Modification in structural, optical, morphological, and electrical properties of zinc oxide (ZnO) nanoparticles (NPs) by metal (Ni, Co) dopants for electronic device applications

2.1 Sample preparation

The samples of (X-Y-ZnO) (X = Nickel, and Y = Cobalt) with X, Y varying concentrations of Ni, Co from 5–80 % were synthesized by the chemical co-precipitation technique. Zinc Chloride (ZnCl₂) and Sodium Hydroxide (NaOH) were used as initial chemicals, while Nickel Chloride (NiCl₂) and Cobalt Chloride (CoCl₂·6H₂O) were involved as metal dopants; their concentrations were weighted by electric balance. Here deionized water was taken as solvent. 5 % Nickel dopant and 95 % Zinc Chloride (ZnCl₂) were dissolved into deionized water. Both solutions were separately stirred for 30 min and combined into a solution. Furthermore, this combined solution was kept on a magnetic stirrer for 1 h. 100 mL Sodium Hydroxide solution was added drop by drop into this solution during stirring. The (NaOH) solution was added until the pH value of the solution reached 7. The pH of the solution becomes neutral at 7. But the solution would become basic if the pH is more than 7. Therefore the pH of the solution must be maintained at 7. The solution turns into light green in color was obtained at the end. After that, the prepared light green colored solution was stirred for 1 h, so that the homogeneity of the prepared solution could be maintained. At the beaker’s bottom, elements of light green color were established. The particles were washed with distilled water. After washing and filtration the final product was placed in a china dish and dehydrated for 2 h in an oven at 90 °C to get rid of the remaining water. After washing and filtration, a powdered form sample was obtained. To produce extra fine precipitates, the final sample was grinded and at four hours heated at 500 °C in a furnace. The prepared samples of Ni-doped ZnO were cooled down. Similarly, three further samples were synthesized by using different concentrations of initial chemical and metal dopants. In the second sample, 80 % Nickel Chloride (NiCl₂) and 20 % Zinc Chloride (ZnCl₂) were kept. In third sample, 5 % Cobalt chloride (CoCl₂·6H₂O) and 95 % zinc chloride (ZnCl₂) were kept. In forth, 80 % Cobalt Chloride (CoCl₂·6H₂O) and 20 % Zinc chloride (ZnCl₂) were kept. The synthesized metals doped Zinc Oxide nanoparticles (NPs) were characterized to examine their morphology and micro structure, formation, chemical composition, surface and electrical characteristics by utilizing Scanning electron microscopy, X-ray diffraction, UV–visible spectroscopy, FTIR and I-V.

3.1 Phase analysis

X-ray diffraction (XRD) patterns were recorded by a Philips X-ray diffractometer using Ni-filtered CuKα radiation. The XRD patterns which with (0.05 and 0.8) of Nickel and (0.05 and 0.8) Cobalt are shown in Fig. 1. All the samples showed the hexagonal wurtzite phase which was confirmed by XRD analysis. Crystallite sizes calculated were 7.67 nm, 6.52 nm, 5.35 nm and 5.17 nm for ZnO nanoparticles NPs doped with metals Nickel and Cobalt (Ni, Co) 0.05 and 0.2 respectively. The crystallite sizes were decreased by increasing the concentration of dopants in samples. No impurity peaks were observed, which indicated that four samples of ZnO doped with metals Nickel and Cobalt (Ni, Co) nanoparticles (NPs) were highly pure which revealed good crystalline in the nature of the samples. By increasing the concentrations of dopants, the characteristic peak was shifted toward a higher angle. The main peak which showed analyzed sample Cobalt were at higher angle and its full width at high maximum was higher as compared to that of other samples doped with 0.05 Nickel, 0.2 Nickel and Cobalt. It also indicated that the crystallite size was 5.17. These results revealed that crystallite size was highly decreased by doping with 0.05 Cobalt.

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Fig. 1

XRD Spectra of metal dopant (Ni, Co) ZnO nanoparticles (NPs).

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XRD analysis was carried out to determine the structural properties, such as phase formation and crystal structure of the prepared product. Debye-Scherrer equation was used to calculate the crystallite size of nanoparticles (NPs) (Ragupathi et al., 2015; Zaman et al., 2020).

Where D is crystallite size, θ is the Bragg diffraction angle, β is the FWHM of the XRD peak appearing at the diffraction angle θ and where λ is the X-ray wavelength (Cu Kα radiation equals to 1.54 Å) The inter-planner spacing or d-spacing was corresponding to (1 0 1) characteristic peak was determined by Bragg’s law formula which was given as (Jameel et al., 2020).

Where ‘d’ is d-spacing λ is the X-ray wavelength (Cu Kα radiation equals 1.54 Å) and θ is the Bragg diffraction angle.

