Peroxidase Mimicking V2O5 Nanozymes as the Spectrophotometric Sensor for the Determination of Glucose in Human Serum Sample Employing New Chromogenic Co-Substrates

Peroxidase Mimicking V2O5 Nanozymes as the Spectrophotometric Sensor for the Determination of Glucose in Human Serum Sample Employing New Chromogenic Co-Substrates | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Peroxidase Mimicking V 2 O 5 Nanozymes as the Spectrophotometric Sensor for the Determination of Glucose in Human Serum Sample Employing New Chromogenic Co-Substrates Nikhil Y Gangadhara, Manju. B, P Kiran Kumar, Honnur Krishna, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5318695/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Enzyme mimics are developed as an alternative to natural enzymes to overcome the inherent limitations of natural enzymes. Among different types of enzyme mimics, nanozymes gained importance due to their tuneable catalytic properties. In this article, we discuss the peroxidase behaviour of different shape V 2 O 5 nanoparticles (NPs). A simple spectrophotometric method is presented for the quantification of glucose and H 2 O 2 using novel chromogenic reagents. The NPS are characterized with SEM, DLS, EDS, FTIR and XRD. From SEM images, based on the morphology, the NPs were named as vanadium nanosheets (VNShs), nanoflowers (VNFws) and nanospheres (VNSps). The average crystalline size of the nanoparticles is calculated using XRD data from Scherrer’s equation and Williamson-Hall plot and was found to be 45.42, 45.7nm for VNShs, 29.14, 32.5nm for VNFws, and 39.83, 38.7nm for VNSps respectively. The linearity of glucose was ranged from 0.0289 to 0.925mM for HRP, VNShs VNFws, and 0.925 to 0.0528mM for VNSps. The H 2 O 2 was in good linear range between 0.003 to 1.9383mM in both rate and fixed time method for all nanozymes and HRP. For recovery study 10µL serum sample was directly used without dilution. The K m values were found to be 1.6239 mM for HRP, 0.7843 mM for VNShs, 0.6514 mM for VNFws, ands 0.6398 mM for VNSps concluding that NZs have better affinity towards substrate molecule. The detection limit and quantification limits were found to be 0.0548mM and 0.1662mM for HRP, 0.066mM and 0.2002mM for VNShs, 0.0425mM and 0.1287mM for VNFws and 0.1474mM and 0.4465mM for VNSps. vanadium oxide nanoparticle nanozyme peroxidase mimics Horseradish peroxidase glucose glucose oxidase Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Reactive oxygen species (ROS), are a collective form of various reactive molecules and free radicals (species with unpaired electrons) which are intermediates of molecular oxygen such as hydrogen peroxide (H 2 O 2 ), hydroxyl radicals (.OH), and super oxides ( − O 2 )[ 1 , 2 ]. ROS are produced by physiological processes in biological systems during which molecular oxygen is converted into oxygen radicals which can cause hepatitis [ 3 ], glomerulonephritis [ 4 ], and inflammatory bowel disease[ 5 ]. Several literatures have exposed those diseases like cancer, and neurodegenerative disorders due to the unevenness between the production and removal of hydrogen peroxide (H 2 O 2 ) and other reactive species[ 6 , 7 ]. H 2 O 2 is an ROS, that is produced from the combination of hydroperoxyl radicals (HO 2 · ) and their hydrated form in the atmosphere[ 8 ]. In humans and animals, it is produced in small amounts as a byproduct of normal cellular metabolism through certain enzyme activities like oxidase which can transfer electrons to oxygen molecules[ 9 ]. H 2 O 2 is present in the human body in the kidney, urinary tract, and bladder[ 10 ]. The concentration of H 2 O 2 is a significant parameter giving an edge between harmless and harmful impacts on the body. In small amounts, it plays a prominent role in the immune system of animals. However, the high level of concentration of H 2 O 2 is cytotoxic and can also cause damage to cells[ 11 ], biomolecules like DNA [ 12 ], tissues[ 13 ], proteins & lipids which can lead to inflammation and cell death[ 14 ]. Because of that, it is important to quantify the H 2 O 2 in real samples. Natural enzymes are used for the quantification of these ROS. Enzymes act as biocatalysts that catalyse biochemical reactions and enable the cells to function properly[ 15 ]. Enzymes like peroxidase[ 16 ] and catalase[ 17 ] catalyse the reduction of hydrogen peroxide to water and organic hydroperoxides to corresponding alcohols[ 18 ]. Horseradish peroxidase (HRP) is a porphyrin enzyme that contains iron as a central metal ion that catalyses the oxidation of a variety of electron donors by H 2 O 2 . Extraction of enzymes is a tedious process making them costly and these enzymes are sensitive towards limited reaction conditions like pH and temperature makes them bit selective[ 19 ]. In this regard enzyme mimics, which are synthetic compounds that can mimic the enzyme's catalytic properties and offer more cost-effective, greater stability, low cost, and the ability to function in wide range of conditions for catalysing specific chemical reactions. The Nanoscience and nanotechnology provide us with a new range of features that are often not formed in bulk materials, since metal oxides are the most fascinating functional materials, they have drawn a lot of attention from many researchers in synthesizing and characterizing the metal oxide nanostructures with different morphologies to expose them to a wide variety of applications in medicine, biosensors, nanozymes (NZs), and environmental science, among many others. Among different types of enzyme mimics, NZs (NPs with enzyme activity) are recently emerged enzyme mimics. Recent exploration of enzymatic behaviour of NPs has provided a greater insight towards the mimic enzyme behaviour. Ferromagnetic iron oxide nanoparticles (NPs) are the first reported NZs for peroxidase mimics[ 20 ], after that many NPs are reported as NZs for different enzyme mimics, Layered vanadium(IV) disulfide nanosheets[ 21 ], Fe-MIM/ZIF-8 [ 22 ], Pt/cube-CeO2 nanocomposite [ 23 ], Bi 2 Fe 4 O 9 NPs [ 24 ], FA@Ag NPs [ 25 ], Cu2+-modified hollow carbon nanosphere[ 26 ], Ru–N–C NZs [ 27 ] for peroxidase activity, Fe 3+ /AMP NPs [ 28 ], Cu-MOFs [ 29 ], Nanostructured binuclear Fe(III) and Mn(III) porphyrin materials [ 30 ], Co 3 O 4 nanocrystals [ 31 ], Fe–N 4 NZs [ 32 ] for catalase activity, polydopamine-decorated CuO [ 33 ], Au NRs-Pd@HA [ 34 ], Au-integrated Fe single-atom nanozyme [ 35 ], MMSN/Au NPs [ 36 ], GOD-GO/MnO 2 [ 37 ] for GOD activity, are recently reported NZs. In most of the NPs reported for the peroxidase mimics, the reaction is optimum at certain reaction conditions like pH, temperature, or the reagents are being costly. In contrast of the above demerits, in this article we report the peroxidase mimicking behaviour of vanadium oxide NPs which has been synthesised with three different shapes, its kinetic parameters have been evaluated and compared each other and also with HRP to analyse its efficiency, and the same is applied for the glucose quantification using glucose oxidase (GOD) in human serum sample without dilution. Novel chromogenic co-substrates, P-aminophenol sulphate (PAP) & N-(1-Naphthyl) ethylenediamine dihydrochloride (NEDA) are used for the quantification of glucose and H 2 O 2 using simple UV-Vis spectrophotometric method. Material and methods Reagents and their preparation All chemicals used in the assay were of analytical grade. Reagents were freshly prepared using double distilled water. PAP was purchased from Himedia. Ltd, India, and the stock solution (72.39mM) was prepared by dissolving 150mg PAP in 10m of distilled water. NEDA was purchased from Sisco Research Laboratories Pvt. Ltd. (SRL) – India, and the stock solution (7.717mM) was prepared by dissolving 20 mg NEDA in 10mL of distilled water. Horseradish peroxidase (139 U mg –1 ) was purchased from SRL chemicals (Mumbai, India), and the stock solution was prepared by dissolving 1mg of peroxidase in 100mL of KH 2 PO 4 /NaOH buffer of pH-6 and the solution was stored in the fridge at -4 o C until use. GOD with 175 U/mg activity was purchased from Sigma–Aldrich and the stock solution was prepared by dissolving 5mg in 5mL of distilled water. Glucose was purchased from SRL chemicals and the stock solution (55.5mM) was prepared by dissolving 99.9mg glucose in 10 mL of the solution. H 2 O 2 was purchased from Molychem (Mumbai, India), the stock solution (116.3mM) was prepared by diluting 1mL of H 2 O 2 to 100mL with double distilled water and the concentration was verified by titrating it with standardized potassium permanganate solution. The stock solutions were diluted to required concentrations with double distilled water. Instrumentation LABMAN (LMSP-UV1200) spectrophotometer, 3cm 3 quartz cells was used to record all absorbance measurements. The prepared materials were subjected to characterization using scanning electron microscope (SEM, Hitachi S 3400 N) for Particle size and morphology, elemental composition and purity, Dynamic Light Scattering (DLS, MicrotracNanotrawave) for Size, size distribution, and stability of NPs, FT-IR spectroscopy (PerkinElmer Spectrum), X-ray diffraction spectrophotometry (XRD, RegakuSmartLab) for the crystallinity and crystal size of NPs. From the XRD spectra, the crystalline diameter (D) was obtained from the XRD spectra using Scherrer’s equation, i.e., d= \(\:\frac{K\lambda\:}{{\beta\:}cos\theta\:}\) and Williamson-Hall plot. Collection and processing of human serum sample for recovery study A pre-quantified human blood sample was collected from a clinical diagnostic centre from a healthy volunteer who was in a good condition and not suffered from any disease with their approval. After that the acquired blood sample was centrifuged in a Remi R-24 Centrifuge above 10000 rpm speed at room temperature. The supernatant solution of serum was collected in a heparin container and kept at -4 0 C until use. The remaining was discorded after adding Trichloroacetic acid as per the clinical laboratory guidelines. Synthesis of different morphological Vanadium oxide nanostructures. The synthesis of vanadium oxide NPs followed the method [ 38 ] with slight modifications. Vanadium Nanosheets (VNShs); Simple wet synthesis approach was incorporated to synthesis vanadium oxide VNShs. 300mg of vanadium pentoxide (V 2 O 5 ) was sonicated in 30mL of double distilled water and later stirred for 15 minutes at 1200rpm. 15mL of 30% H 2 O 2 was added dropwise at the rate of 1.5mL/min. The change in color of the reaction mixture from yellow to orange and finally to dark brown confirms the formation of hydrogen diperoxodioxovanadate(III)[ 39 ]. The stirring was continued for three hours after the formation of dark brown colour. The reaction mixture was diluted by adding 10 mL of double distilled water. The temperature was raised to 60 0 C to accelerate the gelation process of hydrated V 2 O 5 to get viscous brownish gel. The obtained gel was dried at 100 0 C for 12 hours and calcinated at 400 0 C for 6hrs to obtain the vanadium oxide VNShs. Vanadium Nanoflowers (VNFws); Hydrothermal method was adopted to synthesis the vanadium oxide VNFws. 180mg of V 2 O 5 precursor in 50mL of double distilled water was initially sonicated for 10 min. To the suspended mixture 8mL of 30% H 2 O 2 was added drop wise at the rate of 0.5mL/min with continuous stirring. Formation of 4[ 39 ]was confirmed by the change in color of the reaction mixture to dark brown. To the above reaction mixture 170mg of sodium dihydrogen orthophosphate was added and continued stirring for 30 min, the mixture was then transferred to the Teflon lined autoclave and placed in oven at 180 0 C for six hours and finally allowed to cool to room temperature. The formed precipitate was separated by centrifuging at 5000 rpm and washing with double-distilled water later was dried at 70 0 C and calcinated at 400 0 C for four hours. Vanadium Nanospheres (VNSps): Precipitation process was used to synthesis the vanadium oxide nanospheres. 