M uric acid, a noticeable peak of uric acid oxidation was present at 0.6 voltage. The possible and most accepted detection mechanism for uric acid oxidation involves the transfer of two-electron/two-proton, which enhances the response during sensing measurement (Scheme 2) [35][36][37]. Additionally, the porous nature of nanosheets

Sensing Performance Characterization using CV Technique
The response of the cobalt oxide/GCE sensor was characterized towards uric acid before a detailed analysis of sensing performance. The CV analysis of the cobalt oxide/GCE sensor was performed in PBS without and with uric acid (10 µM) at 50 mV/s (Figure 5a). When CV analysis was done in PBS, there was no noticeable peak in the CV curve. However, in 10 µM uric acid, a noticeable peak of uric acid oxidation was present at 0.6 voltage. The possible and most accepted detection mechanism for uric acid oxidation involves the transfer of two-electron/two-proton, which enhances the response during sensing measurement (Scheme 2) [35][36][37]. Additionally, the porous nature of nanosheets provided abundant catalytic sites due to the large surface area. Moreover, cobalt oxide is a p-type semiconductor, which could provide excess hole concentration and help to capture the electrons during uric acid oxidation. Also, we observed the irreversible oxidation peak in the CV curve that indicated swift electron transfer between the GCE and porous cobalt oxide during the electrochemical detection of uric acid. provided abundant catalytic sites due to the large surface area. Moreover, cobalt oxide is a p-type semiconductor, which could provide excess hole concentration and help to capture the electrons during uric acid oxidation. Also, we observed the irreversible oxidation peak in the CV curve that indicated swift electron transfer between the GCE and porous cobalt oxide during the electrochemical detection of uric acid.
To evaluate the sensing performance (i.e., sensitivity, detection range, and detection limit), the CV response curves of the cobalt oxide/GCE sensor were measured with increasing concentrations of uric acid (0-2500 µ M) as shown in Figure 5b. In this figure, the CV curves showed an increase in current with increasing uric acid concentration. A graph of peak current (µ A) vs. uric acid concentration (µ M) was drawn (Figure 5c). From this graph, two regions (linear and non-linear) can be seen. In general, the non-linear region signifies the saturation of the current response of the cobalt oxide/GCE sensor on those uric acid concentrations (i.e., high uric acid concentration). Further, the linear region of the sensor response is taken and a calibration plot (peak current (µ A) vs. uric acid concentration (µ M) is plotted, shown in the inset of Figure 4c. The sensor responded linearly up to 1000 µ M of uric concentration (regression coefficient (R 2 ) = 0.9978). From the slope, we calculated the sensitivity of 470.4 µ AmM −1 cm −2 [38]. Additionally, based on the S/N ratio = 3, the detection limit was calculated to be 10 µ M. The obtained sensitivity, linear range, and detection limit were comparatively better than most of the previously reported literature ( Table 1). The good sensing performance is due to high surface-tovolume ratio and the presence of large active sites on as-synthesised porous nanosheets like nanomaterial.  To evaluate the sensing performance (i.e., sensitivity, detection range, and detection limit), the CV response curves of the cobalt oxide/GCE sensor were measured with increasing concentrations of uric acid (0-2500 µM) as shown in Figure 5b. In this figure, the CV curves showed an increase in current with increasing uric acid concentration. A graph of peak current (µA) vs. uric acid concentration (µM) was drawn (Figure 5c). From this graph, two regions (linear and non-linear) can be seen. In general, the non-linear region signifies the saturation of the current response of the cobalt oxide/GCE sensor on those uric acid concentrations (i.e., high uric acid concentration). Further, the linear region of the sensor response is taken and a calibration plot (peak current (µA) vs. uric acid concentration (µM) is plotted, shown in the inset of Figure 4c. The sensor responded linearly up to 1000 µM of uric concentration (regression coefficient (R 2 ) = 0.9978). From the slope, we calculated the sensitivity of 470.4 µAmM −1 cm −2 [38]. Additionally, based on the S/N ratio = 3, the detection limit was calculated to be 10 µM. The obtained sensitivity, linear range, and detection limit were comparatively better than most of the previously reported literature ( Table 1). The good sensing performance is due to high surface-to-volume ratio and the presence of large active sites on as-synthesised porous nanosheets like nanomaterial.

