Effect of fuel content on nonlinear optical and antibacterial activities of Zn/Cu/Al₂O₄ nanoparticles prepared by microwave-assisted combustion method

2.1 Materials

Starting metal precursors which are used in this experiment are Zinc nitrate hexahydrate (Zn (NO₃)₂·6H₂O), Copper nitrate hexahydrate (Cu(NO₃)₂·6H₂O), and Aluminum nitrate monohydrate (Al (NO₃)₃·9H₂O) which act as oxidizer and glycine (NH₃ COOH CH₂) as a fuel.

2.2 Methods

The Zn_0.96Cu_0.04Al₂O₄ nanoparticles were prepared via microwave-assisted combustion method with various fuel to oxidizer ratio such as, stoichiometric (S), fuel lean (L), and rich (R). The constituents in the desired fuel to oxidizer ratio in stoichiometric conditions were suspended in double distilled water (15 mL). The mixture was vigorously stirred at 90 °C for 30 mins until it forms a highly viscous gel, then it was kept in a microwave oven with the output power of 800 Watts. In the early stage, the gel was effervescence and burst into flames with enormous amount of gases which result in yielding of foamy and porous structure. Finally, the yield was collected and calcination was done at 700 °C for 6 h. The same experimental procedure was continued for the remaining fuel to oxidizer ratios like fuel lean and rich.

2.3 Combustion mechanism

Bipropellant chemistry, microwave-assisted combustion synthesis relies on an exothermic redox reaction between glycine and metal nitrate. In this combustion process, metal nitrates like Zn, Cu and Al nitrate function as oxidizers, whereas glycine functions as a reducer. Typically, valency for the elements are Zinc (+2), Copper (+2), Aluminum (+3), Hydrogen (+1), Carbon (+4) and Oxygen (-2). Generally, element nitrogen is assumed to be zero. Hence, equivalent capacity are Zinc nitrate (−10), Copper nitrate (−10), Aluminum nitrate (−15) and glycine (+9). For a stoichiometric ratio, we need 0.96 (−10) + 0.04 (−10) + 2 (−15) + n (+9) = 0, which provides n = 4.44 mol of glycine. Jain et al. (1981) have calculated the nitrate to glycine ratio which is represented by $({\varnothing }_e)$ using the relation,

The coefficients of reducing element for the oxidizer to fuel ratios are $({\varnothing }_{\mathrm{e}}>1)$ for fuel lean, ${\varnothing }_{\mathrm{e}}<1)$ for fuel rich and $({\varnothing }_{\mathrm{e}}=1)$ for stoichiometric. The combustion reaction to prepare Zn_0.96Cu_0.04Al₂O₄ reaction is expressed as follows.

1. Fuel lean ( ${\varnothing }_e$  = 0.5, Fuel/Nitrate ratio = 0.74)

It shows that more amount of oxygen released to the surrounding environment.

2. Fuel stoichiometric ( ${\varnothing }_e$  = 1, Fuel/Nitrate ratio = 1.48)

From the reaction, it is very clear that oxidizing fuel reacts with the oxygen from metal nitrate completely and for the complete reaction, no heat exchange is generally required.

3. Fuel rich ( ${\varnothing }_e$  = 1.5, Fuel/Nitrate ratio = 2.22)

It is very clear, to finish the combustion process, further oxygen is needed from the atmosphere.

2.4 Instrumentation

The PXRD analysis was performed to characterize the synthesized nanoparticles using XPERTPRO X-ray powder diffractometer system. The 2θ range analyzed between 10°-80°, the scan speed was set as 0.1 min⁻¹ The morphologies were observed by SEM (Make: CAREL ZEISS, Model: EVO 18). FTIR spectra were analyzed using a Perkin–Elmer spectrophotometer in the range of 400–4000 cm⁻¹ at room temperature. The linear optical absorption and electronic transition of the nanoparticles were investigated using a UV–Visible spectrophotometer (scan range: 200 to 800 nm). The luminescence spectra was recorded in the spectrofluorophotometer (shimadzu/RF 6000).

