Process optimisation for green synthesis of zero‐valent iron nanoparticles using Mentha piperita

Abstract

The potential of Mentha piperita in the iron nanoparticles (FeNPs) production was evaluated for the first time. The influences of the variables such as incubation time, temperature, and volume ratio of the extract to metal ions on the nanoparticle size were investigated using central composite design. The appearance of SPR bands at 284 nm in UV–Vis spectra of the mixtures verified the nanoparticle formation. Incubating the aqueous extract and metal precursor with 1.5 volume ratio at 50°C for 30 min leads to the formation of the smallest nanoparticles with the narrowest size distribution. At the optimal condition, the nanoparticles were found to be within the range of 35–50 nm. Experimental measurements of the average nanoparticle size were fitted well to the polynomial model satisfactory with R ² of 0.9078. Among all model terms, the linear term of temperature, the quadratic terms of temperature, and mixing volume ratio have the significant effects on the nanoparticle average size. FeNPs produced at the optimal condition were characterised by transmission electron microscopy, thermogravimetry analysis (TGA), and Fourier‐transform infrared spectroscopy. The observed weight loss in the TGA curve confirms the encapsulation of FeNPs by the biomolecules of the extract which were dissociated by heat.

1 Introduction

Iron nanoparticles have been investigated extensively because of their applications in sensors, catalysts, magnetic recording and electronic devices, biomedicine, and removal of pollutants [1, 2]. There are several manufacturing processes for the production of iron nanoparticles (FeNPs) including chemical and mechanical techniques [3]. Unfortunately, majority of these methods are not economically viable, or the product tends to be very polydisperse in size and irregular in shape. Furthermore, the production of FeNPs is accosted with threats to the environment and humans’ health due to the usage or coproduction of toxic chemicals [4, 5, 6, 7]. Therefore, more environmental friendly and economically feasible techniques are required to produce FeNPs.

Green chemistry methods which focus on using biological entities such as bacteria, fungi, and plants for the FeNPs synthesis have been introduced in the literature recently. The plant‐mediated approaches are simple, non‐toxic, cost‐effective, and eco‐friendly [8, 9]. Phytochemical constituents of the plant extracts such as flavonoids, polyphenols, and acids may simultaneously have the functionality of the reducing and stabilising agents [10, 11, 12]. According to the literature, the FeNPs synthesis has been performed using various plants such as eucalyptus [4], Citrus maxima [13], green and black tea [14, 15], Salvia officinalis [16], and Syzygium aromaticum [17].

Mentha piperita (Peppermint) which belongs to Lamiaceae family and contains rich content of polyphenol compounds seems an attractive target plant for the production of FeNPs. Gallic acid, ellagic acid, and sinapic acid are the main phenolic compounds of M. piperita extract. Also, M. piperita extract contains a significant of flavonoids as a major group of polyphenols. The presence of hydroxyl groups in flavonoids represents them as a potent antioxidant. The main flavonoid compounds of M. piperita are identified as quercetin, routine, naringin, and hesperetin. These compounds contribute to the medical applications of M. piperita as antimicrobial and antioxidant agents [18, 19]. The strong performance of M. piperita extract as an antioxidant stems from the availability of gallic acid (phenolic compound) and quercetin (flavonoid compound) [20].

The current work tries to evaluate the performance of M. piperita leaf extract for FeNP production. Response surface methodology (RSM) was implemented to optimise the nanoparticle generation by considering three independent variables including the incubation time, temperature, and extract:metal ion ratio. Scanning electron microscopy (SEM) was used to investigate the influence of these three variables on nanoparticle's size and size distribution as response variables under 15 designed run experiments. The synthesised nanoparticles at the optimal condition (that verified by SEM) were further analysed using transmission electron microscopy (TEM), thermogravimetry analysis (TGA), and Fourier‐transform infrared spectroscopy (FTIR).

2 Materials and methods

2.1 Materials

The required chemicals used in this study are listed as follows: ferric chloride (FeCl₃), methanol, ethanol, chloroform, n‐hexane, dimethyl sulphoxide (DMSO), sodium carbonate (Na₂ CO₃), magnesium chloride (MgCl₂), calcium chloride (CaCl₂), potassium chloride (KCl), hydrochloric acid (HCl), sulphuric acid (H₂ SO₄), potassium ferricyanide (C6N6FeK3), Folin–Ciocalteu reagent; all these analytical‐grade compounds have been manufactured by Merck. Additionally, the following chemicals manufactured by Sigma‐Aldrich have been also used: linoleic acid (C18H32O2), 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH). Finally, gallic and tannic acids were obtained from Acros.