350mg of ammonium metavanadate was dissolved in 100mL of double distilled water and stirred for 15min. The reaction mixture turns yellow transparent solution by the addition of 1.5mL of 1M HCl dropwise confirming the formation of Vanadium oxytrichloride (VOCl 3 ). The VOCl 3 is reduced to vanadyl trichloride by adding 4.5mL hydrazine hydrate added dropwise at the rate of 1mL / 5min. The grey-colored precipitate formed confirms the formation of Vanadyl trichloride which is separated by centrifugation and repeatedly cleaning with double distilled water. The obtained precipitate was dried for 12 hours at 70 0 C and roasted at 400 0 C for 5 hours. During roasting process, the vanadyl trichloride undergoes air oxidation to V 2 O 5 With the liberation of Cl 2 gas. Optimization of reaction parameters Absorption spectra ; The maximum absorbance of the resultant product obtained by HRP and vanadium oxide NZs of the reaction mixture was identified by using the proposed assay method with a scanning rate of 2 nm/S in the wavelength range of 400-700nm and was found to be 470nm for HRP and all NZs as shown in the Fig. 1 . This confirms the produced product is the same in all cases. Buffer & pH ; Different buffers of various concentrations from 0.01mM to 50 mM included A) CH 3 COOH/CH 3 COONa buffer, B) citric acid/potassium citrate buffer, C) KH 2 PO 4 /K 2 HPO 4 buffer, D) Tris buffer E) KH 2 PO 4 /NaOH buffer were used with reaction mixture and incubated for 10minutes. The absorbance of KH 2 PO 4 /NaOH and tris buffer (buffers with higher pH range) with VNShs and VNFws is less due to development of blank reagent colour, with HRP the absorbance is very less because of lower activity of HRP at higher pH. V 2 O 5 NZs shows good activity with wide range of buffers but maximum was with CH 3 COOH/CH 3 COONa buffer. Among different buffers, 3.33mM CH 3 COOH/CH 3 COONa buffer showed better absorbance for all NZs and HRP, as shown in inset of Figure (2). Different pH solutions of CH 3 COOH/CH 3 COONa buffer were prepared and mixed with reaction mixture. The result shows a good absorbance at pH 3.8 for all NZs and HRP, VNFws shows good absorbance from pH 3.8 to 4.5 with less deviation, as shown in Fig. 2 . Hence, 3.33mM CH 3 COOH/CH 3 COONa buffer of pH 3.8 in the final 3mL of the reaction mixture was chosen as optimum buffer for future work with all NZs and HRP. Temperature ; The effect of temperature was studied for HRP and all NZs from 10 to 50 o C. The result shows a maximum absorbance at 20 o C for HRP, whereas NZs show a considerable absorbance from 10 to 25 o C as shown in Fig. 3 . Hence the reaction with HRP is optimum at 20 o C whereas the reaction with NZs can be performed with variable temperature range from 10 to 25 o C with less deviation. Result and discussion Linearity of glucose A calibration graph for glucose was constructed for HRP and all V 2 O 5 NZs, using fixed-time assay method. Absorbance of the reaction mixture contains optimum concentration of all the reagents [as mentioned in table (1)] and 17.5 units of GOD with varying concentrations of glucose (0.0144 to 3.7mM) was recorded at 470nm with respect to blank reagent mixture after the incubation period of 10 minutes. The result shows good linearity between 0.0289 to 0.925mM for HRP, VNShs VNFws, and 0.0528 to 0.925mM for VNSps. The linearity of glucose with respect to HRP and all NZs is showing in the Fig. 4 . Linearity of HO A calibration graph for H 2 O 2 was constructed for HRP and three V 2 O 5 NZs using both rate and fixed-time assay methods. In the rate method, the absorbance was recorded with respect to the control blank for 5 minutes with a time interval of 1 minute at 470nm. In fixed time assay method, the reaction mixture was incubated for 10 minutes and the absorbance was recorded with respect to the control blank at 470nm. The linearity for the assay of H 2 O 2 was investigated in 3 mL of the reaction mixture and the concentration of reagents used are tabulated in Table (1) for HRP and all NZs. With respect to rate method, HRP and VNFws showed a limited linearity range between 0.0242-0.3877mM. VNSps and VNShs showed a very good response towards H 2 O 2 with a large linearity range between 0.0606-0.9692mM and 0.0969-1.5507mM respectively. This avoids the dilution of real samples for its application studies. Due to the different linearity range of H 2 O 2 for enzyme and NZs, depending on the sample, selectivity of NZs can be done for the quantification of H 2 O 2 . In fixed time method, HRP showed linearity between 0.0030-0.3877mM, linearity in very lesser concentration of H 2 O 2 , whereas in NZs, VNSps showed a linearity in lower concentration of H 2 O 2 between 0.0076-0.9692mM. VNShs and VNFws showed a linearity upto same lower concentration i.e. between 0.0121-0.7753mM and 0.0121–0.3877 mM respectively. The linearity of H 2 O 2 with respect to HRP and all NZs bye rate and fixed time method are showing in the Fig. 5 a & Fig. 5 b respectively. Reagents HRP VNShs VNFws VNSps PAP (mM) 2.413 1.206 1.206 1.609 NEDA (mM) 0.193 0.257 0.129 0.129 Buffer (mM) 0.33 0.33 0.33 0.33 HRP or NZs 0.0185 units 0.3299mM 0.1099mM 0.2199mM Linearity of H 2 O 2 (mM) Rate method 0.0242–0.3877 0.0969–1.5507 0.0242–0.3877 0.0606–0.9692 Fixed time method 0.0030–0.3877 0.0121–0.7753 0.0121–0.3877 0.0076–0.9692 Regression equation (Y=) Rate method 0.2504x + 0.0063 0.0823x − 0.0004 0.3027x − 0.0013 0.072x − 0.0011 Fixed time method 1.8423x + 0.0374 1.1963x + 0.0113 1.5118x − 0.0077 0.389x + 0.0272 Regression coefficient (R 2 ) Rate method 0.9954 0.9957 0.9978 0.9993 Fixed time method 0.9973 0.9992 0.9991 0.9991 Table (1): linearity range of H 2 O 2 and concentrations of reagents used. Effect of concentration of analytical reagents on the rate of reaction The effect of varying concentrations of analytical reagents (PAP, NEDA, and H 2 O 2 ) on the rate of the reaction was investigated under experimental settings with 3mL solution by varying the concentration of one reagent at a time. The results show an increase in the absorbance with an increase in the concentration of analytical reagents up to optimized concentration afterward, there is no significant change in the rate of the reaction or the rate of the reaction decreases slightly. As a result, the final optimal concentration was fixed at the same level for all further experiments. Optimized concentrations of all analytical reagents are tabulated below (Table 2). The graphs of rate versus concentration of PAP, NEDA, and H 2 O 2 are shown in Fig. S 1 , S 2 , S 3 . & S 4 . Therefore, the concentrations of reagents needed by NZs are far lower than those needed by HRP enzyme. The PAP concentration needed by VNShs and VNFws are same and lower than that of VNSps, which is lower than that of HRP. NEDA required by the VNFws and VNSps are same, and greater than the HRP, which is greater than VNShs. H 2 O 2 required by the HRP and VNFws is same and lesser than the VNSps and which is lesser then the VNShs. Reagents HRP VNShs VNFws VNSps PAP (mM) 2.413 1.206 1.206 1.609 NEDA (mM) 0.193 0.257 0.129 0.129 H 2 O 2 (mM) 0.3877 1.551 0.3877 0.9692 NZs 0.0185 units 0.3299mM 0.1099mM 0.2199mM Table (2): Optimized values of analytical reagents. Evaluation of analytical characteristics of the proposed assay The Michaelis–Menten constant (K m ) for PAP, NEDA & H 2 O 2 was determined by Lineweaver–Burk plot, keeping all the reagents at optimized condition & by varying one reagent concentration (PAP, NEDA & H 2 O 2 ) at a time. The K m values for PAP, and NEDA are 1.0867 and 0.3142mM for HRP, 0.3179 and 0.0327mM for VNShs, 0.2108 and 0.07499mM for VNFws, 0.4625 and 0.3085mM for VNSps and the Lineweaver–Burk plots are showed in Fig. S 5 , S 6 , S 7 , & S 8 . K m values with respect to H 2 O 2 , V max , K cat , and K eff are tabulated (Table 3). Lineweaver–Burk plots for the determination of K m values with respect to H 2 O 2 were showed in the Fig. 6 . K m values for all NZs is lesser than that of the HRP enzyme in the increasing order of VNSps, VNFws, VNShs and HRP which indicates the large affinity of NZs towards substrate (H 2 O 2 ) than HRP enzyme. V max is maximum for VNShs is greater than the HRP followed by VNFws and VNSps. K cat and K eff of HRP is much greater than all NZs which is in the increasing order of VNShs, VNSps and VNFws. Comparison of catalytic parameters are showen in Table (4) indicates that the synthesised NZs have higher affinity towards substrates than some reported NZs. Reagents K m for H 2 O 2 (mM) V max (mM/sec) K cat (sec − 1 ) K eff (mM − 1 sec − 1) HRP 0.8051 0.3042 16.4166 20.3908 VNShs 0.7843 0.3369 1.0212 1.3021 VNFws 0.6514 0.2508 2.2817 3.5034 VNSps 0.6398 0.2133 1.9408 3.0334 Table (3): Catalytic parameters. Sl. No. Nanozyme H 2 O 2 linearity, LOD K m Reference 1 Nickel metal-organic framework 2D NShs 0.04–160µM LOD-8 nM TMB-0.365mM H 2 O 2 -2.49mM [ 40 ] 2 porous PtCu dendrites 0.3–325µM LOD-0.1 µM TMB- 0.08 mM H 2 O 2 - 0.26 mM [ 41 ] 3 Pt NPs 1–50µM LOD-1µM TMB-0.091 mM H 2 O 2 -80.25 mM [ 42 ] 4 rhodium nanoparticles 1-100µM LOD- 0.20 µM TMB-0.198 mM H 2 O 2 - 0.38 mM [ 43 ] 5 VNShs 0.0969-1.5507mM LOD-0.066mM H 2 O 2 - 0.7843mM PAP- 0.3179 and NEDA-0.0327mM Present work VNFws 0.0242-0.3877mM LOD-0.0425mM H 2 O 2 - 0.6514mM PAP- 0.2108 and NEDA- 0.0749mM VNSps 0.0606-0.9692mM LOD-0.1474mM H 2 O 2 - 0.6398mM PAP- 0.4625 and NEDA- 0.3085mM Table (4): comparison of catalytic parameters. Characterization of vanadium oxide Nano crystals. SEM and EDS analysis FE-SEM images of vanadium oxide NPs synthesized by different methods were showed in Fig. 7 & S 9 , S 10 , S 11 . Based on the type of the morphology, the NPs were named as VNShs, VNFws and VNSps. During heat treatment of hydrogen diperoxodioxovanadate (III) complex to form brownish gel (V 2 O 5 .nH 2 O), sheet like NPs is formed. When brown solution of hydrogen diperoxodioxovanadate (III) complex was treated with sodium dihydrogen orthophosphate followed by hydrothermal process, flower like NPs were formed. When the yellow transparent solution of ammonium metavanadate and HCl was treated with reducing agent like hydrazine hydrate forms spheres like NPs. Elemental composition of the NPs was showing the presence of 41.97% of vanadium and 58.03% of oxygen in VNShs, 51.56% vanadium and 48.44% of oxygen in VNFws, 36.2% of vanadium and 63.8% of oxygen in VNSps, which indicates the formation of pure vanadium oxide NPs. EDS spectres with percentage compositions were showed in Fig. 8 . XRD The XRD pattern of the NPs were shown in the Fig. 9 . The XRD peaks of VNFws and VNSps, the different peaks were exactly matched with standard card JCPDS no. 41-1426, representing vanadium pentoxide NPs in orthorhombic phase. whereas VNShs were matched with [ 44 ], which correspondence to rhombohedral structure of the V 2 O 5 . The average crystalline size of the synthesised NPs was calculated using Scherrer’s and W-H equations and was found to be 45.42, 45.7nm for VNShs, 29.14, 32.5nm for VNFws, and 39.83, 38.7nm for VNSps. The degree of crystallinity index was calculated and was found to be 0.7978 for VNShs, 0.7856 For VNFws and 0.8570for VNSps. Therefore, in VNShs 79.78% is in crystalline and 20.22% is in amorphous form, in VNFws 78.57% is in crystalline and 21.43% is in amorphous form, and in VNSps, 85.70% is in crystalline and 14.30% is in amorphous form. Raw XRD patterns were showed in Fig. S 15 , S 16 & S 17 . DLS The variations in the intensity of scattered light resulting from Brownian motion in synthesized NPs, size, and size distribution can be predicted with the help of the distribution curve in DLS spectra. The resultant DLS spectrums and size distribution of different morphological vanadium oxide