2.5 Z-Scan measurement

In this experiment, Zn_0.96Cu_0.04Al₂O₄ nanoparticles were investigated for third order nonlinear optical properties using CW laser (532 nm, 100 mW) as excitation source. Initially, the prepared Zn_0.96Cu_0.04Al₂O₄ nanoparticles were dispersed in ethylene glycol by ultra-sonication with transmittance nearly 65%. The dispersed solution was taken in 1 mm cuvette and was mounted on a translation stage that was slowly shifted into the laser propagation pathway. The corresponding far field transmittance was observed by a photodetector.

2.6 Antibacterial activity

Nanoparticles were subjected for antimicrobial activity studies using disc diffusion method (Vijayalakshmi and Dhanasekaran, 2018) against various bacterial pathogens on Mueller Hinton agar plate. The medium that supports the growth inhibition of bacterial cultures were maintained at 37 °C using an incubator. The sterilized petri dish that was used in the experiment was inhibited at 120 °C for 30 mins. The synthesized nanomaterial was dispersed in deionized water. The dispersed solution was loaded on sterile dishes on Mueller Hinton agar plate seeded with bacteria. The petri plates were incubated for 18 h. After 18 h, the results were observed and the diameter of the zone was measured (mm).

3.1 PXRD analysis

The XRD patterns obtained for the Zn_0.96Cu_0.04Al₂O₄ nanoparticles are described in Fig. 1 (a, b, c). It is clear that the diffraction peaks corresponding to the crystallography planes of (2 2 0), (3 1 1), (4 0 0), (3 3 1), (4 2 2), (5 1 1), (4 4 0), (6 2 0), and (5 3 3) are in accordance with JCPDS data (Card No:73-1961). These characters confirmed the presence of cubic spinel structure. However, PXRD pattern did not show any secondary phase diffraction peaks. Debye-Scherrer’s formula was used for the determination of the mean crystallite size (D) as suggested by Pathak et al. (2016).

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Fig. 1

The synthesis Zn_0.96Cu_0.04Al₂O₄ nanoparticles for different fuel ratios (a) Fuel lean, (b) Fuel stoichiometric (c) Fuel rich: (1) XRD pattern.

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3.1.1

3.1.1 Williamson - Hall analysis

Williamson-Hall (W-H) equation, to calculate the lattice strain is,

(7)

$$\beta \mathrm{c}\mathrm{o}\mathrm{s}\theta =4\epsilon \mathrm{s}\mathrm{i}\mathrm{n}\theta +\mathrm{K}\lambda /\mathrm{D}$$ where, ε denotes the lattice strain of materials. Fig. 2 (a, b, c) exhibits the plot drawn for 4sinθVs βcosθ. The calculated lattice strain values are −4.80 (L), 1.17 (S) and 5.66 (R). As fuel to oxidizer ratio increases, the lattice strain values changes from negative to positive strain (Fig. 2). The negative strain value indicates the presence of compressive strain and positive values specify tensile strain in the material. The equation $\delta =1/D^2$ was used to calculate dislocation density (Aly et al., 2016) and values for fuel lean, stoichiometric and rich are 1.6000 × 10¹⁵, 1.1111 × 10¹⁵ and9.7656 × 10¹⁴ (Lines/m²).

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Fig. 2

The synthesis Zn_0.96Cu_0.04Al₂O₄ nanoparticles for different fuel ratios (a) Fuel lean, (b) Fuel stoichiometric (c) Fuel rich: (2) W-H plots.

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3.2 Morphological studies

The morphology of the nanoparticles are analyzed using SEM and are shown in Fig. 3 (a, b, c). The nanoparticles are accompanied by agglomeration and coalescence. In Fig. 3a, it is seen that cubic structure is formed with some nanoparticles deposited on the structure. In Fig. 3b, cubic spinel structure is obtained with some agglomeration. Fig. 3cshows the spongy network structureformed in the nanoparticles. SEM images, reveals slight morphological difference in different size agglomerates covered by smaller particlesdue to selection of fuel.

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Fig. 3

The synthesis Zn_0.96Cu_0.04Al₂O₄ nanoparticles for different fuel ratios (a) Fuel lean, (b) Fuel stoichiometric (c) Fuel rich: (3) SEM images.