2.2 Preparation of plant extracts for phytochemical screening and FeNP formation

Fresh leaves of M. piperita were collected from Iranian Institute of Medicinal Plants (IMP, Karaj, Iran); then the leaves were washed extensively with water to remove dust particles; and finally, they were kept in shade for 48 h to be dried. Nineteen grams of dried powder of M. piperita were weighed carefully and then transferred to percolator apparatus. During the 48 h of extraction, 190 ml of distilled water was used as a solvent and was added after 24 h intervals. The resultant solution was concentrated to 4–5 ml via rotary evaporator under 50°C and reduced pressure. The concentrated extract was transferred to a Petri dish and dried in an oven at 50°C for 24 h; then, the solution was kept in a vacuum oven. Finally, the extract was distributed into tight vials to prevent the degradation of the active chemicals; the vials were maintained at 4°C for quantification of the phytochemicals. The following equation represents the extraction yield calculation procedure:

$$\begin{array}{rl}& Y\left(\mathrm{Extraction}\mathrm{yield}\right)\\ & =(\mathrm{dried}\mathrm{weight}\mathrm{of}\mathrm{resultant}\mathrm{extract}/\mathrm{Initial}\mathrm{dried}\mathrm{weight}\mathrm{of}\mathrm{plant})\times 100\end{array}$$ (1)

For the nanoparticle synthesis, the extract obtained from percolator was filtered using a Whatman filter paper No. 1 and then centrifuged at 6000 rpm for 10 min. This aqueous extract was used for the FeNP synthesis.

2.3 Phytochemical screening

2.3.1 Determination of total polyphenolic content of plant extract

Folin–Ciocalteu reagent was used for measuring the total phenols of M. piperita. At first, 10 mg of plant extract powder was dissolved in 2 ml of DMSO. About 200 µl of the extract was mixed with 45 ml of distilled water and reacted with 1 ml of folin reagent. Then, the solution was vortexed for 3 min followed by addition of 3 ml of prepared sodium carbonate (2%) (three replicates). The final volumes of the samples were set to 50 ml. For the preparation of the blank samples (control), instead of extract, 0.2 ml of DMSO was added. The prepared samples were kept at room temperature for 2 h, and then their absorbance intensities were recorded at 760 nm. The standard curve for the calculation of total phenolic content was plotted using gallic acid (1.1–11 mg/ml), and the results were expressed as mg of gallic acid equivalent per 1 g of dry weight of M. piperita extract.

2.3.2 Determination of the flavonoid content of plant extract

Total flavonoid content of the plant extract was measured based on colorimetric aluminium chloride method. The stock solution was prepared by addition of the methanol solution to 10 mg of the plant extract (until reaching the final volume of 10 ml). Then, 500 µl of the stock solution was mixed with 1.5 ml of methanol followed by addition of sodium acetate (100 µl). Finally, 100 µl of aluminium chloride was added (final volume of 5 ml), and the samples were kept at room temperature for 30 min. Blank solution was prepared by addition of 500 µl of methanol instead of extract solution. The absorbance intensities of all samples were recorded at 415 nm. Samples were prepared at three replicates. For quantification of the flavonoid content, quercetin dehydrate was used as a standard.

2.3.3 Determination of the tannins content of plant extract

First, methanolic extract with concentrations of 500, 1000, and 2000 ppm was prepared. About 1 ml of an aliquot of diluted sample or standard solution was added to 5 ml of distilled water and 0.5 ml of Folin–Ciocalteu reagent. Then, the mixture was shaken for 3 min before the addition of 1 ml of 20% Na₂ CO₃. Finally, the solution was adjusted with distilled water to a final volume of 10 ml and mixed thoroughly. Blank solution was prepared by adding 1 ml of distilled water instead of extract. After incubation of samples for 1 h, absorbance intensities were recorded at 725 nm. The number of tannins was expressed as mg of tannic acid equivalent per 1 g of dry weight of M. piperita extract. All samples were analysed in triplicate.

