Optimization, Characterization, and Antibacterial Activity of Copper Nanoparticles Synthesized Using <i>Senna didymobotrya</i> Root Extract

Sadia, Bernard Otieno; Cherutoi, Jackson Kiplagat; Achisa, Cleophas Mecha

doi:https://doi.org/10.1155/2021/5611434

Journal of Nanotechnology

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Abstract Introduction Materials and Methods Results and Discussion Conclusion Abbreviations Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2021 | Article ID 5611434 | https://doi.org/10.1155/2021/5611434

Optimization, Characterization, and Antibacterial Activity of Copper Nanoparticles Synthesized Using Senna didymobotrya Root Extract

Bernard Otieno Sadia,^1,2Jackson Kiplagat Cherutoi,¹and Cleophas Mecha Achisa³

Academic Editor: Brajesh Kumar

Received11 Aug 2021

Revised03 Oct 2021

Accepted08 Oct 2021

Published15 Oct 2021

Abstract

The economic burden and high mortality associated with multidrug-resistant bacteria is a major public health concern. Biosynthesized copper nanoparticles (CuNPs) could be a potential alternative to combat bacterial resistance to conventional medicine. This study for the first time aimed at optimizing the synthesis conditions (concentration of copper ions, temperature, and pH) to obtain the smallest size of CuNPs, characterizing and testing the antibacterial efficacy of CuNPs prepared from Senna didymobotrya (S. didymobotrya) roots. Extraction was done by the Soxhlet method using methanol as the solvent. Gas chromatography-mass spectrometry (GC-MS) analysis was performed to identify compounds in S. didymobotrya root extracts. Box–Behnken design was used to obtain optimal synthesis conditions as determined using a particle analyzer. Characterization was done using ultraviolet-visible (UV-Vis), particle size analyzer, X-ray diffraction, zeta potentiometer, and Fourier transform infrared (FT-IR). Bioassay was conducted using the Kirby–Bauer disk diffusion susceptibility test. The major compounds identified by GC-MS in reference to the NIST library were benzoic acid, thymol, N-benzyl-2-phenethylamine, benzaldehyde, vanillin, phenylacetic acid, and benzothiazole. UV-Vis spectrum showed a characteristic peak at 570 nm indicating the formation of CuNPs. The optimum synthesis conditions were temperature of 80°C, pH 3.0, and copper ion concentration of 0.0125 M. The FT-IR spectrum showed absorptions in the range 3500–3400 cm⁻¹ (N-H stretch), 3400–2400 cm⁻¹ (O-H stretch), and 988–830 cm⁻¹ (C-H bend) and peak at 1612 cm⁻¹ (C=C stretch), and 1271 cm⁻¹ (C-O bend). Cu nanoparticle sizes were 5.55–63.60 nm. The zeta potential value was −69.4 mV indicating that they were stable. The biosynthesized nanoparticles exhibited significant antimicrobial activity on Escherichia coli and Staphylococcus aureus with the zone of inhibition diameters of 26.00 ± 0.58 mm and 30.00 ± 0.58 mm compared to amoxicillin clavulanate (standard) with inhibition diameters of 20 ± 0.58 mm and 28.00 ± 0.58 mm, respectively.

1. Introduction

Nanotechnology is of great scientific interest due to its wide application in pharmaceutical products, electronics, biotechnology, and medicine [1, 2]. Nanoparticles are solid particles with sizes approximately extending from 1 nm to 100 nm in length in at least one dimension [3]. Their application in the field of biotechnology has grown because of their comparable size range scale to biomolecules and their versatile properties that can be controlled using the method used for their biosynthesis [4]. Copper nanoparticle (CuNP) is one of the most common nanoparticles utilized in medicine. They have been synthesized using both physical and chemical methods [4]. Physical methods experience low production of nanoparticles and high energy consumption to maintain high temperature and pressure utilized during the synthesis process. Chemical methods are known to use noxious precursor chemicals, harmful by-products, and uses toxic solvents [5, 6].