Sensing Performance Characterization using DPV Technique
The DPV technique is more sensitive than CV due to the minimization of capacitive current. For this reason, we utilized the DPV technique to evaluate the sensing performance of the cobalt oxide/GCE sensor. Initially, the electrochemical behavior of the cobalt oxide/GCE sensor was measured. Figure 6a shows DPV curves obtained in PBS buffer (pH 7.4) without and with 10 µM uric acid. The appearance of the uric acid oxidation peak at 0.45 potential (vs. Ag/AgCl), compared to the DPV curve recorded in PBS buffer solution, indicated the sensitivity nature of the cobalt oxide/GCE sensor towards uric acid. Additionally, when measuring CV response (as shown in Figure 5a), it was seen that uric acid oxidation is an irreversible process.
in PBS buffer. The obtained DPV curves are shown in Figure 6b, where an increase in current can be seen with increased uric acid concentration. A plot of peak current (µ A) vs. uric acid concentration (µ M) is shown in Figure 6c along with the calibrated plot of the linear range of the sensor in the inset. The cobalt oxide/GCE sensor showed a linear range of up to 800 µ M uric acid concentration with R 2 of 0.9929. However, the current level was decreased at a higher uric acid concentration due to the saturation of electrocatalysis of uric acid on the electrode surface. The sensitivity of the cobalt oxide/GCE sensor was calculated by using the standard equation of slope of the calibrated plot/working electrode surface area. The sensor showed the highest sensitivity of 3566.5 µ AmM −1 cm −2 . The limit of detection was 12 µ M. The achieved sensing performance results are shown in Table 1. As shown in Table 1, the cobalt oxide/GCE sensor showed ultra-high sensitivity compared to previously reported literature [11,24,30,[39][40][41][42][43][44][45][46]. Furthermore, the DPV technique showed ~7.58 times high sensitivity (3566.5 µ AmM −1 cm −2 ) compared to CV's measured sensitivity (470.4 µ AmM −1 cm −2 ). These results confirmed the fact that DPV is a more sensitive technique as compared to CV. However, the main reason for getting high sensing performance is attributed to large active sites and the surface-to-volume ratio of assynthesised porous nanosheets like nanomaterial.

Interference and Stability Tests of Cobalt Oxide/GCE Sensor
We investigated the selectivity of the cobalt oxide/GCE sensor for uric acid detection in the presence of possible interferences. Eight possible interfering species, including lactic acid, L-cysteine, glucose, urea, fructose, sodium chloride, and potassium chloride, were taken for the selectivity study. Figure 7a illustrates CV curves for 25 µ M uric acid only and 25 µ M uric acid and 100 µ M of each interfering species (i.e., lactic acid, L-cysteine, glucose, urea, fructose, sodium chloride, and potassium chloride). There is a slight increase (positive interference) in CV response with a high concentration of interfering species. Based on this result, the cobalt oxide/GCE sensor is selective for uric acid determination. Additionally, we evaluated the sensor stability after storing sensor at room temperature and measuring the response after 30 and 45 days for 25 µ M uric acid ( Figure 7b). As shown in Figure 7b, the sensor showed good stability and maintained 97.4% of its current peak after 45 days of storage. Additionally, the low RSD of 2.6% indicated good stability. Then, DPV was performed with increasing uric acid concentration (up to 2500 µM) in PBS buffer. The obtained DPV curves are shown in Figure 6b, where an increase in current can be seen with increased uric acid concentration. A plot of peak current (µA) vs. uric acid concentration (µM) is shown in Figure 6c along with the calibrated plot of the linear range of the sensor in the inset. The cobalt oxide/GCE sensor showed a linear range of up to 800 µM uric acid concentration with R 2 of 0.9929. However, the current level was decreased at a higher uric acid concentration due to the saturation of electrocatalysis of uric acid on the electrode surface. The sensitivity of the cobalt oxide/GCE sensor was calculated by using the standard equation of slope of the calibrated plot/working electrode surface area. The sensor showed the highest sensitivity of 3566.5 µAmM −1 cm −2 . The limit of detection was 12 µM. The achieved sensing performance results are shown in Table 1. As shown in Table 1, the cobalt oxide/GCE sensor showed ultra-high sensitivity compared to previously reported literature [11,24,30,[39][40][41][42][43][44][45][46]. Furthermore, the DPV technique showed 7.58 times high sensitivity (3566.5 µAmM −1 cm −2 ) compared to CV's measured sensitivity (470.4 µAmM −1 cm −2 ). These results confirmed the fact that DPV is a more sensitive technique as compared to CV. However, the main reason for getting high sensing performance is attributed to large active sites and the surface-to-volume ratio of as-synthesised porous nanosheets like nanomaterial.