Both lattice constants ‘a’ and ‘c’ values increased slightly due to the increased incorporation of Ni and Co in the ZnO nanoparticles (NPs). Therefore a regular increase in cell volume (V_cell) (Table 1) with the increase in the content of Ni²⁺ and Co²⁺ ions in composition in the crystal structure is attributed to the increase in lattice constant. Moreover, it can also be seen clearly for the unit cell volume formula, with the increase in the value of ‘a’ and ‘c’ cause in the value of unit cell volume to increase. Table 2.Table 4.

Where‘d’ is d-spacing, h,k,l. are miller indices and a, c are lattice constants.

Where ‘V’ is unit cell volume and a,c are lattice constants. Moreover

the X-ray density ( ${\rho }_{\mathit{XRD}}$ ) of the samples was calculated using the following formula (Jameel et al., 2021).

where ‘M’ is molecular mass, N is Avogadro’s number and ‘V’ is unit cell volume.

The lowest X-ray density exhibited by Ni_0.2-Zn_0.8O was 6.58, which increased with increasing concentrations of dopants up to 11.39. The bulk density ( ${\rho }_B$ ) of the samples was calculated by using the following formula

The highest bulk density exhibited by Ni_0.2-Zn_0.8O was 5.98, which decreased with increasing concentrations of dopants was up to 10.97. The porosity of the samples was calculated by using the following formula (Arif, 2021).

The calculated value of lattice parameters of four samples were (a = 3.2227, a = 3.2210 and c = 1.7623, c = 1.7610) and (a = 2.8397, a = 2.8361 and c = 1.4117, c = 1.4112).

It is observed that increasing the concentrations of dopants increases the values of ‘a’ and ‘b’ lattice parameters. This is attributed to a small mis-match between Zn ion and Ni, Co ions. It indicates that Ni and Co ions systematically substituted Zn ions in the samples without changing their crystal structure. The analyzed lattice volumes of all four samples were 17.0197, 16.9555, 14.8721 and 9.8367. It was noted that this decrease in the lattice volume of unit cell was due to the increase in the concentration of both dopants.

3.2 Dislocation density

The addition of dopants produces the dislocation density, and the dislocation density of the highest peak in all samples was calculated using the equation is given below (Zaman et al., 2021).

Where ‘δ_hkl’ is dislocation density and ‘D’ is crystallite size.

It found from the calculated values ${\delta }_{\mathit{hkl}}=\frac{1}{D^2}$ data that dislocation density with increase the concentrations dopants in samples. The smallest dislocation density 0.016998 was exhibited by Ni_0.05Zn_0.95O, while highest dislocation density 0.37412 was exhibited by Co_0.80Zn_0.2O. The increase in δ_(hkl) value is caused by the atoms of dopants settled inside the host ZnO matrix. Further, the addition of dopants concentration cause to move from grain boundary to crystallite. So, the increase in δ_(hkl) by increasing dopants concentration may also due to the movement of Ni and Co atoms from grain boundary to crystallite.

3.3 Micro-strain

The effect of microstrain and crystallite size in peak broadening was separated using Williamson-Hall (W-H) plot method (Rajesh Kumar and Hymavathi, 2017).

where ‘β’ is FWHM, ‘ε’ is strain, ‘D’ is the crystallite size and ‘λ’ is wavelength. Accordingly, the variations of βCosθ with Sinθ are plotted in Fig. 2, for all the samples.

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Fig. 2

Williamson-Hall plots of metals of dopant (Ni, Co) ZnO nanoparticles NPs.

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The equation used to find out the microstrain is given below (Jacob et al., 2017; Chattopadhyay et al., 2021; Ismail et al., 2019). $$\epsilon =\beta /4tan\theta \left(a\right)$$ where ‘ε’ is micro-strain, ‘β’ is FWHM and ‘θ’ is the Bragg diffraction angle.

In the Williamson-Hall plot, the plots of ${\beta }_{\mathit{hkl}}\cos \theta$ versus 4Sinθ is a straight line for the samples doped with 5% and 80% Nickel and Cobalt as shown in Fig. 2. The root of the y-intercept gives micro-strain, while the crystallite size is determined from the slope of the linearly fitted data. All the values of micro-strain indicate that the prepared samples are under tensile strain. The calculated values of strain (ε) are 2.2591 × 10⁻³, 3.7246x10⁻³, 5.6711x10⁻³ and 8.4304x10⁻³ of ZnO doped with metals Nickel and Cobalt (Ni, Co) nanoparticles (NPs), respectively also represented in Table 3. The micro-strain results exhibit an opposite trend to the crystallite size, such as micro-strain increased with increase in the concentrations of metal (Ni, Co) dopants. The highest crystallite nanoparticles NPs exhibited smaller micro-strain, while the smallest nanoparticles (NPs) exhibited higher micro-strain. Furthermore, the increase in micro-strain may be due to the growth of smaller grains of metals-doped ZnO with respect to an increase in concentrations of dopants. Hence, it showed that the influence of doping of Ni and Co on the average crystallite size and microstrain for as-prepared samples is investigated.