Due to the limitations of physical and chemical methods of CuNPs synthesis, biological methods have been developed. These use bacteria, algae, fungi, plants, and plant products. The biological method of synthesis of the copper nanoparticle is considered a bottom-up technique, where oxidation or reduction is the main reaction that occurs during the production of nanoparticles [7]. Currently, the use of phytochemicals in the synthesis of nanoparticles is being explored as such nanoparticles are friendly both to the environment and humans [8]. Biosynthesized inorganic nanoparticles can offer solutions to the emergence of multidrug-resistant microbes. This can act as substitutes to the traditional organic agents that have limited application due to the high rate of decomposition and low heat resistance. The unique chemical and physical properties, low-cost preparation, surface-to-volume ratio, and low toxicity make CuNPs command a superior position as gas sensors, photocatalysts, dye absorbents, antioxidant, antimicrobial, antimalarial, and antitumor agents in comparison to nanoparticles prepared from gold, zinc, iron, and silver compounds [2, 9–13].

Senna didymobotrya (Fresen) Irwin & Barneby (Synonym: Cassia didymobotrya Fresen.), a plant from the genus Senna and family Fabaceae has been used by different ethnic communities worldwide [14–16]. In Kenya, it is used to treat malaria, fungal and bacterial infections, hypertension, haemorrhoids, sickle cell anaemia, a range of diseases affecting women such as inflammation of fallopian tubes, fibroids, and backache, stimulate lactation, and induce uterine contraction and abortion [17–26]. Preceding authors reported that aqueous and organic extracts and fractions of different parts (leaves, flowers, twigs, roots, stem bark, immature pods, and root bark) of S. didymobotrya elicited antipyretic activity [27], hypolipidemic activity [28], antimicrobial activity against bacteria (such as Bacillus cereus, Bacillus subtilis, Escherichia coli, Enterobacter aerogenes, Lactobacillus acidophilus, Klebsiella pneumoniae, Proteus vulgaris, Ralstonia solanacearum, Salmonella typhi, Serratia liquefaciens, and Streptococcus mitis) [29–40] and fungi (such as Aspergillus niger, Candida albicans, Candida glabrata, Candida krusei, Candida parapsilosis, Candida tropicalis, Candida duobushaemulonii, Candida haemulonii, Candida auris, Candida famata, Candida orientalis, Cryptococcus neoformans, Trichophyton mentagrophyte, and Microsporum gypseum) [15, 34, 35, 40–42]. Insecticidal activity (against fleas and Acanthoscelides obtectus) and anthelmintic and antiamoebic activities as well as toxicity of the extracts have also been reported [34–36, 43, 44].

Classical phytochemical screening of various extracts of S. didymobotrya has indicated that anthraquinones, tannins, saponins, naphthoquinones, terpenes, steroids, alkaloids, flavonoids, phenols, and terpenoids are the major secondary therapeutic secondary metabolites [14, 30, 34, 36, 45, 46]. Alemayehu et al. [45] isolated and characterized for the first time 2,6,4′-trihydroxy-trans-stilbene (a stilbenoid derivative) and 4-(2′-oxymethylene-4′-hydroxyphenyl) chrysophanol (a phenyl anthraquinone) from chloroform/methanol extract of S. didymobotrya roots. Later, chromatographic separation of the hexane and dichloromethane extracts of S. didymobotrya roots led to the identification of terpenoids (3β-sitosterol and stigmasterol) and anthraquinones (chrysophanol and physcion) [35]. Recently, terpinolene and alpha-pinene were reported as the main antipyretic compounds in dichloromethane extract of S. didymobotrya leaves [27].

Though some pharmacological activities and toxicity of different extracts of S. didymobotrya parts have been reported, there is no report on the synthesis of CuNPs and antimicrobial activity of nanoparticles synthesized from this plant. The current study therefore for the first time investigated the synthesis of CuNPs using S. didymobotrya roots extracts and their antimicrobial efficacy against E. coli and Staphylococcus aureus. Since the synthesis of nanoparticles of smaller and uniformly distributed size, crystalline, and good stability requires control of experimental conditions [47, 48], synthesis conditions (concentration of copper ions, temperature, and pH) for the CuNPs were optimized.

2. Materials and Methods

2.1. Chemicals and Reagents

Copper (II) sulphate pentahydrate (CuSO₄·5H₂O), anhydrous sodium sulphate, silica gel, and methanol were purchased from Merck Ltd., USA. All the chemicals and reagents were of analytical grade and were used without further purification. E. coli (ATCC 25922), S. aureus (ATCC 25923), Kirby–Bauer disks, amoxicillin clavulanate, and 0.5 McFarland standards were obtained from Cypress Diagnostics, Belgium.