2.4 Green synthesis of FeNPs

Ferric chloride hexahydrate (FeCl₃ ·6H₂ O) was used as a precursor for the nanoparticle synthesis. FeNPs were synthesised by adding the prepared extract to 0.01 M of FeCl₃ ·6H₂ O under the different operational conditions mentioned in Table 1 . After the reaction completion, the produced nanoparticles were collected using centrifugation at 6000 rpm for 10 min followed by repeated washings using double distilled water and ethanol, respectively. Then the concentrated nanoparticles were dried in vacuum oven for 24 h and then preserved in a desiccator to inhibit the side effects of oxygen or moisture on the synthesised nanoparticles.

Table 1.

Experimental tests matrix generated by three‐level CCD of the three independent variables in terms of coded units

Run experiments	X 1 (time)	X 2 (temperature)	X 3 (mixing volume ratio)
1	45	50	3.0
2	30	50	4.5
3	45	50	3.0
4	60	50	1.5
5	45	35	1.5
6	45	35	4.5
7	30	35	3.0
8	30	50	1.5
9	45	65	4.5
10	30	65	3.0
11	60	50	4.5
12	60	65	3.0
13	60	35	3.0
14	45	50	3.0
15	45	65	1.5

2.5 Design of experimental conditions

To optimise the synthesis process and find the most influential factors on the average size of the produced nanoparticles, we performed a sensitivity analysis on the contributing parameters. For this purpose, three factors at three levels were considered. Variables were investigated via central composite design (CCD) in the response surface optimisation method. Table 2 summarises the selected factors and their levels.

Table 2.

Selected factors and their coded levels

Symbols	Factors	Coded levels
		−1	0	+1
X 1	time, min	30	45	60
X 2	temperature, °C	35	50	65
X 3	mixing volume ratio, v/v	1.5	3.0	4.5

According to CCD, a set of 15 experimental trials was generated using the statistical software MINITAB version 16.0 (Table 1). In this study, the experimental procedure consists of 15 runs, and the independent variables are studied at three levels: low (−1), medium (0), and high (+1). To correlate the average size of the nanoparticles to the operational parameters, the quadratic polynomial model, as shown below, can be of help

$$Y={\beta }_0+\sum _{i=1}^4{\beta }_i{X}_i+\sum _{i=1}^4{\beta }_{ii}{X}_i^2+\sum _{i=1}^4\sum _{j>1}^4{\beta }_{ij}{X}_i{X}_j$$ (2)

where Y represents the predicted response variable, ${\beta }_0$ is the model intercept; ${\beta }_i$, ${\beta }_{ii}$, and ${\beta }_{ij}$ are the regression coefficients of linear, square, and interaction terms, respectively. $X_i$ and $X_j$ indicate the coded independent variables. The second‐order polynomial coefficients were calculated and analysed using the MINITAB statistical software (version 16.0). The data obtained from CCD was assessed using regression analysis as well as analysis of variance (ANOVA).

2.6 Characterisation of FeNPs

2.6.1 UV–Vis spectroscopy

The colour changes of the reaction mixtures are the first evidence for M. piperita capped FeNP formation. All samples were withdrawn at various intervals, diluted with distilled water100 folds, and then their absorbance was measured by a UV–Vis spectrophotometer (X‐ma 2000, Varian, Human Corporation, USA) at a resolution of 1 nm in the range 200–900 nm.

2.6.2 Scanning electron microscopy

The morphological assessment of the synthesised Fe nanoparticles was conducted on SEM (EM3200, KYKY, China) operating at 26 kV with a magnification of 40,000×. For the SEM analysis, the purified synthesised FeNPs were located on the SEM holder, and then gold coated using sputter coated.

2.6.3 Transmission electron microscopy

The morphological investigation of the produced nanoparticles was conducted using a transmission electron microscope (EM10C‐100 KV, Zeiss, Germany) operating at 100 kV. TEM samples were prepared using the drop‐casting of the colloidal FeNPs on the carbon‐coated copper grids and were allowed to dry at room temperature.

2.6.4 Fourier‐transform infrared spectroscopy

To achieve a deeper insight into the mechanism of FeNP formation, the presence of functional groups on the surface of nanoparticles was investigated by FTIR (Magna‐IR™ Spectrometer 550, Nicolet, USA). For sample preparation, the purified nanoparticles were mixed with KBr powder and pressed into a pellet. The FTIR spectra were recorded in the 500–4000 cm⁻¹ range with a resolution of 4 cm⁻¹.