NPs are shown below in Table (5). It showed that the size distribution by the analysis of DLS are in good agreement with histogram plot data which is showed in Table S 1 , S 2 & S 3 . Zeta potential is a stability indicator of NPs, which is influenced by the surface charge and is measured by analysing particles' electrophilic mobility in an electric field. The Zeta potential of VNShs is 4.0mv, VNFws is -4.5mv and VNSps is 4.6mv which are almost neutral[ 45 ]. The Polydispersity index (PDI) is a parameter used to measure the size distribution, specifying the uniformity of NPs. International standards organizations have established that PDI values of 0.1 to 0.25 indicate a small size distribution (monodisperse samples) and values greater than 0.5 are common to broad distribution (polydisperse samples)[ 46 , 47 ]. PDI values of VNShs is 1.144, VNFws is 1.834 and VNSps is 1.896 which indicates polydispersity with multiple-sized particles, which might be due to the rapid agglomeration of the NPs due to low zeta potentials[ 48 ]. Size distribution spectres as obtained by DLS analysis is showed in the Fig. 10 . DLS result on Surface charge measurements are showed in Fig. S 12 , S 13 , & S 14 . NZs Volume % Diameter(nm) VNShs 100 349 VNFws 44.7 390 55.3 180.8 VNSps 100 254.1 Table (5): size distribution of different shaped vanadium oxide NZs. FT-IR Spectra analysis To study the nature of binding between vanadium and oxygen in V 2 O 5 NPs, FT-IR was recorded in the range of 400 to 4000cm − 1 wave number, which identifies the chemical bonds as well as functional groups in the compound. FTIR spectra of V 2 O 5 NPs exhibited three characteristic vibration modes. The peaks near 470 cm − 1 are due to the V-O-V bond symmetric stretch, 800cm − 1 is due to the V-O-V asymmetric stretch and the peak around 1000cm − 1 can be assigned to the V = O stretching, the peaks around 600 to 700cm − 1 is potentially originate from the V − O bond, and the peaks from 1950 to 2350cm − 1 are due to δ(HOH) and ט γ(OH) in water. FRIR spectres of V 2 O 5 NPs were showed in Fig. 11 . Influence of interfering species To study the effect of interfering species, optimized experimental conditions were used by taking several cations, anions and amino acids using hydrogen peroxide concentration of 0.0969mM for HRP, 0.1938mM for both VNShs and VNFws and 0.4845mM for VNSps. Influence of interfering species is tabulated in Table (6) as tolerant ratio. It is the ratio of limit of interfering species concentration to that of the concentration of hydrogen peroxide. The result shows high interference for Fe 2+ , Fe 3+ , and FAS (iron-containing salts) with HRP and all NZs. Zr 4+ , Na 2 CO 3 , Glycine, Glucose, and Alanine show average interference, whereas NH 4 Cl, Na + , Mg 2+ , Zn 2+ , Zr 4+ , KNO 3 , and Urea show less interference to a large extent. Foreign species Tolerant ratio HRP VNShs VNSps VNFws Cl − 1 360 715 1429 360 Na + , Mg 2+ 280 550 1100 270 Zn 2+ 55 210 395 90 Se 4+ 20 163 325 90 Ni 2+ 20 210 430 50 Zr 4+ 40 70 250 20 Co 2+ 55 15 20 5 Cu 2+ 10 10 0.05 0.05 Na 2 CO 3 60 10 250 35 KNO 3 165 330 660 165 Urea 430 210 430 105 Glycine 80 85 170 50 Glucose 40 70 140 35 EDTA 20 10 85 5 Alanine 80 70 85 20 Leucine, Isoleucine 55 50 50 10 L-Histidine 45 40 85 10 cysteine 10 25 110 10 Methionine 45 50 85 50 Cetric acid 40 15 140 5 D-Valine 55 55 110 25 glutamic acid 10 50 50 25 L-ornithine HCl 40 40 20 20 Fe 3+ 0.0282 0.0029 0.0058 0.0283 Fe 2+ 0.2861 0.2887 0.0231 0.0014 FAS 0.2861 0.0029 0.0058 0.0282 Table (6): Interference study of different metal ions, organic and organic compounds. Precision study Precision studies were performed at optimized conditions of all the reagents along with four different concentrations of H 2 O 2 within the linearity range. The study included 10 runs in a day with a time interval of 1 h for within-day precision and 10 days run for day-to-day precision with one day interval. All solutions were prepared freshly every day(n = 10). The standard deviation (SD) and percentage standard deviation (%SD) were showed in Table (7). The results show Intraday %SD is greater than the Inter-day %SD in all cases and VNSps has highest %SD when compared to other NZs and HRP. The Limit of detection (LOD), and Limit of quantification (LOQ) were determined by taking the readings of blank regent versus blank reagent. LOD and LOQs were found to be 0.0548 mM and 0.1662 mM for HRP, 0.066 mM and 0.2002 mM for VNShs, 0.0425 mM and 0.1287 mM for VNFws and 0.1474 mM and 0.4465 mM for VNSps. HRP VNShs [H 2 O 2 ] in mM SD %SD [H 2 O 2 ] in mM SD %SD Intraday Inter-day Intraday Inter-day Intraday Inter-day Intraday Inter-day 0.3877 0.022 0.0252 1.7933 2.0629 0.1938 0.0066 0.0106 0.8221 1.3616 0.1938 0.0043 0.0078 0.7192 1.3288 0.0485 0.0072 0.0081 1.0815 1.1883 0.0969 0.0037 0.0038 1.584 1.6163 0.0242 0.004 0.0055 0.8156 1.0317 0.0484 0.0016 0.0021 1.4266 1.8851 0.0121 0.0048 0.0060 1.1507 1.4792 VNFws VNSps [H 2 O 2 ] in mM SD %SD [H 2 O 2 ] in mM SD %SD Intraday Inter-day Intraday Inter-day Intraday Inter-day Intraday Inter-day 0.3877 0.005 0.0054 0.865 0.9475 0.2423 0.0169 0.0253 1.7465 2.652 0.1938 0.0043 0.0052 1.301 1.5629 0.1211 0.0187 0.0207 2.088 2.3316 0.0969 0.0014 0.0016 1.048 1.1694 0.0606 0.0182 0.0195 2.264 2.4247 0.0485 0.0005 0.0012 0.908 1.8965 0.0151 0.0131 0.0134 2.614 2.6942 Table (7): Inter & Intraday precision study of the proposed method. (n = 10) Recovery and applicability With glucose Recovery studies were conducted by inoculating the standard glucose to serum sample in the proposed method. The serum sample has been analysed by spiking 10µL directly to the reaction mixture. The result shows recovery rate ranging between 71.51 and 96.8% for HRP, 74.58 and 97.65% for VNShs, 75.29 and 97.28% for VNFws, 76.89 and 97.79% for VNSps. The recovery percentage is high with higher concentration of glucose, decreases with decrease in concentration and the percentage recovery is almost similar for HRP and NZs. The results are showen in the Table (8). Blood sample in µL HRP VNShs Glucose (mM) Added (mM) Found (mM) Recovered*(%) Glucose (mM) Added (mM) Found (mM) Recovered*(%) 10 0.4625 0.4742 0.4591 96.8 0.4625 0.462 0.4512 97.65 0.2312 0.2079 0.1927 92.71 0.2312 0.236 0.2252 95.39 0.0578 0.0532 0.038 71.51 0.0578 0.0427 0.0319 74.58 VNFws VNSps Glucose (mM) Added (mM) Found (mM) Recovered*(%) Glucose (mM) Added (mM) Found (mM) Recovered*(%) 0.4625 0.47 0.4572 97.28 0.4625 0.4353 0.4257 97.79 0.2312 0.2188 0.2059 94.16 0.2312 0.3242 0.3146 97.03 0.0578 0.0516 0.0389 75.29 0.1156 0.0417 0.03205 76.89 Table (8): recovery study of glucose in blood sample. With H 2 O 2 Recovery studies were conducted by inoculating the standard H 2 O 2 to serum sample in the proposed method. The experiment demonstrated repeatability and minimal interference from inhibitory species, resulting in a recovery rate ranging from 99.72–99.93% for HRP, 94.99–98.92% for VNShs, 95.70–99.16% for VNFws and 98.13–99.55% for VNSps as shown in the Table (9). The results show the recovery percentage is maximum when the added H 2 O 2 concentration was maximum, minimum when added H 2 O 2 concentration was minimum and highest recovery percentage with HRP and least with VNShs. Blood sample in µL HRP VNShs H 2 O 2 (mM) Added (mM) Found (mM) Recovered*(%) H 2 O 2 (mM) Added (mM) Found (mM) Recovered*(%) 10 0.1938 0.1952 0.1951 99.93 0.7753 0.7702 0.7618 98.92 0.0969 0.0929 0.0928 99.85 0.3877 0.4118 0.4034 97.98 0.0484 0.0509 0.0508 99.72 0.1938 0.1666 0.1583 94.99 VNFws VNSps H 2 O 2 (mM) Added (mM) Found (mM) Recovered*(%) H 2 O 2 (mM) Added (mM) Found (mM) Recovered*(%) 0.1938 0.1898 0.18824 99.16 0.4845 0.4841 0.4819 99.55 0.0969 0.1095 0.1079 98.54 0.2422 0.2495 0.2438 99.11 0.0484 0.0371 0.0355 95.70 0.121 0.1167 0.1145 98.13 Table (9): recovery study of H 2 O 2 in blood sample. Probable reaction mechanism V 2 O 5 [V(V)] NZs oxidises P-amino phenol to 4-iminocyclohexa-2, 5-dien-1-one (oxidized P-amino phenol) and converted into V(IV). V(IV) donates electron to H 2 O 2 and convert them to H 2 O. Oxidized P-amino phenol coupled with NEDA to form 4-((4-((2-aminoethyl) amino) naphthalen-1-yl)imino)cyclohexa-2,5-dien-1-one which is an orange-red coloured product with maximum absorbance at 470nm. Conclusion The proposed method which involves coupling of oxidized PAP and NEDA using H 2 O 2 , is very easy to use, quick and sensitive to H 2 O 2 assay. The reagents utilized are affordable, widely accessible, and soluble in water. Since the coupled product's maximum absorbance occurs at 470nm, this method can also be used for the quantification of H 2 O 2 in biological samples. The linearity of H 2 O 2 in the range of 0.0242 to 0.3877mM for HRP, 0.0969 to 1.5507mM for VNShs, 0.0242 to 0.3877mM for VNFws and 0.0606 to 0.9692mM for VNSps which indicates the sensitivity of the proposed method. Declarations Ethical Approval The institutional Human Ethical Committee (IHEC-UOMNo.22/Ph.D/2008-09) of the university of mysore has granted us permission to use of human blood serum sample Funding Not applicable Author Contribution NYG prepared the manuscript, RHS helped to prepare the manuscript and all authors are reviewed the manuscript Acknowledgement One of the authors, Nikhil Y Gangadhara would like to thank ATME college of engineering, mysore, Karnataka, for providing the research laboratory facilities. Availability of data and materials The authors affirm that the data supporting the discoveries of the study are accessible within the paper and its supplementary information file. References Tripathy S, Mohanty PK (2017) Reactive oxygen species (ROS) are boon or bane. Int J Pharm Sci Res 8(1):1 Wang M-Q et al (2017) Controlled synthesis of Mn 3 (PO 4) 2 hollow spheres as biomimetic enzymes for selective detection of superoxide anions released by living cells. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5318695","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":370782054,"identity":"601eab5a-47c6-4046-9699-8f19e3cd4e28","order_by":0,"name":"Nikhil Y Gangadhara","email":"","orcid":"","institution":"Department of Chemistry, Maharaja Institute of Technology Mysore, Mandya-571438","correspondingAuthor":false,"prefix":"","firstName":"Nikhil","middleName":"Y","lastName":"Gangadhara","suffix":""},{"id":370782055,"identity":"acac49fe-7e38-416d-822e-df65245ab9b8","order_by":1,"name":"Manju. 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Inset image shows optimization of buffer. A) CH\u003csub\u003e3\u003c/sub\u003eCOOH/CH\u003csub\u003e3\u003c/sub\u003eCOONa buffer, B) citric acid/potassium citrate buffer, C) KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e/K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4 \u003c/sub\u003ebuffer, D) Tris buffer E) KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e/NaOH buffer.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5318695/v1/0f47a9200b56e8c1f89ed6ce.jpg"},{"id":67651368,"identity":"6f222785-b3f9-4f20-8b25-098af117dce8","added_by":"auto","created_at":"2024-10-28 11:44:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46759,"visible":true,"origin":"","legend":"\u003cp\u003eTemperature optimization.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5318695/v1/c8fea0de87a86185d1557f83.jpg"},{"id":67653705,"identity":"4a63b49c-4026-486e-92bf-3bb217f52f49","added_by":"auto","created_at":"2024-10-28 12:00:43","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47428,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration graph for Glucose.\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5318695/v1/ebdba3cfd31c6f6801edba89.jpg"},{"id":67654707,"identity":"c96d8b0c-d7b6-4b7b-a1c4-1282e498eb41","added_by":"auto","created_at":"2024-10-28 12:08:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":92806,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration graph for H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e.