2.2. Sample Collection and Preparation

S. didymobotrya roots were harvested from plants growing in their natural habitat in West Uyoma sublocation, Siaya County, Kenya (). They were identified and authenticated at the Department of Biological Sciences, Moi University (Kenya), where a voucher specimen (SD 2018/03) was deposited for future reference.

The collected roots were washed several times with distilled water to remove dust. They were dried at room temperature under shade for three weeks. After, they were chopped into small pieces and pulverized using a laboratory mill. The extraction was carried out according to the method described by Kigondu et al. [49] with slight modifications. Weighed 50 g of the root powder was transferred into the Soxhlet apparatus and extracted with 250 mL of methanol for 48 hours. Methanol was used as the solvent of extraction because it was the best solvent of extraction according to trial extractions done using diethyl ether, methanol, and distilled water. The crude extract was concentrated by rotary evaporation at 40°C and transferred to a desiccator containing anhydrous sodium sulphate. The percentage yield of the crude extract was determined as per the following equation [50]:

2.3. Gas Chromatography-Mass Spectrometry Analyses

GC-MS analysis was performed using an Agilent 8890A GC system interfaced with a 5977B mass spectrometer detector fused with a capillary column (30 × 0.25 mm, 0.25 μm). For GC-MS detection, an electron ionization system was operated in electron impact mode with an ionization energy of 70 eV. Helium gas (99.999%) was used as a carrier gas at a constant flow mode of 1.2 ml/min, and an injection volume of 2 μL was employed (a split ratio of 10:1). The injector temperature was maintained at 250°C; the ion-source temperature was 200°C; and the oven temperature was programmed from 60°C (for 1.5 min), with an increase of 20°C/min to 220°C, then 5°C/min to 280°C (4 min), and ending with a 10 min isothermal at 280°C. Mass spectra were taken at 70 eV. The Jet‐Clean Ion Source temperature was at 320°C, and MS Quadrupole was at 180°C with a scan interval of 0.5 s. The solvent delay was 0 to 3 min, and the total GC-MS running time was 36 min. Identification of the peaks was based on computer matching of the mass spectra with the National Institute of Standards and Technology (NIST 08) library; direct comparison with the published data was also utilized.

2.4. Synthesis, Optimization, and Characterization of CuNPs from S. didymobotrya Root Extracts

2.4.1. Synthesis of CuNPs

Synthesis of CuNPs was carried out by adding 10 mL of S. didymobotrya root extracts to 90 mL of 0.0125, 0.03125, and 0.05 M of CuSO₄·5H₂O solution. The reaction mixture was kept on a magnetic stirrer at 200 rpm and varying temperatures (40, 60, and 80°C) and pH (3, 6.5, and 10 pH). The reaction mixture was centrifuged at 5,000 rpm for 5 min to remove any free biomass residue. The supernatant was again centrifuged at 12,000 rpm for 40 min to obtain pellets. The pellets of CuNPs were resuspended using distilled water. The reduction of Cu ions was measured by UV-Vis spectrophotometer (Beckham Coulter DU 720, Beckham Coulter Inc., USA) after 4 hours.

2.4.2. Optimization of Synthesis of CuNPs

A three-level Box–Behnken experimental design was used for the optimization of analytical parameters affecting the synthesis of CuNPs [51] using the methanolic root extract of S. didymobotrya. Minitab statistical software (v17, Minitab Inc., USA) was used for the experimental design. The selected design matrix from Box–Behnken consisted of 15 trials. The parameters optimized were concentration of copper ions (0.0125–0.05 M), pH of the mixture (3.0–10.0), and temperature of the mixture (40°C–80°C) on the particle size of CuNPs. Response variables as a function of the synthesized parameters followed a second-order polynomial as follows:where Conc = concentration and Temp = temperature.

2.4.3. Ultraviolet-Visible Spectroscopy

Synthesized CuNPs (300 μL) were diluted with 3 mL of distilled water and scanned on a UV-Vis spectrophotometer (Beckham Coulter DU 720, Beckham Coulter Inc., USA) from 300 to 700 nm at a resolution of 1 nm using distilled water as the blank [52].