2.6.5 Thermogravimetric analysis

To characterise the structure of capping agents on the surface of Fe, thermal analysis of the green synthesised FeNPs was studied using a TGA (STA 503, BAHR, Germany). The samples were weighed and heated from 34 to 800°C at a heating rate of 10°C/min in a nitrogen atmosphere.

2.6.6 X‐ray diffraction (XRD) analysis

The crystalline or amorphous nature of synthesised FeNPs was investigated using thePW‐1730 Philips instrument. Samples were prepared by casting a FeNP powder on a silicon substrate followed by an exposure to X‐ray. The pattern was recorded between 2θ angles of 10–80°.

3 Results and discussions

As mentioned above, the dried extract of M. piperita was prepared using percolator by submerging 19 g of dried powder of plant leaves in 190 ml of distilled water. The yield of extraction was 17.45 (%w/w).

3.1 Optimisation of the FeNP formation by CCD

Three operational parameters of incubation time, temperature, and mixing volume ratio of extract and precursor (FeCl₃ ·6H₂ O) were selected based on their effects on the size distribution of the nanoparticles. All the 15 cases summarised in Table 1 were performed for the nanoparticle synthesis to optimise the three selected independent variables by CCD design. Figs. 1, 2, 3, 4 show the SEM images (scale bar: 1 µm) of the produced nanoparticles under these operational conditions. The average size of the nanoparticles in each experiment was obtained from the corresponding SEM image using the ImageJ software.

Fig. 1.

SEM micrographs of FeNPs synthesised using M. piperita for the tests 1–4 of Table 1 created by CCD

Fig. 2.

SEM micrographs of FeNPs synthesised using M. piperita for the tests 5–8 of Table 1 created by CCD

Fig. 3.

SEM micrographs of FeNPs synthesised using M. piperita for the tests 9–12 of Table 1 created by CCD

Fig. 4.

SEM micrographs of FeNPs synthesised using M. piperita for the tests 13–15 of Table 1 created by CCD

The effects of operating parameters such as time (X ₁), temperature (X ₂), and mixing volume ratio of the extract to the metal precursor (X ₃) on the average size of the nanoparticles were investigated using RSM with considering the CCD. Table 3 depicts the size distribution and the average size of the nanoparticles for the 15 tests listed in Table 1.

Table 3.

Size distributions of the nanoparticles synthesised using M. piperita under experiments designed by CCD

Run number	Factors			Size distribution of FeNPs, nm	Average size, nm
	X 1	X 2	X 3
1	45	50	3.0	60–80	72.1
2	30	50	4.5	60–70	64.2
3	45	50	3.0	80–90	86.0
4	60	50	1.5	80–90	81.4
5	45	35	1.5	90–100	95.2
6	45	35	4.5	60–70	65.4
7	30	35	3.0	80–100	88.3
8	30	50	1.5	35–50	40.0
9	45	65	4.5	70–80	76.3
10	30	65	3.0	70–80	76.0
11	60	50	4.5	80–90	86.5
12	60	65	3.0	120–140	135.0
13	60	35	3.0	120–130	120.0
14	45	50	3.0	80–90	86.0
15	45	65	1.5	70–80	74.7

The quadratic polynomial model can represent the experimental results to a reasonably good extent. The following expression shows the model equation:

$$\begin{array}{rl}Y& =283.777-2.310X_1-9.887X_2+45.886X_3\\ & +0.030X_1^2+0.074X_2^2-8.948X_3^2\\ & +0.030X_1{X}_2-0.212X_1{X}_3+0.349X_2{X}_3\end{array}$$ (3)

The ANOVA for the above predicted quadratic model was conducted and the results are summarised in Table 4. As shown in the table, high F ‐value of 5.47 and low P ‐value (P  < 0.05) of the model prove that the predicted model is statistically significant. Also, ‘lack of fit’ with F ‐value of 2.71 and P ‐value of 0.281 suggest that it is not significant. Non‐significant ‘lack of fit’ indicates the accuracy of the model. The coefficient of determination (R ²) is another criterion for checking the fitness of the model. Higher R ² values (close to 1) indicate the better correlation between the experimental and the predicted results. R ² of this model was evaluated as 0.9078 that reveals the relatively good correlation between the experimental data and the predicted values by the model.

Table 4.