\u003csub\u003e \u003c/sub\u003eA) by rate method B) by fixed time method.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5318695/v1/49995f19e344e8b7990ba272.png"},{"id":67651370,"identity":"9c3ee38d-b350-4a0c-8ad8-1503456c9a71","added_by":"auto","created_at":"2024-10-28 11:44:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":64348,"visible":true,"origin":"","legend":"\u003cp\u003eLineweaver–Burk plots for H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e. A) For HRP B) For VNShs C) For VNFws D) For VNSps.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5318695/v1/c1132f96406cf3ac4d790c2e.png"},{"id":67651373,"identity":"317aa58d-9fb6-44de-857b-b34d9d63e9af","added_by":"auto","created_at":"2024-10-28 11:44:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":529914,"visible":true,"origin":"","legend":"\u003cp\u003eFE-SEM images of A) VNShs B) VNFws C) VNSps\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5318695/v1/ca59bb5f8d195663cf494ca1.png"},{"id":67651374,"identity":"4399cf45-5ff7-424f-aa0c-7ab4e58d6df8","added_by":"auto","created_at":"2024-10-28 11:44:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":192535,"visible":true,"origin":"","legend":"\u003cp\u003eEDS images of A) VNShs B) VNFws C) VNSps. Inset images shows the elemental composition\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5318695/v1/6e0f154e3fe10f1cc6fcf0ec.png"},{"id":67653709,"identity":"771e6a8b-e5df-4511-99cb-b05a6aab4aaa","added_by":"auto","created_at":"2024-10-28 12:00:43","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":63759,"visible":true,"origin":"","legend":"\u003cp\u003eThe spectra of XRD pattern of V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e NPs. inset figure shows V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e NPs (JCPDS no. 41-1426).\u0026nbsp; A) VNFws B) VNSps C) VNShs. Inset figure shows XRD of V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e NPs (JCPDS no. 41-1426).\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5318695/v1/51b4bab20ce03e9182d89002.jpg"},{"id":67652271,"identity":"c832f7da-3dc2-432a-95bc-728b40df218c","added_by":"auto","created_at":"2024-10-28 11:52:43","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":885121,"visible":true,"origin":"","legend":"\u003cp\u003eSize distribution spectres as obtained by DLS analysis. Inset images shows the size distribution histogram. A) VNShs B) VNFws C) VNSps.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5318695/v1/7213021728310cbfaea354ae.png"},{"id":67653706,"identity":"8e879274-f47d-47e1-8f61-7e7671e2a5ae","added_by":"auto","created_at":"2024-10-28 12:00:43","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":54147,"visible":true,"origin":"","legend":"\u003cp\u003eFRIR spectres.\u003c/p\u003e","description":"","filename":"Figure11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5318695/v1/ead654aec01e144e6fc07aee.jpg"},{"id":68561217,"identity":"c3163de7-f62e-44c6-b83e-074ab881b316","added_by":"auto","created_at":"2024-11-08 14:23:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3089020,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5318695/v1/e78d9d7b-ba5d-4a0d-93df-f022ef0d1bcb.pdf"},{"id":67651364,"identity":"42770393-854f-4902-963f-5f170aaa1983","added_by":"auto","created_at":"2024-10-28 11:44:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1334535,"visible":true,"origin":"","legend":"","description":"","filename":"4supplimentry1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5318695/v1/be30243fe61ce7448a3f9121.pdf"},{"id":67654708,"identity":"64aecf38-6fa9-4d5c-9911-61995a67c3c4","added_by":"auto","created_at":"2024-10-28 12:08:52","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":37206,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"Graphicalabstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5318695/v1/e1fb7484fe86c96466e2b98a.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePeroxidase Mimicking V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e Nanozymes as the Spectrophotometric Sensor for the Determination of Glucose in Human Serum Sample Employing New Chromogenic Co-Substrates\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eReactive oxygen species (ROS), are a collective form of various reactive molecules and free radicals (species with unpaired electrons) which are intermediates of molecular oxygen such as hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e), hydroxyl radicals (.OH), and super oxides (\u003csup\u003e\u0026minus;\u003c/sup\u003eO\u003csub\u003e2\u003c/sub\u003e)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. ROS are produced by physiological processes in biological systems during which molecular oxygen is converted into oxygen radicals which can cause hepatitis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], glomerulonephritis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and inflammatory bowel disease[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Several literatures have exposed those diseases like cancer, and neurodegenerative disorders due to the unevenness between the production and removal of hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) and other reactive species[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e is an ROS, that is produced from the combination of hydroperoxyl radicals (HO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026middot;\u003c/sup\u003e) and their hydrated form in the atmosphere[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In humans and animals, it is produced in small amounts as a byproduct of normal cellular metabolism through certain enzyme activities like oxidase which can transfer electrons to oxygen molecules[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e is present in the human body in the kidney, urinary tract, and bladder[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The concentration of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e is a significant parameter giving an edge between harmless and harmful impacts on the body. In small amounts, it plays a prominent role in the immune system of animals. However, the high level of concentration of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e is cytotoxic and can also cause damage to cells[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], biomolecules like DNA [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], tissues[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], proteins \u0026amp; lipids which can lead to inflammation and cell death[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Because of that, it is important to quantify the H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e in real samples. Natural enzymes are used for the quantification of these ROS. Enzymes act as biocatalysts that catalyse biochemical reactions and enable the cells to function properly[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Enzymes like peroxidase[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and catalase[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] catalyse the reduction of hydrogen peroxide to water and organic hydroperoxides to corresponding alcohols[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Horseradish peroxidase (HRP) is a porphyrin enzyme that contains iron as a central metal ion that catalyses the oxidation of a variety of electron donors by H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eExtraction of enzymes is a tedious process making them costly and these enzymes are sensitive towards limited reaction conditions like pH and temperature makes them bit selective[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In this regard enzyme mimics, which are synthetic compounds that can mimic the enzyme's catalytic properties and offer more cost-effective, greater stability, low cost, and the ability to function in wide range of conditions for catalysing specific chemical reactions. The Nanoscience and nanotechnology provide us with a new range of features that are often not formed in bulk materials, since metal oxides are the most fascinating functional materials, they have drawn a lot of attention from many researchers in synthesizing and characterizing the metal oxide nanostructures with different morphologies to expose them to a wide variety of applications in medicine, biosensors, nanozymes (NZs), and environmental science, among many others. Among different types of enzyme mimics, NZs (NPs with enzyme activity) are recently emerged enzyme mimics. Recent exploration of enzymatic behaviour of NPs has provided a greater insight towards the mimic enzyme behaviour. Ferromagnetic iron oxide nanoparticles (NPs) are the first reported NZs for peroxidase mimics[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], after that many NPs are reported as NZs for different enzyme mimics, Layered vanadium(IV) disulfide nanosheets[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], Fe-MIM/ZIF-8 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], Pt/cube-CeO2 nanocomposite [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], Bi\u003csub\u003e2\u003c/sub\u003eFe\u003csub\u003e4\u003c/sub\u003eO\u003csub\u003e9\u003c/sub\u003e NPs [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], FA@Ag NPs [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Cu2+-modified hollow carbon nanosphere[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], Ru\u0026ndash;N\u0026ndash;C NZs [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] for peroxidase activity, Fe\u003csup\u003e3+\u003c/sup\u003e/AMP NPs [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], Cu-MOFs [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], Nanostructured binuclear Fe(III) and Mn(III) porphyrin materials [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], Co\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e nanocrystals [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], Fe\u0026ndash;N\u003csub\u003e4\u003c/sub\u003e NZs [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] for catalase activity, polydopamine-decorated CuO [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], Au NRs-Pd@HA [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], Au-integrated Fe single-atom nanozyme [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], MMSN/Au NPs [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], GOD-GO/MnO\u003csub\u003e2\u003c/sub\u003e [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] for GOD activity, are recently reported NZs. In most of the NPs reported for the peroxidase mimics, the reaction is optimum at certain reaction conditions like pH, temperature, or the reagents are being costly.\u003c/p\u003e \u003cp\u003eIn contrast of the above demerits, in this article we report the peroxidase mimicking behaviour of vanadium oxide NPs which has been synthesised with three different shapes, its kinetic parameters have been evaluated and compared each other and also with HRP to analyse its efficiency, and the same is applied for the glucose quantification using glucose oxidase (GOD) in human serum sample without dilution. Novel chromogenic co-substrates, P-aminophenol sulphate (PAP) \u0026amp; N-(1-Naphthyl) ethylenediamine dihydrochloride (NEDA) are used for the quantification of glucose and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e using simple UV-Vis spectrophotometric method.