2.4.4. Particle Size Analysis

The sizes of synthesized CuNPs were measured using a particle size analyzer (Microtrac Nanotrac Wave II, SL-PS-25 Rev. H) with a laser diode detector.

2.4.5. X-Ray Diffraction Analysis

The synthesized CuNPs were subjected to an X-ray diffraction (XRD) analyzer operated at the voltage of 40 kV and 20 mA with copper Kα radiation in the range of θ–2θ configuration with a scanning rate of 0.030°C/s. The crystallite size (CS) was calculated using Debye–Scherrer equation as follows [52, 53]:where constant (K) = 0.94, λ = 1.5406 × 10⁻¹⁰, cos θ = Bragg angle, and β is the full width at half maximum (FWHM). Full width at half maximum in radius (β) = FWHM × π/180.

2.4.6. FT-IR Analysis

FT-IR analysis was performed to identify functional groups bound on the surface of the CuNPs. The specimen and potassium bromide granules were powdered together in a ratio of 1:100 (w/w) and then compressed into pellets. Subsequently, the analysis was performed and measured using FT-IR spectrophotometer in the range of 400–4,000 cm⁻¹ and with a resolution of 4 cm⁻¹ [52, 53].

2.5. Antibacterial Activity of the Synthesized CuNPs from the S. didymobotrya Root Extract

The antimicrobial efficacy of biosynthesized 4 cm⁻¹ CuNPs was assessed using Kirby–Bauer disk diffusion susceptibility test protocol [54]. The test microorganisms were chosen according to the National Committee for Clinical Laboratory Standards 2010 protocols [55]. Gram-negative E. coli and Gram-positive S. aureus were tested. Amoxicillin clavulanate impregnated antimicrobial susceptibility testing discs were used as a positive control. All bioassay was done with 30 μL of solution of CuNPs resuspended in distilled water, S. didymobotrya root extract, and copper sulphate solution as per the specification of the positive control (amoxicillin clavulanate). After 18 hours of incubation, the zone of inhibition diameter (ZOI) was measured to the nearest millimetre using a ruler and recorded. The susceptibility or resistance of the test organism to each drug tested was determined using the published Clinical Laboratory Standards Institute (CLSI). The ZOI was classified as susceptible (S), intermediate (I), or resistant (R) based on the CLSI interpretive criteria [50].

3. Results and Discussion

The percentage yield of S. didymobotrya root powder was 9.94% as calculated using equation (1).

3.1. GC-MS Results

The GC-MS analysis on S. didymobotrya methanolic root extract was conducted to identify the active phytochemicals that might take part in the fabrication of CuNPs. The results indicated that the extract contained mainly fatty acids and some volatile organic compounds. The compounds along with their retention times, abundances, molecular formulae, and molecular weights are presented in Table 1. The major compounds identified were benzoic acid, thymol, n-benzyl-2-phenethylamine, benzaldehyde, vanillin, phenylacetic acid, and benzothiazole.

There is a paucity of literature on volatile compounds in S. didymobotrya. This study presented the first comprehensive report on the GC-MS analysis of volatile compounds in S. didymobotrya extract. Previously, Mworia et al. [27] reported the presence of terpinolene and alpha-pinene as the main antipyretic compounds in dichloromethane extract of S. didymobotrya leaves using GC-MS. None of the foregoing compounds were identified in this study. Interestingly, some compounds identified in the methanolic extract of S. didymobotrya roots in this study have a potential to take part in the formation of nanoparticles. For instance, alizarin (a dihydroxyanthraquinone with two hydroxyl groups on a phenyl ring) possesses a structure similar to compounds proposed to take part in chelation and reduction of copper ions to CuNPs [2, 56].

3.2. Synthesis and Characterization of CuNPs

Bioreduction of copper ions to CuNPs on exposure to methanolic extract of S. didymobotrya roots was monitored by observing the colour change and using UV–visible spectroscopy. There was a gradual colour change from light orange solution to dark brown, indicating the formation of CuNPs after 4 hours [57–61]. Pretrial runs indicated that no significant changes occurred after 3 hours. Usually, small metal nanoparticles absorb visible electromagnetic waves through the collective oscillation of conduction electrons at the surface, a phenomenon known as surface plasmon resonance (SPR) effect [62]. Thus, the final dark colour observed could be ascribed to the excitation of surface plasmon vibrations, indicating the formation of CuNPs [10, 52]. Copper oxides are thermodynamically more stable than copper sulphates, which leads to the aggregation and oxidation of copper without proper protection [62]. Thus, the addition of the S. didymobotrya root extract might have inhibited the oxidation of copper, thereby acting as a reducing and capping agent for the CuNPs [10].