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3.3 Compositional analysis

EDAX was carried out to confirm the elemental composition in the synthesized nanoparticles for fuel (L, S & R) is depicted in the Fig. 4 (a, b, c). The corresponding peaks of the elements O, Al, Zn and Cu were determined for the synthesized nanoparticles. The elements are well distributed and without any characteristic impurity peak indicating the purity of prepared materials. The content of Zn, Cu, Al and O differs with fuel to oxidizer ratio. Moreover, the atomic ratio significantly decreases with increase in the fuel to oxidizer ratio.

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Fig. 4

The synthesis Zn_0.96Cu_0.04Al₂O₄ nanoparticles for different fuel ratios (a) Fuel lean, (b) Fuel stoichiometric (c) Fuel rich: (4) EDAX spectra.

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3.4 FTIR analysis

The chemical bonding and functional groups of synthesized nanoparticles were identified by FTIR spectrum. The infrared spectra of the prepared sample are depicted in Fig. 5 (a, b, c). The absorption band at 3443 cm⁻¹ and 1633 cm⁻¹ could be assigned to O–H stretching vibration due to water (H₂O) adsorption (Theophil Anand et al., 2019). A band observed at 2924 cm⁻¹ could assign to C–H stretching vibration. Also observed a band at the wave number 2340 cm⁻¹ could attributed to stretching vibration modes of CO₂ molecule in the air. The peak at 1712 cm⁻¹ is due to the C = O carbonyl group. In this present work, metal–oxygen stretching frequencies appear as three main peaks between 400 and 800 cm⁻¹. The bands observed at 667 cm⁻¹, 558 cm^-1and 501 cm⁻¹ are mainly attributed to intrinsic metal stretching vibration of the ${\mathrm{M}}_{\mathrm{t}\mathrm{e}\mathrm{t}\mathrm{r}\mathrm{a}}-\mathrm{O}$ siteand stretching ${\mathrm{M}}_{\mathrm{o}\mathrm{c}\mathrm{t}\mathrm{r}\mathrm{a}}-\mathrm{O}$ site (Ahmed et al., 2011). These bands in the formation of crystallite phase spinel structure which is in accordance with the XRD results.

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Fig. 5

The synthesis Zn_0.96Cu_0.04Al₂O₄ nanoparticles for different fuel ratios (a) Fuel lean, (b) Fuel stoichiometric (c) Fuel rich: (5) FTIR spectra.

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3.5 Optical studies

The optical absorption spectra are shown in Fig. 6a for the prepared nanoparticles have maximum absorption edge around 331 nm (L), 335 nm (S) and 337 nm (R). There are small variations in the absorption edges and also intensity of edges increase with increase in F/O ratios of glycine. The optical energy bandgap were calculated by the Tauc’s equation (Bhaumik et al., 2014),

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Fig. 6

The synthesis Zn_0.96Cu_0.04Al₂O₄ nanoparticles for different fuel ratios (a) Fuel lean, (b) Fuel stoichiometric (c) Fuel rich: (6a) UV–Vis absorption spectra (6b) Optical bandgap.

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3.6 Luminescence analysis

Luminescence spectra analysis was performed and the results were shown in Fig. 7. The entire sample exhibiting emission peaks at 371, 425, 471 and 637 nm are found in the luminescence spectra under the excitation wavelength of λ_exc = 330 nm. In this study, violet emission peaks were observed at 371 and 425 nm and this characteristic emission peaks were previously determined by Geetha Devi and Sakthi Velu (2016). The blue emission peaks were observed near 471 nm and this kind of blue emission peak was previously reported by Vijayaprasath et al. (2015). A red emission peak was observed at 637 nm mainly due to surface state and deep trap emission. The luminescence emission intensity is observed to significantly decrease with high increase in fuel to oxidizer ratio and there is also a slight peak shift as depicted in the spectrum. The luminescence emission peak shift is due to the variation position of the defect levels within the bandgap. These results indicate that, it has a potential application in monochromatic laser and other optoelectronic devices (Karthik et al., 2017).