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eReagents and their preparation\u003c/h2\u003e \u003cp\u003eAll chemicals used in the assay were of analytical grade. Reagents were freshly prepared using double distilled water. PAP was purchased from Himedia. Ltd, India, and the stock solution (72.39mM) was prepared by dissolving 150mg PAP in 10m of distilled water. NEDA was purchased from Sisco Research Laboratories Pvt. Ltd. (SRL) \u0026ndash; India, and the stock solution (7.717mM) was prepared by dissolving 20 mg NEDA in 10mL of distilled water. Horseradish peroxidase (139 U mg\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) was purchased from SRL chemicals (Mumbai, India), and the stock solution was prepared by dissolving 1mg of peroxidase in 100mL of KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e/NaOH buffer of pH-6 and the solution was stored in the fridge at -4\u003csup\u003eo\u003c/sup\u003eC until use. GOD with 175 U/mg activity was purchased from Sigma\u0026ndash;Aldrich and the stock solution was prepared by dissolving 5mg in 5mL of distilled water. Glucose was purchased from SRL chemicals and the stock solution (55.5mM) was prepared by dissolving 99.9mg glucose in 10 mL of the solution. H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e was purchased from Molychem (Mumbai, India), the stock solution (116.3mM) was prepared by diluting 1mL of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e to 100mL with double distilled water and the concentration was verified by titrating it with standardized potassium permanganate solution. The stock solutions were diluted to required concentrations with double distilled water.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInstrumentation\u003c/h3\u003e\n\u003cp\u003eLABMAN (LMSP-UV1200) spectrophotometer, 3cm\u003csup\u003e3\u003c/sup\u003e quartz cells was used to record all absorbance measurements. The prepared materials were subjected to characterization using scanning electron microscope (SEM, Hitachi S 3400 N) for Particle size and morphology, elemental composition and purity, Dynamic Light Scattering (DLS, MicrotracNanotrawave) for Size, size distribution, and stability of NPs, FT-IR spectroscopy (PerkinElmer Spectrum), X-ray diffraction spectrophotometry (XRD, RegakuSmartLab) for the crystallinity and crystal size of NPs. From the XRD spectra, the crystalline diameter (D) was obtained from the XRD spectra using Scherrer\u0026rsquo;s equation, i.e., d=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{K\\lambda\\:}{{\\beta\\:}cos\\theta\\:}\\)\u003c/span\u003e\u003c/span\u003e and Williamson-Hall plot.\u003c/p\u003e\n\u003ch3\u003eCollection and processing of human serum sample for recovery study\u003c/h3\u003e\n\u003cp\u003eA pre-quantified human blood sample was collected from a clinical diagnostic centre from a healthy volunteer who was in a good condition and not suffered from any disease with their approval. After that the acquired blood sample was centrifuged in a Remi R-24 Centrifuge above 10000 rpm speed at room temperature. The supernatant solution of serum was collected in a heparin container and kept at -4\u003csup\u003e0\u003c/sup\u003eC until use. The remaining was discorded after adding Trichloroacetic acid as per the clinical laboratory guidelines.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSynthesis of different morphological Vanadium oxide nanostructures.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe synthesis of vanadium oxide NPs followed the method [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] with slight modifications.\u003c/p\u003e \u003cp\u003e \u003col style=\"list-style-type:lower-alpha;\"\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eVanadium Nanosheets (VNShs); Simple wet synthesis approach was incorporated to synthesis vanadium oxide VNShs. 300mg of vanadium pentoxide (V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e) was sonicated in 30mL of double distilled water and later stirred for 15 minutes at 1200rpm. 15mL of 30% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e was added dropwise at the rate of 1.5mL/min. The change in color of the reaction mixture from yellow to orange and finally to dark brown confirms the formation of hydrogen diperoxodioxovanadate(III)[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The stirring was continued for three hours after the formation of dark brown colour. The reaction mixture was diluted by adding 10 mL of double distilled water. The temperature was raised to 60\u003csup\u003e0\u003c/sup\u003eC to accelerate the gelation process of hydrated V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e to get viscous brownish gel. The obtained gel was dried at 100\u003csup\u003e0\u003c/sup\u003eC for 12 hours and calcinated at 400\u003csup\u003e0\u003c/sup\u003eC for 6hrs to obtain the vanadium oxide VNShs.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eVanadium Nanoflowers (VNFws); Hydrothermal method was adopted to synthesis the vanadium oxide VNFws. 180mg of V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e precursor in 50mL of double distilled water was initially sonicated for 10 min. To the suspended mixture 8mL of 30% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e was added drop wise at the rate of 0.5mL/min with continuous stirring. Formation of 4[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]was confirmed by the change in color of the reaction mixture to dark brown. To the above reaction mixture 170mg of sodium dihydrogen orthophosphate was added and continued stirring for 30 min, the mixture was then transferred to the Teflon lined autoclave and placed in oven at 180\u003csup\u003e0\u003c/sup\u003eC for six hours and finally allowed to cool to room temperature. The formed precipitate was separated by centrifuging at 5000 rpm and washing with double-distilled water later was dried at 70\u003csup\u003e0\u003c/sup\u003eC and calcinated at 400\u003csup\u003e0\u003c/sup\u003eC for four hours.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eVanadium Nanospheres (VNSps): Precipitation process was used to synthesis the vanadium oxide nanospheres. 350mg of ammonium metavanadate was dissolved in 100mL of double distilled water and stirred for 15min. The reaction mixture turns yellow transparent solution by the addition of 1.5mL of 1M HCl dropwise confirming the formation of Vanadium oxytrichloride (VOCl\u003csub\u003e3\u003c/sub\u003e). The VOCl\u003csub\u003e3\u003c/sub\u003e is reduced to vanadyl trichloride by adding 4.5mL hydrazine hydrate added dropwise at the rate of 1mL / 5min. The grey-colored precipitate formed confirms the formation of Vanadyl trichloride which is separated by centrifugation and repeatedly cleaning with double distilled water. The obtained precipitate was dried for 12 hours at 70\u003csup\u003e0\u003c/sup\u003eC and roasted at 400\u003csup\u003e0\u003c/sup\u003eC for 5 hours. During roasting process, the vanadyl trichloride undergoes air oxidation to V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e With the liberation of Cl\u003csub\u003e2\u003c/sub\u003e gas.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e\n\u003ch3\u003eOptimization of reaction parameters\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eAbsorption spectra\u003c/b\u003e; The maximum absorbance of the resultant product obtained by HRP and vanadium oxide NZs of the reaction mixture was identified by using the proposed assay method with a scanning rate of 2 nm/S in the wavelength range of 400-700nm and was found to be 470nm for HRP and all NZs as shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This confirms the produced product is the same in all cases.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBuffer \u0026amp; pH\u003c/b\u003e; Different buffers of various concentrations from 0.01mM to 50 mM included A) CH\u003csub\u003e3\u003c/sub\u003eCOOH/CH\u003csub\u003e3\u003c/sub\u003eCOONa buffer, B) citric acid/potassium citrate buffer, C) KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e/K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e buffer, D) Tris buffer E) KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e/NaOH buffer were used with reaction mixture and incubated for 10minutes. The absorbance of KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e/NaOH and tris buffer (buffers with higher pH range) with VNShs and VNFws is less due to development of blank reagent colour, with HRP the absorbance is very less because of lower activity of HRP at higher pH. V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e NZs shows good activity with wide range of buffers but maximum was with CH\u003csub\u003e3\u003c/sub\u003eCOOH/CH\u003csub\u003e3\u003c/sub\u003eCOONa buffer. Among different buffers, 3.33mM CH\u003csub\u003e3\u003c/sub\u003eCOOH/CH\u003csub\u003e3\u003c/sub\u003eCOONa buffer showed better absorbance for all NZs and HRP, as shown in inset of Figure (2). Different pH solutions of CH\u003csub\u003e3\u003c/sub\u003eCOOH/CH\u003csub\u003e3\u003c/sub\u003eCOONa buffer were prepared and mixed with reaction mixture. The result shows a good absorbance at pH 3.8 for all NZs and HRP, VNFws shows good absorbance from pH 3.8 to 4.5 with less deviation, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Hence, 3.33mM CH\u003csub\u003e3\u003c/sub\u003eCOOH/CH\u003csub\u003e3\u003c/sub\u003eCOONa buffer of pH 3.8 in the final 3mL of the reaction mixture was chosen as optimum buffer for future work with all NZs and HRP.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTemperature\u003c/b\u003e; The effect of temperature was studied for HRP and all NZs from 10 to 50\u003csup\u003eo\u003c/sup\u003eC. The result shows a maximum absorbance at 20\u003csup\u003eo\u003c/sup\u003eC for HRP, whereas NZs show a considerable absorbance from 10 to 25\u003csup\u003eo\u003c/sup\u003eC as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Hence the reaction with HRP is optimum at 20\u003csup\u003eo\u003c/sup\u003eC whereas the reaction with NZs can be performed with variable temperature range from 10 to 25\u003csup\u003eo\u003c/sup\u003eC with less deviation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Result and discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLinearity of glucose\u003c/h2\u003e \u003cp\u003eA calibration graph for glucose was constructed for HRP and all V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e NZs, using fixed-time assay method. Absorbance of the reaction mixture contains optimum concentration of all the reagents [as mentioned in table (1)] and 17.5 units of GOD with varying concentrations of glucose (0.0144 to 3.7mM) was recorded at 470nm with respect to blank reagent mixture after the incubation period of 10 minutes. The result shows good linearity between 0.0289 to 0.925mM for HRP, VNShs VNFws, and 0.0528 to 0.925mM for VNSps. The linearity of glucose with respect to HRP and all NZs is showing in the Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLinearity of HO\u003c/h3\u003e\n\u003cp\u003eA calibration graph for H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e was constructed for HRP and three V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e NZs using both rate and fixed-time assay methods. In the rate method, the absorbance was recorded with respect to the control blank for 5 minutes with a time interval of 1 minute at 470nm. In fixed time assay method, the reaction mixture was incubated for 10 minutes and the absorbance was recorded with respect to the control blank at 470nm. The linearity for the assay of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e was investigated in 3 mL of the reaction mixture and the concentration of reagents used are tabulated in Table\u0026nbsp;(1) for HRP and all NZs.