ANOVA for the fitted second‐order polynomial model determined from CCD

Source	Degrees of freedom	Sum of squares	Adjusted sum of squares	Adjusted mean squares	F ‐value	P ‐value
regression	9	6424.11	6424.11	713.79	5.47	0.038
linear	3	2986.02	2335.87	778.62	5.97	0.042
X 1	1	2979.92	87.94	87.94	0.67	0.449
X 2	1	5.95	1424.08	1424.08	10.92	0.021
X 3	1	0.15	499.41	499.41	3.83	0.108
square	3	2914.07	2914.07	971.36	7.45	0.027
X 1 2	1	258.37	170.31	170.31	1.31	0.305
X 2 2	1	1159.02	1025.64	1025.64	7.86	0.038
X 3 2	1	1496.68	1496.68	1496.68	11.48	0.020
interaction	3	524.01	524.01	174.67	1.34	0.361
X 1  × X 2	1	186.32	186.32	246.49	1.43	0.286
X 1  × X 3	1	91.20	91.20	186.32	0.70	0.441
X 2  × X 3	1	246.49	246.49	91.20	1.43	0.228
residual error	5	652.12	652.12	130.42	—	—
lack‐of‐fit	3	523.32	523.32	174.44	2.71	0.281
pure error	2	128.81	128.81	64.40	—	—
total	14	7076.24	—	—	—	—
R 2	0.9078	—	—	—	—	—
R 2 ‐adjusted	0.7420	—	—	—	—	—

It is believed that P ‐value <0.05 provides insights into the significance of the model terms in (3). As indicated in Table 4, the linear term of temperature (X ₂, P  < 0.05), the quadratic terms of temperature (X ₁₂, P  < 0.05), and the mixing volume ratio (X ₃₂, P  < 0.05) show noticeable effects on the nanoparticle average size. Contrarily, all the other terms containing two linear terms (X ₁, X ₃), one quadratic term (X ₂ ²), and all the interaction terms (X ₁ X ₂, X ₁ X ₃, X ₂ X ₃) show non‐significant effects.

The model terms which have higher F ‐value and lower P ‐value tend to be more effective on the response variable. Among all significant terms, F ‐value of the quadratic terms (F ‐value of 7.45) is higher than the linear term which indicates the greater influence of quadratic terms of time and mixing volume ratio on the response variable (Table 4). After elimination of the non‐significant model terms, the equation model can be rearranged considering the significant terms as follows:

$$Y=283.777-9.877X_2+0.074X_2^2-8.948X_3^2$$ (4)

The positive and negative values of the model coefficients are indicatives of synergistic and antagonistic effects on the response, respectively. As indicated in (4), the linear term of temperature exhibits a higher effect in comparison with the other parameters.

The optimum values of temperature (X ₂) and mixing volume ratio (X ₃) for providing the minimum value of the response (Y) were calculated by simultaneous solving of two first‐order differential equations. Optimum values using MINITAB software were predicted as the temperature of 57.0 and mixing volume ratio of 1.5. The corresponding time for these optimum values was 30 min. Minimum average size of the FeNPs under predicted optimum conditions was calculated as 40.19 nm. These conditions are in accordance with the test number 8.

3D response surface and 2D contour plots are the graphical representation of the derived regression model that were used to study the mutual interactions of the independent variables and identify the optimum values of variables for obtaining the minimum FeNP average size. Figs. 5 and 6 illustrate the binary interactive effects of the independent variables of time, temperature, and mixing volume ratio of the extract to precursor on the nanoparticle size using 3D and 2D response surface plots, respectively. The third independent variable in each plot was kept constant at related zero level.

Fig. 5.

3D response surface and their related 2D contour plots for nanoparticle size as a function of

(a) Time and temperature, (b) Temperature and mixing volume ratio of M. piperita extract to the precursor

Fig. 6.

3D response surface and their related 2D contour plots for nanoparticle size as a function of time and mixing volume ratio of M. piperita extract to precursor and time

The interactive effects of time and temperature on the average nanoparticle size are shown in Fig. 5 a, while the mixing volume ratio of extract to precursor was kept constant at 3 (zero level). According to these plots, the time exhibits the most noticeable influence on the particle size compared to the temperature. According to Fig. 5 a, increasing the temperature from 35 to 57°C leads to the reduction of average nanoparticle size, while the further increase in temperature results in the nanoparticle size increase. The higher temperature causes the acceleration of the reaction as a result of the effective molecular collision. As the reaction rate enhances, the reactant depletion occurs faster which leads to the formation of smaller nanoparticles. In contrast, the further increase in temperature promotes the aggregation of the nanoparticles without efficient stabilisation.