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Fig. 7

The synthesis Zn_0.96Cu_0.04Al₂O₄ nanoparticles for different fuel ratios (a) Fuel lean, (b) Fuel stoichiometric (c) Fuel rich: (7) Emission spectrum.

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3.7 Z-Scan analysis

This technique has been developed by Sheik-bahae et al. (1989) to determine both the nonlinear absorption coefficient and nonlinear refractive index for the prepared nanoparticles. The recorded closed aperture and open aperture Z-Scan curves are described in Fig. 8a and b respectively. In this study, open aperture Z-Scan curves were determined. This observation revealed that the sample exhibit the self-defocusing nonlinearity and reverse saturable absorption (RSA). The nonlinear optical parameters are calculated using standard relations (Yugandhar et al., 2017). The third order nonlinear optical susceptibility $({\chi }^{\left(3\right)})$ can be estimated using,

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Fig. 8

The synthesis Zn_0.96Cu_0.04Al₂O₄ nanoparticles for different fuel ratios (a) Fuel lean, (b) Fuel stoichiometric (c) Fuel rich: (8a) Closed aperture (8b) Closed aperture Z-scan analysis.

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Hence, ${\epsilon }_0$ is the permittivity of vacuum (8.854 × 10⁻¹²F/m), $n_0$ is the linear refractive index of the nanomaterial and c is the velocity of the light in vacuum (3 $\times$ 10⁸ m/s). Then, the third order nonlinear optical susceptibility $({\chi }^{\left(3\right)})$ of the prepared nanoparticles can be calculated from

The analyzed parameters of third-order nonlinear optical nonlinearity are described in Table 1. The open aperture Z-Scan curves of this study clearly illustrate decrease in transmission with significant increase in the input intensity. This finding indicates the availability of RSA and has been previously reported by Henari (2001). The obtained results confirm that Zn_0.96Cu_0.04Al₂O₄ nanoparticles are suitable material for optical limiting device applications.

3.8 Antibacterial activity

The synthesized nanoparticles were evaluated against foodborne bacterial pathogens such as, Staphylococcus aureus, Bacillus cereus, Shigella flexneri, Vibrio cholerae bacteria. Nanoparticles showed activity against these bacterial pathogens (Fig. 9). Photograph of antibacterial activity of Zn_0.96Cu_0.04Al₂O₄ nanoparticles is shown in Fig. 10. By the formation of zone of inhibition, the results prove that the samples have good antibacterial activity. The important mechanism of metal oxide in antimicrobial property is the disruption of cell wall by various mechanisms, including generation of reactive oxygen species (ROS) which causes cell disruption (Karthik et al., 2019a).

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Fig. 9

The antibacterial activity of synthesized nanoparticles for different fuel ratios (a) Fuel lean, (b) Fuel stoichiometric (c) Fuel rich: (9) zone of inhibition.

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Fig. 10

The antibacterial activity of synthesized nanoparticles for different fuel ratios (a) Fuel lean, (b) Fuel stoichiometric (c) Fuel rich: (10) Antibacterial activity plate photos and.

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Fig. 11 illustrates the antibacterial mechanism of Zn/Cu/Al₂O₄ nanoparticles. The process involved attraction of positive charged heavy metal ions (Zn²⁺, Cu²⁺ and Al³⁺) with negative charged cell wall and penetrate into the cell wall. Certain bacteria produce ROS production in host organism, including human and may cause cancer (Sadiq et al., 2009). Zn/Cu/Al₂O₄ nanoparticles have the capacity to inhibit the production of ROS in bacteria and protect mammalian cell damage (Parham et al., 2016). Hence, the synthesized nanoparticles are efficient material against the foodborne pathogens which are suitable for medical devices and paramedical applications (Karthik et al., 2019). The prepared Zn/Cu/Al₂O₄ nanoparticles have potential antibacterial activity than other metal oxides (Table 2).

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Fig. 11

The antibacterial activity of synthesized nanoparticles for different fuel ratios (a) Fuel lean, (b) Fuel stoichiometric (c) Fuel rich: (11) antibacterial mechanism.

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