\u003c/p\u003e \u003cp\u003eWith respect to rate method, HRP and VNFws showed a limited linearity range between 0.0242-0.3877mM. VNSps and VNShs showed a very good response towards H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e with a large linearity range between 0.0606-0.9692mM and 0.0969-1.5507mM respectively. This avoids the dilution of real samples for its application studies. Due to the different linearity range of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e for enzyme and NZs, depending on the sample, selectivity of NZs can be done for the quantification of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eIn fixed time method, HRP showed linearity between 0.0030-0.3877mM, linearity in very lesser concentration of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, whereas in NZs, VNSps showed a linearity in lower concentration of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e between 0.0076-0.9692mM. VNShs and VNFws showed a linearity upto same lower concentration i.e. between 0.0121-0.7753mM and 0.0121\u0026ndash;0.3877 mM respectively. The linearity of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e with respect to HRP and all NZs bye rate and fixed time method are showing in the Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eReagents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHRP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVNShs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVNFws\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVNSps\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePAP (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNEDA (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBuffer (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHRP or NZs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0185 units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3299mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1099mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2199mM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLinearity of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRate method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0242\u0026ndash;0.3877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0969\u0026ndash;1.5507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0242\u0026ndash;0.3877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0606\u0026ndash;0.9692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFixed time method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0030\u0026ndash;0.3877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0121\u0026ndash;0.7753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0121\u0026ndash;0.3877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0076\u0026ndash;0.9692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRegression equation (Y=)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRate method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2504x\u0026thinsp;+\u0026thinsp;0.0063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0823x \u0026minus;\u0026thinsp;0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3027x \u0026minus;\u0026thinsp;0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.072x \u0026minus;\u0026thinsp;0.0011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFixed time method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8423x\u0026thinsp;+\u0026thinsp;0.0374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1963x\u0026thinsp;+\u0026thinsp;0.0113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5118x \u0026minus;\u0026thinsp;0.0077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.389x\u0026thinsp;+\u0026thinsp;0.0272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRegression coefficient (R\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRate method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFixed time method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;(1): linearity range of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and concentrations of reagents used.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEffect of concentration of analytical reagents on the rate of reaction\u003c/h3\u003e\n\u003cp\u003eThe effect of varying concentrations of analytical reagents (PAP, NEDA, and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) on the rate of the reaction was investigated under experimental settings with 3mL solution by varying the concentration of one reagent at a time. The results show an increase in the absorbance with an increase in the concentration of analytical reagents up to optimized concentration afterward, there is no significant change in the rate of the reaction or the rate of the reaction decreases slightly. As a result, the final optimal concentration was fixed at the same level for all further experiments. Optimized concentrations of all analytical reagents are tabulated below (Table\u0026nbsp;2). The graphs of rate versus concentration of PAP, NEDA, and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e are shown in Fig. S\u003csub\u003e1\u003c/sub\u003e, S\u003csub\u003e2\u003c/sub\u003e, S\u003csub\u003e3\u003c/sub\u003e. \u0026amp; S\u003csub\u003e4\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eTherefore, the concentrations of reagents needed by NZs are far lower than those needed by HRP enzyme. The PAP concentration needed by VNShs and VNFws are same and lower than that of VNSps, which is lower than that of HRP. NEDA required by the VNFws and VNSps are same, and greater than the HRP, which is greater than VNShs. H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e required by the HRP and VNFws is same and lesser than the VNSps and which is lesser then the VNShs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReagents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHRP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVNShs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVNFws\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVNSps\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAP (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEDA (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e(mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNZs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0185 units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3299mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1099mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2199mM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;(2): Optimized values of analytical reagents.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of analytical characteristics of the proposed assay\u003c/h2\u003e \u003cp\u003eThe Michaelis\u0026ndash;Menten constant (K\u003csub\u003em\u003c/sub\u003e) for PAP, NEDA \u0026amp; H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e was determined by Lineweaver\u0026ndash;Burk plot, keeping all the reagents at optimized condition \u0026amp; by varying one reagent concentration (PAP, NEDA \u0026amp; H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) at a time. The K\u003csub\u003em\u003c/sub\u003e values for PAP, and NEDA are 1.0867 and 0.3142mM for HRP, 0.3179 and 0.0327mM for VNShs, 0.2108 and 0.07499mM for VNFws, 0.4625 and 0.3085mM for VNSps and the Lineweaver\u0026ndash;Burk plots are showed in Fig. S\u003csub\u003e5\u003c/sub\u003e, S\u003csub\u003e6\u003c/sub\u003e, S\u003csub\u003e7\u003c/sub\u003e, \u0026amp; S\u003csub\u003e8\u003c/sub\u003e. K\u003csub\u003em\u003c/sub\u003e values with respect to H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, V\u003csub\u003emax\u003c/sub\u003e, K\u003csub\u003ecat\u003c/sub\u003e, and K\u003csub\u003eeff\u003c/sub\u003e are tabulated (Table\u0026nbsp;3). Lineweaver\u0026ndash;Burk plots for the determination of K\u003csub\u003em\u003c/sub\u003e values with respect to H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e were showed in the Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eK\u003csub\u003em\u003c/sub\u003e values for all NZs is lesser than that of the HRP enzyme in the increasing order of VNSps, VNFws, VNShs and HRP which indicates the large affinity of NZs towards substrate (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) than HRP enzyme. V\u003csub\u003emax\u003c/sub\u003e is maximum for VNShs is greater than the HRP followed by VNFws and VNSps. K\u003csub\u003ecat\u003c/sub\u003e and K\u003csub\u003eeff\u003c/sub\u003e of HRP is much greater than all NZs which is in the increasing order of VNShs, VNSps and VNFws. Comparison of catalytic parameters are showen in Table\u0026nbsp;(4) indicates that the synthesised NZs have higher affinity towards substrates than some reported NZs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReagents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eK\u003csub\u003em\u003c/sub\u003e for H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (mM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV\u003csub\u003emax\u003c/sub\u003e (mM/sec)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK\u003csub\u003ecat\u003c/sub\u003e (sec\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eK\u003csub\u003eeff\u003c/sub\u003e (mM\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003esec\u003csup\u003e\u0026minus;\u0026thinsp;1)\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.4166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.3908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVNShs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.3021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVNFws\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.2817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.5034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVNSps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.0334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;(3): Catalytic parameters.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSl. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNanozyme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e linearity, LOD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK\u003csub\u003em\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNickel metal-organic framework 2D NShs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u0026ndash;160\u0026micro;M\u003c/p\u003e \u003cp\u003eLOD-8 nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTMB-0.365mM\u003c/p\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e-2.49mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eporous PtCu dendrites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u0026ndash;325\u0026micro;M\u003c/p\u003e \u003cp\u003eLOD-0.1 \u0026micro;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTMB- 0.08 mM\u003c/p\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e- 0.26 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePt NPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;50\u0026micro;M\u003c/p\u003e \u003cp\u003eLOD-1\u0026micro;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTMB-0.091 mM\u003c/p\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e-80.25 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003erhodium nanoparticles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-100\u0026micro;M\u003c/p\u003e \u003cp\u003eLOD- 0.20 \u0026micro;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTMB-0.198 mM\u003c/p\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e- 0.38 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVNShs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0969-1.5507mM\u003c/p\u003e \u003cp\u003eLOD-0.066mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e- 0.7843mM\u003c/p\u003e \u003cp\u003ePAP- 0.3179 and NEDA-0.0327mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePresent work\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVNFws\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0242-0.3877mM\u003c/p\u003e \u003cp\u003eLOD-0.0425mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e- 0.6514mM\u003c/p\u003e \u003cp\u003ePAP- 0.2108 and NEDA- 0.0749mM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVNSps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0606-0.9692mM\u003c/p\u003e \u003cp\u003eLOD-0.1474mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e- 0.6398mM\u003c/p\u003e \u003cp\u003ePAP- 0.4625 and NEDA- 0.3085mM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;(4): comparison of catalytic parameters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCharacterization of vanadium oxide Nano crystals.