The UV–Visible spectrum of the methanolic root extract of S. didymobotrya (Figure 1(a)) showed bands at λ_max 338 nm (band II). The band at 338 nm (band II) can either be due to n ⟶ π∗ transition or a combination of n ⟶ π∗ and π ⟶ π∗ transitions of heteroatoms linked in a double bond. The presence of quercetin, a class of flavonoids, has also been reported as a major constituent of the crude aqueous root extract of S. didymobotrya [63]. The observed transitions are probably related to quercetin involved in the reduction process and formation of CuNPs via π-electron interactions [56, 64]. Hence, the extract of S. didymobotrya roots further acted as a reductant and stabilizer agent.

(a)

(b)

The UV–Vis spectrum of the CuNPs (Figure 1(b)) showed changes in the absorbance maxima due to surface SPR, demonstrating the formation of CuNPs [65, 66]. The SPR peak, which is a signature of the formation of CuNPs, appears in the visible region [67, 68] at 542, 570, 604, 616, 638, 662, and 694, nm with absorbances of 0.064, 0.153, 0.066, 0.064, 0.065, 0.072, and 0.970, respectively (Figure 1). According to Mei’s theory, the occurrence of a single UV-visible peak in the UV-Visible spectrum of synthesized nanoparticles confirms that they are spherical in shape [58].

3.3. Design of Experiments and Optimization Analysis

Table 2 contains the list of experimental runs and the corresponding responses obtained from the experiments projected by Box–Behnken design. The design optimized parameters that would yield CuNPs with the least average particle size. The experiments were done as per the run order to eliminate experimental bias. The mean particle size of CuNPs was recorded on particle size analyzer (Nanotrac).

A regression coefficient (R²) of 0.9964 was obtained with a second-order quadratic equation generated for the optimization process. The adequacy of the model was checked using ANOVA. The predictor variables, that is, pH, concentration of copper ions, and temperature, of the mixture were all significant [47]. The value of indicated that pH () is the most influencing factor when compared to the concentration of copper ion () and temperature of the mixture (). Variance inflation factors (VIF) value close to 1 indicates that the predictors are not correlated (Table 3) [69]. The qualities of the fitted models were evaluated based on the coefficients of determination (R²) that was 0.9964. The model explains 99.64% of the variation in the average size data. The adjusted R² is 99.00%. R² (pred) is 94.31%, which indicates that the model explains 94.31% of the variation in the average size of CuNPs when used for prediction.

Figure 2 presents the main effects plot for the average size of CuNPs. The temperature of the reaction mixture, the concentration of copper ion, and pH of the medium affect the average size of CuNPs. Comparing the slopes of the lines, it can be concluded that pH had the greater magnitude of effects.

Figure 3 presents an interaction effects plot for mean size for Cu NPs. From the plot, it is seen that there was the interaction of temperature and concentration and the interaction of concentration and pH as shown by lines intersecting at a point, but there was no possible interaction of temperature and pH as indicated by lines being approximately parallel from each other.

3.3.1. Effect of pH

The pH of range 3–10 was varied during CuNPs average size optimization process. The study revealed that pH as a parameter strongly influenced the size of CuNPs as shown by Figure 3 of the interaction effects plot for mean particle size. The least average size of the nanoparticles was recorded at a lower pH of 3.0. It was observed that increasing the pH increased the mean size of the nanoparticles. Similar observations have been reported by Honary et al. [70] and Dang et al. [62]. A possible explanation for this observation is that at a pH of 3.0, nanoparticles were experiencing high electrostatic repulsion, hence reducing agglomeration. Therefore, at alkaline pH, the nanoparticles were exhibiting lower electrostatic forces hence allowing particle growth.

3.3.2. Effect of Copper Ion Concentration

Copper ion concentration (0.0125–0.05 M) was varied for CuNPs average size optimization. The least mean size of nanoparticles was recorded at lower concentrations of copper salt as revealed in Figure 3. This finding agrees with previous findings [70, 71] that reported that high salt ion concentrations led to large particle sizes and broad size distribution of synthesized nanoparticles. This could be because a low concentration of salt reduced the probability of copper-copper interactions, hence reducing agglomeration.