It was found that the mechanism of nanoparticle formation contains three main steps of nucleation, growth, and stabilisation [21]. Nucleation and growth rates can be induced by increasing the temperature. Increasing the number of nuclei followed by the faster consumption of the reactants at high temperature results in the formation of smaller nanoparticles. Further increase of the temperature causes an excessive rate of growth which favours the aggregate formation. This aggregation arises from slower capping rate of the nanoparticles by stabilising agent compared to nuclei formation.

Increasing the reaction time causes the particle size growth. Results show that the nanoparticle size shifts from 75–100 to 100–125 nm when the time increases from 30–50 to 50–60 min. This may be attributed to the aggregation and coalescence of the nanoparticles due to the longer residence time. As indicated in Fig. 5 a, the simultaneous increment in time and temperature was not desirable because of the formation of larger nanoparticles.

Fig. 5 b illustrates the simultaneous effects of the temperature and extract to precursor volume ratio on the average nanoparticle size. The mixing volume ratio shows both positive and negative effects on the particle size. Particles within the range of 60–75 nm were produced at volume ratio of 1.5. Particle size range considerably increases from 60–75 to 90–105 nm by the enhancement of the mixing volume ratio until 3 and then decreases gradually to 80–90 nm with the further increase of the volume ratio to 4.5. With respect to the utilisation of the three‐valent iron salt with a concentration of 0.01 M as a precursor, it seems that the volume ratio of the extract to iron salt solution should be higher than 1 until the oxidation occurs completely. As the volume ratio increases, the amount of extract (reducing and capping agents) increases in comparison with the amount of the metal salt (precursor). The excessive amount of extract after complete reduction of metal ions may attach to the nanoparticles and cause the formation of larger nanoparticles. As indicated in contour plot (Fig. 5 b), the simultaneous increase of the time and volume ratio do not show considerable effects on the nanoparticle size. Response surface and contour plots of the nanoparticle size as a function of volume ratio and time (Fig. 6) imply that the simultaneous increase of these two factors from their low levels of −1 (30 min and volume ratio of 1.5) promotes the production of nanoparticles with the larger size.

3.2 Characterisation of the synthesised FeNPs under the optimal conditions

3.2.1 UV–Vis spectroscopy

Colour change of the reaction mixture containing M. piperita extract and iron salt to dark brownish is the preliminary evidence of FeNP formation. The progress of nanoparticle formation in the reaction solution was investigated by means of UV–Vis spectroscopy. The UV–Vis spectrum of the solution after the reaction completion is presented in Fig. 7. As shown in this figure, two absorption peaks can be observed at wavelengths of 216 and 284 nm. These peaks are centred at the reported ideal wavelength for the FeNP formation [22].

Fig. 7.

UV–Vis spectrum of the colloidal solution of FeNPs in reaction mixtures containing M. piperita leaf extract and iron salt with the volume ratio of 1.5 under 60°C after 30 min

3.2.2 TEM analysis

The TEM image of the generated FeNPs is shown in Fig. 8 a. The particle sizes range from 35 to 50 nm which is consistent with the size distribution obtained from the SEM analysis. It can be seen that the produced nanoparticles have an experimental shape. Moreover, TEM micrograph (Fig. 8 b) shows some degrees agglomerations due to the high surface energy of the nanoparticles.

Fig. 8.

TEM image of the colloidal Fe NPs synthesised using M. piperita in the optimal condition (extract to iron salt volume ratio of 1.5, incubation time of 30 min, and temperature of 60°C)

3.2.3 TGA analysis

Thermal stability of the green‐synthesised FeNPs generated from M. piperita extract was investigated by TG analysis (Fig. 9). As revealed in Fig. 9, the initial weight increasing is presumably because of the intense oxidation of the FeNP and formation of iron oxide nanoparticles. The nanoparticle degradation begins at around 90°C and continues until 800°C. The continuous weight loss occurs due to the surface desorption of the bio‐organic compounds which are available around the nanoparticles and act as capping agents. The available bio‐organic compounds the plant extract decomposes completely with temperature increment until 800°C. Thus, TGA confirms that M. piperita leaf extract exists on the surface of FeNPs. The similar results have been observed in the previous studies [23].

Fig. 9.