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSEM and EDS analysis\u003c/h2\u003e \u003cp\u003eFE-SEM images of vanadium oxide NPs synthesized by different methods were showed in Fig.\u0026nbsp;7 \u0026amp; S\u003csub\u003e9\u003c/sub\u003e, S\u003csub\u003e10\u003c/sub\u003e, S\u003csub\u003e11\u003c/sub\u003e. Based on the type of the morphology, the NPs were named as VNShs, VNFws and VNSps. During heat treatment of hydrogen diperoxodioxovanadate (III) complex to form brownish gel (V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e.nH\u003csub\u003e2\u003c/sub\u003eO), sheet like NPs is formed. When brown solution of hydrogen diperoxodioxovanadate (III) complex was treated with sodium dihydrogen orthophosphate followed by hydrothermal process, flower like NPs were formed. When the yellow transparent solution of ammonium metavanadate and HCl was treated with reducing agent like hydrazine hydrate forms spheres like NPs. Elemental composition of the NPs was showing the presence of 41.97% of vanadium and 58.03% of oxygen in VNShs, 51.56% vanadium and 48.44% of oxygen in VNFws, 36.2% of vanadium and 63.8% of oxygen in VNSps, which indicates the formation of pure vanadium oxide NPs. EDS spectres with percentage compositions were showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eXRD\u003c/h2\u003e \u003cp\u003eThe XRD pattern of the NPs were shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The XRD peaks of VNFws and VNSps, the different peaks were exactly matched with standard card JCPDS no. 41-1426, representing vanadium pentoxide NPs in orthorhombic phase. whereas VNShs were matched with [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], which correspondence to rhombohedral structure of the V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e. The average crystalline size of the synthesised NPs was calculated using Scherrer\u0026rsquo;s and W-H equations and was found to be 45.42, 45.7nm for VNShs, 29.14, 32.5nm for VNFws, and 39.83, 38.7nm for VNSps. The degree of crystallinity index was calculated and was found to be 0.7978 for VNShs, 0.7856 For VNFws and 0.8570for VNSps. Therefore, in VNShs 79.78% is in crystalline and 20.22% is in amorphous form, in VNFws 78.57% is in crystalline and 21.43% is in amorphous form, and in VNSps, 85.70% is in crystalline and 14.30% is in amorphous form. Raw XRD patterns were showed in Fig. S\u003csub\u003e15\u003c/sub\u003e, S\u003csub\u003e16\u003c/sub\u003e \u0026amp; S\u003csub\u003e17\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDLS\u003c/h2\u003e \u003cp\u003eThe variations in the intensity of scattered light resulting from Brownian motion in synthesized NPs, size, and size distribution can be predicted with the help of the distribution curve in DLS spectra. The resultant DLS spectrums and size distribution of different morphological vanadium oxide NPs are shown below in Table\u0026nbsp;(5). It showed that the size distribution by the analysis of DLS are in good agreement with histogram plot data which is showed in Table S\u003csub\u003e1\u003c/sub\u003e, S\u003csub\u003e2\u003c/sub\u003e \u0026amp; S\u003csub\u003e3\u003c/sub\u003e. Zeta potential is a stability indicator of NPs, which is influenced by the surface charge and is measured by analysing particles' electrophilic mobility in an electric field. The Zeta potential of VNShs is 4.0mv, VNFws is -4.5mv and VNSps is 4.6mv which are almost neutral[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The Polydispersity index (PDI) is a parameter used to measure the size distribution, specifying the uniformity of NPs. International standards organizations have established that PDI values of 0.1 to 0.25 indicate a small size distribution (monodisperse samples) and values greater than 0.5 are common to broad distribution (polydisperse samples)[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. PDI values of VNShs is 1.144, VNFws is 1.834 and VNSps is 1.896 which indicates polydispersity with multiple-sized particles, which might be due to the rapid agglomeration of the NPs due to low zeta potentials[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Size distribution spectres as obtained by DLS analysis is showed in the Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. DLS result on Surface charge measurements are showed in Fig. S\u003csub\u003e12\u003c/sub\u003e, S\u003csub\u003e13\u003c/sub\u003e, \u0026amp; S\u003csub\u003e14\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNZs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVolume %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiameter(nm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVNShs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVNFws\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVNSps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;(5): size distribution of different shaped vanadium oxide NZs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFT-IR Spectra analysis\u003c/h2\u003e \u003cp\u003eTo study the nature of binding between vanadium and oxygen in V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e NPs, FT-IR was recorded in the range of 400 to 4000cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e wave number, which identifies the chemical bonds as well as functional groups in the compound. FTIR spectra of V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e NPs exhibited three characteristic vibration modes. The peaks near 470 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e are due to the V-O-V bond symmetric stretch, 800cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e is due to the V-O-V asymmetric stretch and the peak around 1000cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e can be assigned to the V\u0026thinsp;=\u0026thinsp;O stretching, the peaks around 600 to 700cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e is potentially originate from the V\u0026thinsp;\u0026minus;\u0026thinsp;O bond, and the peaks from 1950 to 2350cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e are due to δ(HOH) and ט γ(OH) in water. FRIR spectres of V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e NPs were showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eInfluence of interfering species\u003c/h2\u003e \u003cp\u003eTo study the effect of interfering species, optimized experimental conditions were used by taking several cations, anions and amino acids using hydrogen peroxide concentration of 0.0969mM for HRP, 0.1938mM for both VNShs and VNFws and 0.4845mM for VNSps. Influence of interfering species is tabulated in Table\u0026nbsp;(6) as tolerant ratio. It is the ratio of limit of interfering species concentration to that of the concentration of hydrogen peroxide. The result shows high interference for Fe\u003csup\u003e2+\u003c/sup\u003e, Fe\u003csup\u003e3+\u003c/sup\u003e, and FAS (iron-containing salts) with HRP and all NZs. Zr\u003csup\u003e4+\u003c/sup\u003e, Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e, Glycine, Glucose, and Alanine show average interference, whereas NH\u003csub\u003e4\u003c/sub\u003eCl, Na\u003csup\u003e+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, Zn\u003csup\u003e2+\u003c/sup\u003e, Zr\u003csup\u003e4+\u003c/sup\u003e, KNO\u003csub\u003e3\u003c/sub\u003e, and Urea show less interference to a large extent.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabf\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabg\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c2\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eTolerant ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHRP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVNShs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVNSps\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVNFws\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSe\u003csup\u003e4+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZr\u003csup\u003e4+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKNO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeucine, Isoleucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Histidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecysteine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethionine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCetric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-Valine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eglutamic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-ornithine HCl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe\u003csup\u003e3+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;(6): Interference study of different metal ions, organic and organic compounds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePrecision study\u003c/h2\u003e \u003cp\u003ePrecision studies were performed at optimized conditions of all the reagents along with four different concentrations of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e within the linearity range. The study included 10 runs in a day with a time interval of 1 h for within-day precision and 10 days run for day-to-day precision with one day interval. All solutions were prepared freshly every day(n\u0026thinsp;=\u0026thinsp;10). The standard deviation (SD) and percentage standard deviation (%SD) were showed in Table\u0026nbsp;(7). The results show Intraday %SD is greater than the Inter-day %SD in all cases and VNSps has highest %SD when compared to other NZs and HRP. The Limit of detection (LOD), and Limit of quantification (LOQ) were determined by taking the readings of blank regent versus blank reagent. LOD and LOQs were found to be 0.0548 mM and 0.1662 mM for HRP, 0.066 mM and 0.2002 mM for VNShs, 0.0425 mM and 0.1287 mM for VNFws and 0.1474 mM and 0.4465 mM for VNSps.