3.4 Morphological analysis

The shape and surface morphology of the synthesized metals doped ZnO nanoparticles (NPs) were analyzed using scanning electron microscopy. Fig. 3. exhibit the surface morphology of the ZnO nanoparticles (NPs) under different magnifications. The SEM image showed that most nanoparticles (NPs) are spherical formed within a diameter of 80–130 nm. The sample of ZnO which was doped with 20% Nickel showed non-uniform distribution and irregularly shaped particles. The surface morphology was gradually changed by increasing the concentration of dopants. The sample was doped with 80% Nickel small in particle size, shape and morphology. 20% nickel doped ZnO nanoparticles (NPs) showed the small agglomeration, non-uniform distribution and 9.06 % porosity. With increasing the concentration of nickel from 20% to 80%, the agglomeration was slightly increased, non-uniformly distributed nanoparticles (NPs) and 8.13 % porosity. Some particles are also found in bunches due to agglomeration. The ZnO doped with 20% Cobalt showed a highly uniform distribution, moderate agglomeration and 5.53% porosity. By increasing the concentration of Cobalt from 20% to 80%, the agglomeration was highly increased, highly non-uniformly distributed nanoparticles (NPs) and 3.70% porosity. It is important to note that porosity was decreased from 9.06% to 3.70% with increasing the concentrations of dopants. The SEM micrographs of metals doped ZnO samples are given in Fig. 3.

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Fig. 3

SEM micrographs of metal dopant (Ni, Co) ZnO nanoparticles NPs.

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3.5 UV visible

UV–Vis absorption calculations along with the band gap measurement with 5% and 80% concentrations of metal (Ni, Co) doped ZnO are shown in Fig. 4. The absorption measurements have been carried out in the range of 200–800 nm. The prepared Ni_0.05Zn_0.95O, Ni_0.8Zn_0.2O, Co_0.05Zn_0.95O and Co_0.8Zn_0.2O samples exhibited absorption peaks at 365 nm, 361 nm, 356 nm and 353 nm, respectively which are the characteristic absorption peaks for wurtzite hexagonal Zinc Oxide. Prominent redshift appeared for metal-doped ZnO nanoparticles (NPs) corresponding to increasing dopant concentrations (Abbas et al., 2021; Parra and Haque, 2014; Vijayakumar et al., 2018; Jameel et al., 2021). Using Tauc’s equation, the energy band gap of doped samples can be calculated through the extrapolation line in the plot of (αhν)ⁿ given by;

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Fig. 4

UV–Visible Absorption Spectra of metal dopant (Ni, Co) ZnO nanoparticles NPs.

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where “α” is the absorbance coefficient, “hν” is the incident photon energy, “k” is the energy independent constant while “E_g” is the energy gap of the optical band. The band gap values were obtained using the Tauc plot by plotting the energy of the absorbed light against the Kubelka–Munk function. The standard band gap value for ZnO is 3.3 eV, but ZnO doped with 5% and 80% Nickel exhibited the wide band gap of 5.23 eV and 5.19 eV, respectively. Moreover ZnO doped with 5% and 80% Cobalt exhibited 5.13 eV and 5.05 eV, respectively. The band gap value in the range of 5.23 to 5.05 eV was considered an ultra-wide band gap semiconductor material. These higher band gaps are due to the too high energy of conduction band minimum (CBM), which nearly equal to the reported band gap of ZnO in the reports (Fan and Freer, 1993; Alhadhrami et al., 2018). Due to the higher band, ZnO NPs exhibited high resistivity and lower conductivity. However, when high energy of conduction band minimum (CBM) records a slight decrease causes the band gap to decrease from 5.23 eV to 5.05 eV, As a result resistivity also decreased. Furthermore, conductivity is also slightly increased.

Although; the standard band gap of ZnO is 3.3 eV, those nanomaterials with a have band gap of about 3.3 eV are considered as a semiconductor. However, in the current study ZnO nanoparticles (NPs) have an ultra-wide band gap of 5.23 eV, 5.19 eV, 5.13 eV and 5.05 eV, with electronic properties, which fall between conventional semiconductor and insulators. The higher energy gap gives devices the ability to operate at higher temperatures and power-switching. Furthermore, the band gap shrinks with increasing temperature. Thus, wide band gap semiconductors are useful at shorter wavelengths than other semiconductor materials.