Interference and Stability Tests of Cobalt Oxide/GCE Sensor
We investigated the selectivity of the cobalt oxide/GCE sensor for uric acid detection in the presence of possible interferences. Eight possible interfering species, including lactic acid, L-cysteine, glucose, urea, fructose, sodium chloride, and potassium chloride, were taken for the selectivity study. Figure 7a illustrates CV curves for 25 µM uric acid only and 25 µM uric acid and 100 µM of each interfering species (i.e., lactic acid, L-cysteine, glucose, urea, fructose, sodium chloride, and potassium chloride). There is a slight increase (positive interference) in CV response with a high concentration of interfering species. Based on this result, the cobalt oxide/GCE sensor is selective for uric acid determination. Additionally, we evaluated the sensor stability after storing sensor at room temperature and measuring the response after 30 and 45 days for 25 µM uric acid ( Figure 7b). As shown in Figure 7b, the sensor showed good stability and maintained 97.4% of its current peak after 45 days of storage. Additionally, the low RSD of 2.6% indicated good stability. Finally, we tested selectivity tests in the presence of ascorbic acid, dopamine, and urea (Figure 7c). In the presence of these species, a slight increase in current response was noticed. However, no other peaks were noticed. Finally, we tested selectivity tests in the presence of ascorbic acid, dopamine, and urea ( Figure 7c). In the presence of these species, a slight increase in current response was noticed. However, no other peaks were noticed.

Analysis of Real Serum Sample
To determine whether the cobalt oxide/GCE sensor was suitable for uric acid detection in human serum samples (obtained from Sigma−Aldrich; H4522). We used a standard addition-(known uric acid concentration) based method to estimate the recovery results of the added uric acid concentration in a serum sample. The recovery (%) was calculated using formula [Recovery (%) = Calculated uric acid concentration  100/Added uric acid concentration]. The obtained data are shown in Table 2. Recovery results showed that the cobalt oxide/GCE sensor was suitable for uric acid determination in the real sample.

Conclusions
In this study, a low temperature-based hydrothermal method was utilized to synthesize porous nanosheets-like cobalt oxide nanostructures. The crystallinity and morphology of as-synthesized cobalt oxide nanostructures were tested using direct techniques (i.e., FESET, TEM, and XRD). The obtained results showed the successful formation of the porous nanosheet-like nanostructure that bears good crystallinity. The possibility of using such porous nanosheets-like cobalt oxide nanostructures in the sensor was tested by using electrochemical methods, such as CV and EIS. Based on the obtained data, sensing performance evaluation using CV and DPV techniques indicated high sensitivity. The DPV is a more sensitive technique as compared to CV. In this context, DVP data showed ultra-high sensitivity (3566.5 µ AmM −1 cm −2 ), which was ~7.58 times better than CV-based sensitivity (470.4 µ AmM −1 cm −2 ). Additionally, the cobalt oxide/GCE sensor exhibited good selectivity

Analysis of Real Serum Sample
To determine whether the cobalt oxide/GCE sensor was suitable for uric acid detection in human serum samples (obtained from Sigma−Aldrich; H4522). We used a standard addition-(known uric acid concentration) based method to estimate the recovery results of the added uric acid concentration in a serum sample. The recovery (%) was calculated using formula [Recovery (%) = Calculated uric acid concentration × 100/Added uric acid concentration]. The obtained data are shown in Table 2. Recovery results showed that the cobalt oxide/GCE sensor was suitable for uric acid determination in the real sample.

Conclusions
In this study, a low temperature-based hydrothermal method was utilized to synthesize porous nanosheets-like cobalt oxide nanostructures. The crystallinity and morphology of as-synthesized cobalt oxide nanostructures were tested using direct techniques (i.e., FE-SET, TEM, and XRD). The obtained results showed the successful formation of the porous nanosheet-like nanostructure that bears good crystallinity. The possibility of using such porous nanosheets-like cobalt oxide nanostructures in the sensor was tested by using electrochemical methods, such as CV and EIS. Based on the obtained data, sensing performance evaluation using CV and DPV techniques indicated high sensitivity. The DPV is a more sensitive technique as compared to CV. In this context, DVP data showed ultra-high sensitivity (3566.5 µAmM −1 cm −2 ), which was~7.58 times better than CV-based sensitivity (470.4 µAmM −1 cm −2 ). Additionally, the cobalt oxide/GCE sensor exhibited good selectivity during uric acid measuring in the interfering species. The stability and applicability of the cobalt oxide/GCE sensor were tested, which showed good stability and applicability in a serum sample. Nevertheless, this work contributes to obtaining ultra-high sensitivity using porous cobalt oxide nanosheet-like nanostructures and provides the further possibility to improve sensing performance with surface modification of nanosheets using other metal/metal oxides.