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabh\" border=\"1\"\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHRP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003eVNShs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e] in mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e%SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e] in mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e%SD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntraday\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInter-day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntraday\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInter-day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIntraday\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInter-day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIntraday\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eInter-day\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.3877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.3616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.1938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.1883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.0969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.6163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.0317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.0484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.1507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.4792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVNFws\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eVNSps\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e] in mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e%SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e] in mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e%SD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntraday\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInter-day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntraday\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInter-day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIntraday\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInter-day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIntraday\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eInter-day\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.3877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.7465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.1938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.3316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.0969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.4247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.0485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.6942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eTable\u0026nbsp;(7): Inter \u0026amp; Intraday precision study of the proposed method. (n\u0026thinsp;=\u0026thinsp;10)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRecovery and applicability\u003c/h2\u003e \u003cp\u003e \u003cb\u003eWith glucose\u003c/b\u003e \u003c/p\u003e \u003cp\u003eRecovery studies were conducted by inoculating the standard glucose to serum sample in the proposed method. The serum sample has been analysed by spiking 10\u0026micro;L directly to the reaction mixture. The result shows recovery rate ranging between 71.51 and 96.8% for HRP, 74.58 and 97.65% for VNShs, 75.29 and 97.28% for VNFws, 76.89 and 97.79% for VNSps. The recovery percentage is high with higher concentration of glucose, decreases with decrease in concentration and the percentage recovery is almost similar for HRP and NZs. The results are showen in the Table\u0026nbsp;(8).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabi\" border=\"1\"\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBlood sample in \u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eHRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eVNShs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlucose (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdded (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFound (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRecovered*(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGlucose (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdded (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFound (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRecovered*(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e97.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e74.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eVNFws\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eVNSps\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlucose (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdded (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFound (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRecovered*(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGlucose (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdded (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFound (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRecovered*(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e97.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e97.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e76.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;(8): recovery study of glucose in blood sample.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWith H\u003c/b\u003e \u003csub\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sub\u003e \u003cb\u003eO\u003c/b\u003e \u003csub\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sub\u003e \u003c/p\u003e \u003cp\u003eRecovery studies were conducted by inoculating the standard H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e to serum sample in the proposed method. The experiment demonstrated repeatability and minimal interference from inhibitory species, resulting in a recovery rate ranging from 99.72\u0026ndash;99.93% for HRP, 94.99\u0026ndash;98.92% for VNShs, 95.70\u0026ndash;99.16% for VNFws and 98.13\u0026ndash;99.55% for VNSps as shown in the Table\u0026nbsp;(9). The results show the recovery percentage is maximum when the added H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e concentration was maximum, minimum when added H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e concentration was minimum and highest recovery percentage with HRP and least with VNShs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabj\" border=\"1\"\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBlood sample in \u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eHRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eVNShs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdded (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFound (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRecovered*(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdded (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFound (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRecovered*(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.7618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e98.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e97.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e94.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eVNFws\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eVNSps\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdded (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFound (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRecovered*(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdded (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFound (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRecovered*(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e99.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e99.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e98.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;(9): recovery study of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e in blood sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eProbable reaction mechanism\u003c/h2\u003e \u003cp\u003e\u003cimg 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\" width=\"372\" height=\"245\"\u003e\u003c/p\u003e \u003cp\u003eV\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e [V(V)] NZs oxidises P-amino phenol to 4-iminocyclohexa-2, 5-dien-1-one (oxidized P-amino phenol) and converted into V(IV). V(IV) donates electron to H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and convert them to H\u003csub\u003e2\u003c/sub\u003eO. Oxidized P-amino phenol coupled with NEDA to form 4-((4-((2-aminoethyl) amino) naphthalen-1-yl)imino)cyclohexa-2,5-dien-1-one which is an orange-red coloured product with maximum absorbance at 470nm.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe proposed method which involves coupling of oxidized PAP and NEDA using H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, is very easy to use, quick and sensitive to H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e assay. The reagents utilized are affordable, widely accessible, and soluble in water. Since the coupled product's maximum absorbance occurs at 470nm, this method can also be used for the quantification of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e in biological samples. The linearity of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e in the range of 0.0242 to 0.3877mM for HRP, 0.0969 to 1.5507mM for VNShs, 0.0242 to 0.3877mM for VNFws and 0.0606 to 0.9692mM for VNSps which indicates the sensitivity of the proposed method.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthical Approval\u003c/strong\u003e \u003cp\u003eThe institutional Human Ethical Committee (IHEC-UOMNo.22/Ph.D/2008-09) of the university of mysore has granted us permission to use of human blood serum sample\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNYG prepared the manuscript, RHS helped to prepare the manuscript and all authors are reviewed the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eOne of the authors, Nikhil Y Gangadhara would like to thank ATME college of engineering, mysore, Karnataka, for providing the research laboratory facilities.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe authors affirm that the data supporting the discoveries of the study are accessible within the paper and its supplementary information file.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTripathy S, Mohanty PK (2017) Reactive oxygen species (ROS) are boon or bane. 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J Hazard Mater 261:414\u0026ndash;420\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"vanadium oxide nanoparticle, nanozyme, peroxidase mimics, Horseradish peroxidase, glucose, glucose oxidase","lastPublishedDoi":"10.21203/rs.3.rs-5318695/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5318695/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnzyme mimics are developed as an alternative to natural enzymes to overcome the inherent limitations of natural enzymes. Among different types of enzyme mimics, nanozymes gained importance due to their tuneable catalytic properties. In this article, we discuss the peroxidase behaviour of different shape V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e nanoparticles (NPs). A simple spectrophotometric method is presented for the quantification of glucose and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2 \u003c/sub\u003eusing novel chromogenic reagents. The NPS are characterized with SEM, DLS, EDS, FTIR and XRD. From SEM images, based on the morphology, the NPs were named as vanadium nanosheets (VNShs), nanoflowers (VNFws) and nanospheres (VNSps). The average crystalline size of the nanoparticles is calculated using XRD data from Scherrer’s equation and Williamson-Hall plot and was found to be 45.42, 45.7nm for VNShs, 29.14, 32.5nm for VNFws, and 39.83, 38.7nm for VNSps respectively. The linearity of glucose was ranged from 0.0289 to 0.925mM for HRP, VNShs VNFws, and 0.925 to 0.0528mM for VNSps. The H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e was in good linear range between 0.003 to 1.9383mM in both rate and fixed time method for all nanozymes and HRP. For recovery study 10µL serum sample was directly used without dilution. The K\u003csub\u003em\u003c/sub\u003e values were found to be 1.6239 mM for HRP, 0.7843 mM for VNShs, 0.6514 mM for VNFws, ands 0.6398 mM for VNSps concluding that NZs have better affinity towards substrate molecule. The detection limit and quantification limits were found to be 0.0548mM and 0.1662mM for HRP, 0.066mM and 0.2002mM for VNShs, 0.0425mM and 0.1287mM for VNFws and 0.1474mM and 0.4465mM for VNSps.\u003c/p\u003e","manuscriptTitle":"Peroxidase Mimicking V2O5 Nanozymes as the Spectrophotometric Sensor for the Determination of Glucose in Human Serum Sample Employing New Chromogenic Co-Substrates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-28 11:44:38","doi":"10.21203/rs.3.rs-5318695/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0eade78f-71ad-4548-ae4f-617466585dd7","owner":[],"postedDate":"October 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-08T14:23:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-28 11:44:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5318695","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5318695","identity":"rs-5318695","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}