3.3.3. Effect of Temperature

A temperature of range 40–80°C was controlled for CuNPs mean size optimization. The study showed that an increase in temperature from 40–80°C led to a reduction in the mean size of CuNPs. A previous research has reported similar findings [71]. This could be due to possible agglomeration at lower temperatures. (Figure 4).

(a)

(b)

(c)

According to Figure 5, the predicted average particle size is 1.7862 nm. Increasing temperature yields small particle size nanoparticles. Decrease in salt concentration and pH favours synthesis of CuNPs of the least mean size. These observations agree with those of Dang et al. [62]. Previous studies [72, 73] indicated that the pH of aqueous media influences copper reduction reaction in CuNPs synthesis. Probable kinetic enhancement is thus conducive for the reduction of crystallite size because of the enhancement of the nucleation rate [62].

3.4. Characterization Results for the CuNPs

3.4.1. Particle Size Analysis

Particle size analysis was conducted for thirteen (13) samples of CuNPs prepared at varied conditions of pH of the reaction medium, copper ion concentration, and temperature of the solution. The smallest particle was for CuNPs prepared at 80°C, pH 3.0, and copper ion concentration of 0.03125 M (Figure 6).

3.4.2. X-Ray Diffraction Results

The XRD peaks were assigned in comparison with the standard powder diffraction card of the Joint Committee on Powder Diffraction Standards (JCPDS card no. 89-2838). The peak positions were consistent with metallic copper of a crystalline nature. X-ray diffraction spectrum (Figure 7) revealed diffraction peaks at 2 values of 43.30°, 50.02°, and 73.41° corresponding to the Miller indices (111), (200), and (220), respectively, which represent face-centred cubic structure of copper [66]. Further, the peak at 30.0° showed that a small amount of copper is oxidized to copper (II) oxide. The average size of CuNPs as determined using Debye–Scherrer’s formula was 6 nm, which is close to 5.55 nm as established by XRD analysis. The size of the crystal under 100 nm suggested that the nanocrystalline nature of the biosynthesized CuNPs was below 15 nm [57]. Similar results were reported by other researchers from the structure analysis of XRD for biosynthesized CuNPs [57, 58, 60].

3.4.3. Zeta Potential of the CuNPs

The zeta potential value of biosynthesized CuNPs was −69.4 mV (Figure 8). This indicated that the biosynthesized CuNPs surfaces possessed strong electrostatic repulsion hence good stability. A recent study [61] indicated that CuNPs of size 82.32 nm had a negative zeta potential of −11.9 mV. Such negative zeta potentials suggest that charge distribution of the nanoparticles as well as their sizes could play a role in promoting or enhancing their biological properties [74]. In other words, high negative zeta potential translates into strong repulsion between the particles causing amplification or enhancement of their stabilities [75].

3.4.4. FT-IR Analysis

FT-IR analysis was done to identify the functional groups of the phytochemicals that participated in synthesizing CuNPs and their stabilization. The spectrum shown in Figure 9 revealed a broadband in the range 3,400–3,500 cm⁻¹ characteristic of N-H stretch of amines and amides, and a band of the range 3400–2400 cm⁻¹ indicating the presence of O-H of carboxylic acids, alcohols, and phenols. The peak at 1,612.25 cm⁻¹ is assigned to C=C of alkenes and aromatic compounds. The presence of aromatic compounds was confirmed by the two peaks at 988.30 cm⁻¹ and 830.60 cm⁻¹ known for C-H out-of-plane bend for aromatic compounds. A peak at 1,271.13 cm⁻¹ is attributed to C-O bond of alcohols, carboxylic acids, and esters.

The presence of these functional groups indicated the possible involvement of reductive groups on the surfaces of the CuNPs [76]. They are also involved in the capping of the CuNPs, as observed in previous studies that synthesized CuNPs from plant extracts [10, 77]. The spectrum indicated new chemical linkages on the surface of CuNPs, suggesting that S. didymobotrya root extract can bind to CuNPs through hydroxyl and carbonyl groups of the amino acid residues in the protein of the extracts, therefore acting as reducing, stabilizing, and dispersing agents for synthesized copper oxide nanoparticles and preventing agglomeration of the CuNPs [60, 61].