TGA analysis of the green synthesised FeNPs by M. piperita at optimal condition (extract to iron salt volume ratio of 1.5, incubation time of 30 min, and temperature of 60°C)

3.2.4 XRD analysis

The phase of the synthesised FeNPs was assessed by X‐ray technique. As shown in Fig. 10, no diffraction peaks are detected even at 2θ value of 44.9° which is the characteristic peak of the zero‐valent iron. This finding approves the amorphous nature of the green synthesised FeNPs and is consistent with the results of the previous studies. The appearance of a broadband at 2θ value of 28° may be due to the presence of wide ranges of biomolecules in the plant extract which act as reducing and capping agents [24, 25].

Fig. 10.

XRD pattern of synthesised FeNPs using M. piperita leaf extract under optimal condition (extract to iron salt volume ratio of 1.5, incubation time of 30 min, and temperature of 60°C)

3.3 Mechanism study of FeNP formation

3.3.1 Identification of phytochemicals

Test results verify that phenols, flavonoids, and tannins were present in the M. piperita leaf extract. The contents of the phenols, flavonoids, and tannins in M. piperita extract are listed in Table 5. M. piperita contains a high phenolic content (49.25 mg/g). The high amount of phenols, flavonoids, and tannins are evidence of the high antioxidant activity of the plant extract. This is due to the presence of hydroxyl groups in the structure of these phenolic compounds which give them the free radical scavenging activity [26]. These compounds with significant anti‐oxidant activity have the key role in reducing the Fe³⁺ ions followed by formation of the Fe nanoparticles.

Table 5.

Contents of phenols, flavonoids, and tannins present in the M. piperita leaf extract

Compound	Content, mg/g
total phenols	49.25
flavonoids	14.20
tannins	234.06

3.3.2 FTIR analysis

FTIR was used to identify the possible biomolecules in the plant extract which are responsible for reducing the Fe ions and acting as capping agents. FTIR spectra of the aqueous extract of M. piperita before and the synthesised FeNPs are presented in Fig. 11. The spectra of M. piperita extract in Fig. 11 b shows the absorption bands that appear at around 1072, 1267, 1411, 1627, 2925, and 3416 cm⁻¹. The band at 1072 cm⁻¹ can be related to the C–N stretching vibration of aliphatic amines [27]. The band at 1411 cm⁻¹ can be assigned to C = O stretching vibration which might arise from the functional groups of ketones, aldehydes, and carboxylic acids. The bands observed at 1627 cm⁻¹ can be attributed to the C = C stretching vibrations of the aromatic ring which belongs to the phenol compounds (e.g. flavonoids and polyphenols) [8]. Absorption band associated with the C–H and stretching vibration of aliphatic hydrocarbon chains appears at 2925 cm⁻¹ [4, 22]. The broad bands around 3416 represent the O–H stretching of the phenolic compounds which is an indication of the strong hydrogen bonding [28]. These functional groups prove the presence of phenols, aliphatic amines, and organic acids in the extracts which might act as reducing and stabilising agents in the FeNP synthesis.

Fig. 11.

FTIR spectra of the aqueous extract of M. piperita

(a) After synthesis of FeNP at the optimal condition, (b) Before adding Fe³⁺ cations

The FTIR spectra reveal that the observed bands for functionalised FeNPs (Fig. 11 a) are similar to those obtained for M. piperita extract (Fig. 11 b) with a slight shift.

As far as IR spectrum is concerned, there is no considerable change in the molecular bonds of functional groups from the extract compounds. Appearance of the absorption band around 450 cm⁻¹ in the FTIR spectrum of FeNPs (Fig. 11 a) is attributable to the small quantities of Fe–O from dissolving Fe₃ O₄ and Fe₂ O₃. This can be related to the formation of FeNPs that are partially oxidised due to the exposure to air or water [27]. The Fe–O stretching vibration band of the bulk magnetite usually appears at −570 cm⁻¹, and the band shifts to the higher wave numbers (582.82 cm⁻¹, Fig. 11 a) because of the finite size of the nanoparticles production [29].

4 Conclusion

In this study, the zero‐valent FeNPs were synthesised successfully using M. piperita plant extract. Optimisation results based on RSM technique revealed that the size distribution and average size of the green synthesised FeNPs could be controlled by the physical parameters such as time, temperature, and mixing volume ratio of the extract to the metal ions. A quadratic polynomial model for the prediction of the average nanoparticle size was derived as a function of time, temperature, and mixing volume ratio. FTIR analysis proved that the biomolecules in the plant extract bound onto the surface of the nanoparticles via their functional groups and stabilised the FeNPs.