3.6 Fourier transformation infrared spectroscopy (FTIR)

FT-IR is an effective method to reveal the composition of products and helps in indentifying the possible fuinctional groups involved in Ni-doped ZnO and Co-doped nanoparticles (NPs). This spectrum is a powerful technique that is used to probe different ordering phenomena. This technique provides the information about the crystal vibration modes and the position of ions in the crystal. The FTIR spectra of metal (Ni, Co) doped ZnO nanoparticles (NPs) are represented in Fig. 5. The spectrum obtained clearly showed the ZnO absorption band in the range between 550 cm⁻¹ and 650 cm⁻¹. The FTIR analysis was performed in the frequency range of 500 cm⁻¹ to 4000 cm⁻¹ at room temperature. A peak at 612 cm⁻¹ is the characteristic absorption of the Zn-O bond which is shifted towards 602 cm⁻¹, 597 cm⁻¹ and 591 cm⁻¹ due to the varation or increasing concentrations of Cobalt (Co) and Nickel (Ni) dopant in other samples. This band shows that the materials are Nickel doped Zinc Oxide and Cobalt doped Zinc Oxide (Mohamed and Khairalla, 2019; Wahab et al., 2008; Srivastava et al., 2013). The broad absorption peak at 3397 cm⁻¹ can be attributed to the characteristic absorption of hydroxyl groups (O-H). Due to the increase in concentrations of both dopants, this broad absorption peak was shifted towards 3395 cm⁻¹, 3390 cm⁻¹ and 3386 cm⁻¹. These data are similar to the results observed by others . Peaks 1718.12 cm⁻¹, 1712.37 cm⁻¹, 1707.29 cm⁻¹ and 1697.13 cm⁻¹ were due to the bending of water molecules. The bands at 1461.33 cm⁻¹, 1456.26 cm⁻¹, 1440.29 cm⁻¹ and 1424.33 cm⁻¹. The spectrum showed bands at stretching of alkanes, C-H (aromatics), C = C-H (alkynes) and –OH stretching in intermolecular H-bond, C = O and C-C stretching of alkanes (5). Some unresolved peaks in precipitate may be due to some impurities (Kumar and Choubey, 1512; Singh et al., 2020; Nava et al., 2017). Fig. 6.

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Fig. 5

FTIR Spectra of metal dopant (Ni, Co) ZnO nanoparticles NPs.

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Fig. 6

Current voltage (I-V) characteristics of metal dopant (Ni, Co) ZnO nanoparticles (NPs).

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3.7 Current voltage (I-V) characteristics

From the I-V curves, a linear behavior can be seen implying the ohmic nature of the prepared Ni, Co-ZnO sample. The resistivity and conductivity of the Ni, Co-ZnO were calculated using the following equation:

where R is the resistance of the sample, b is the width of the electrodes; t is the thickness of the sample and l is the length of the electrodes. The conductivity of the material was calculated by taking the inverse of the resistivity. I-V characteristic curves show the relationship between the current flowing through an electronic device and the applied voltage across its terminals. Thus optimization of proton conductivity depends on the proton transport, defect formation and chemical stability of the materials. The determination of proton conductivity relies on the introduction of defects into the oxide structure and their distribution in the crystalline lattice. The proton conductivity is dependent on the concentration of oxygen vacancies in the oxides producing reactive sites and enabling ionic diffusion. When the material is exposed to hydrogen rich or hydroxyl group filled the oxygen vacancies, simply that protons are combined into perovskite structure leading to proton defect (Yedurkar et al., 2016; Ali et al., 2021; Grinevich and Filevska, 2020; Kumar et al., 2015; Kumar et al., 2019).

I-V characteristic curves are used to define its operation within an electrical circuit. As its name suggests, I-V characteristic curves show the relationship between the current flowing through an electronic device and the applied voltage across its terminals. I-V characteristic curves are generally used as a tool to determine and understand the basic parameters of a component or device. They can also be used to mathematically model its behavior within an electronic circuit. But as with most electronic devices, Infinite I-V characteristics curves represent the various inputs or parameters. We can display a family or group of curves on the same graph to represent the various values. For example the electrical supply voltage V, applied to the resistive element R above terminals was varied, the resulting current, can be measured. This current would be characterized as: I = V/R, being one of Ohm’s Law equations. Many electronic components and devices which have non-linear characteristics that is their V/I which ratio is not constant. For example non-linear current–voltage characteristics characterized semiconductor diodes as the current flowing through a forward-biased common silicon diode is limited by the ohmic resistance of the PN-junction (Kumar et al., 2019; Panwar et al., 2014; Mondal et al., 2013; Ali et al., 2021).