3.5. Antibacterial Activity of S. didymobotrya Root Extract and Synthesized CuNPs

Figure 10 shows that the CuNPs had an inhibitory effect against E. coli and S. aureus. Pearson’s product-moment correlation was performed to evaluate the association between the size of CuNPs and ZOI of CuNPs against E. coli and S. aureus (n = 13). The analysis showed that there was a negative correlation between the size of CuNPs and the zone of inhibition of E. coli (r = −0.74; ). Similarly, a negative correlation was observed between the size of CuNPs and the zone of inhibition of S. aureus by the CuNPs (r = 0.74; ).

The highest zone of inhibition was 30.00 ± 0.58 mm for S. aureus and 26.00 ± 0.58 mm for E. coli was achieved for CuNPs with the least mean particle size that was synthesized at optimum conditions of 80°C, copper ion concentration of 0.03125 M, and pH 3.0. This indicated that CuNPs of least mean particle size had a high surface area to volume ratio, hence effectively binding to the microbial membrane and probably altered its permeability that could have caused growth inhibition. Sathiyavimal et al. [10] reported similar results in which 100 μL of CuNPs prepared from Sida acuta extract highly inhibited E. coli with a zone of inhibition maximum of 15 mm, showed a lower antibacterial activity against S. aureus while the lowest inhibition diameter was 11 mm against P. vulgaris. Previous authors [66, 78, 79] have reported similar results, in which E. coli was the most inhibited bacteria when compared with S. aureus and other Gram-positive bacteria.

The higher inhibition of Gram-negative bacteria by CuNPs could be partially explained by the facilitated influx of smaller-sized nanoparticles into the cell wall of Gram-negative bacteria that consists of a unique outer membrane layer and a single peptidoglycan layer as compared to the cell wall of Gram-positive bacteria with several peptidoglycan layers [80, 81]. Furthermore, CuNPs have been speculated to adhere to Gram-negative bacterial cell walls due to electrostatic interaction, or the copper ions facilitate rapid DNA degradation and reduction of bacterial respiration [82]. In some Gram-negative strains, copper ions alter the conformation and electron transferase of the associated reductases, culminating in the inhibition of cytochromes in the membrane [83].

4. Conclusion

Synthesis of CuNPs from S. didymobotrya methanolic root extract had the following optimum synthesis conditions: temperature 80°C, pH 3.0, and copper ion concentration of 0.0125 M. The mean particle size of CuNPs predicted by the design at the optimum conditions was 1.7862 nm. UV-Vis analysis showed a characteristic surface plasmon resonance peak at 571 nm indicating the formation of CuNPs. FT-IR analysis revealed that the nanoparticles were bound by carboxylic acids, amines, and amides, phenols, and esters. Particle size analysis conducted using Nanotrac particle analyzer showed that synthesized CuNPs were of the range of 5.55–63.60 nm particle size. X-ray diffraction measurement confirmed the presence of cubic face centred CuNPs. The measured zeta potential value of CuNPs was −69.4 mV indicating that they were stable. In conclusion, the biosynthesized Cu nanoparticles are stable and displayed better antimicrobial activity against E. coli and S. aureus compared to amoxicillin clavulanate (standard). The study recommends the testing of biosynthesized CuNPs against other potential multidrug resistant microbes to enable their development into antimicrobial agents.

Abbreviations

CuNPs:	Copper nanoparticles
E. coli:	Escherichia coli
S. didymobotrya:	Senna didymobotrya
SPR:	Surface plasmon resonance.

Data Availability

The datasets supporting the conclusions of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

The authors are grateful to the World Bank and the Inter-University Council of East Africa for the fellowship awarded to Bernard Otieno Sadia through the Africa Center of Excellence II in Phytochemicals, Textile and Renewable Energy at Moi University, Kenya. This study was financially supported by the Africa Center of Excellence II in Phytochemicals, Textile and Renewable Energy (ACE II PTRE) hosted at Moi University, Kenya (Credit No. 5798-KE). Timothy Omara (Department of Chemistry and Biochemistry, Moi University) is acknowledged for his tireless guidance in preparing and English proofreading of this article.

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Copyright © 2021 Bernard Otieno Sadia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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