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Tertiary NiCuZn ferrites for improved humidity sensors: A systematic study
Accepted Manuscript Original article Tertiary NiCuZn ferrites for improved humidity sensors: a systematic study Constantin Virlan, Florin Tudorache, Aurel Pui PII: DOI: Reference:
S1878-5352(18)30063-7 https://doi.org/10.1016/j.arabjc.2018.03.005 ARABJC 2274
To appear in:
Arabian Journal of Chemistry
Received Date: Accepted Date:
9 October 2017 3 March 2018
Please cite this article as: C. Virlan, F. Tudorache, A. Pui, Tertiary NiCuZn ferrites for improved humidity sensors: a systematic study, Arabian Journal of Chemistry (2018), doi: https://doi.org/10.1016/j.arabjc.2018.03.005
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Tertiary NiCuZn ferrites for improved humidity sensors: a systematic study Constantin Virlan1, Florin Tudorache2, Aurel Pui1,* 1
Faculty of Chemistry, “Alexandru Ioan Cuza” University of Iasi, Carol I Bd., no. 11, 700506 Iasi, Romania
2
Research Center on Advanced Materials and Technologies, Interdisciplinary Research Department—Field Science, Alexandru Ioan Cuza University of Iasi, Bd. Carol I, Nr. 11, 700506 Iasi, Romania *
[email protected]
Abstract Numerous works on ferrites containing three or more divalent cations offer incomplete data thus failing to offer clear correlations between composition and properties. This study describes the magnetic and electrical behaviour of spinel ferrites containing Ni, Cu and Zn. Samples with the general formula Ni0.nCu0.cZn0.zFe2O4, with all metals varying between 0 and 0.5, were obtained by the co-precipitation method. The structure of the samples was investigated by X-ray diffraction and infrared spectroscopy, confirming the formation of pure spinel ferrites. The lattice parameter calculation and EDX analysis confirm the composition of the samples. The magnetic behaviour of the nanoparticles is, in general, superparamagnetic with few samples that present small coercivity, corresponding to larger crystallite size. The measurements indicate the superparamagnetic size limit to be of about 14 nm, which can be observed for samples with low zinc content. The electrical properties of the nanoparticles vary with the composition with dominant indirect effect through microstructural changes. All samples have a similar behaviour in relation to electrical current frequency, with variations of about one order of magnitude resistivity. The relative permittivity indicates several samples that change behaviour, such as nickel rich samples, that significantly decrease relative permittivity at higher frequencies compared to zinc rich samples with smaller variations. All samples present variations in the presence of water vapour but increased sensitivity towards
1
humidity was observed for nickel rich samples characterized by larger crystallite size and density. Keywords: Ferrite, co-precipitation, superparamagnetic, humidity sensors
1. Introduction Spinel ferrites are extremely versatile materials with numerous applications. They can be easily obtained by chemical methods such as co-precipitation (Gherca et al., 2014; Lu et al., 2007), hydrothermal (Naseri et al., 2013; Zhao et al., 2008), sol-gel (Zandi Khajeh et al., 2015; Zhang et al., 2014b), or ceramic synthesis (Murugesan et al., 2014). Alternative physical methods include laser ablation, spray pyrolysis (Laurent et al., 2008), etc., but require a more complex set-up and specific precursors. The large variety of formulations allowed these materials to be used in different applications. Spinel ferrites are highly promising catalysts for environmental remediation and organic chemistry (Casbeer et al., 2012; Nidheesh et al., 2013; Zhang et al., 2014a) as well as adsorbents for undesired species (Hua et al., 2012) as proven by numerous studies. In recent years, attention turned towards their use in biomedical applications as drug carriers (Sharifi et al., 2012), contrast agents (Sathya et al., 2016) and in cancer treatment by magnetic hyperthermia (Deatsch and Evans, 2014). Ferrites containing nickel and copper are some of the most appealing compositions for gas and humidity sensor applications due to their large electrical resistivity and sensibility for both oxidizing and reducing agents (Su et al., 2009; Yu et al., 2006). The use of zinc is justified by the specific replacement of iron species located in the tetrahedral lattice due to its predisposition to form normal spinels (Habibi et al., 2014; Shrotri et al., 1999). A brief literature study indicates that numerous studies that approach tertiary ferrites contain a limited number of samples whilst maintaining one of the divalent metals constant or varying within a small interval. 2
The aim of this study is to obtain a clear overview on the magnetic and electrical properties of tertiary ferrites by using a comprehensive approach. A short literature review indicates a problem in this direction, with numerous works on a small number of samples providing a limited description of the ternary systems with no reference values to refer to. This study proposes the synthesis and characterization of 15 samples with all three metals alternatively varying between 0 and 0.5, thus covering a wider domain and offering an overview over the combined influence of each metal. The structure of the study will therefore ensure a preliminary assessment of the samples towards their use in the development of electrical components, emphasizing their applicability as humidity and gas sensors.
2. Materials and methods 2.1. Nanoparticle synthesis The nanoparticles where obtained using the co-precipitation method in the presence of carboxymethylcellulose described elsewhere (Pui et al., 2011; Virlan et al., 2016). The metal precursors where metal chlorides solubilized in water, carefully mixed in the desired ratios for each samples. The precipitating agent was NaOH 3M, added once the temperature is stable at 80 °C. The mixture was maintained in the heating mantle for one hour and the pH of the product is carefully monitored and maintained above 12. The brownish precipitate is repeatedly washed with distilled water to remove excess surfactant and a major part of NaCl, the main side product. After drying in air at 110 °C the samples were calcined at 500°C for 6 hours. The final product is again washed with distilled water until the complete removal of NaCl.
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2.2. Sample characterization The samples were characterized by means of X-ray diffraction using a LabX 600 diffractometer, infrared spectra were recorded in the 600-250 cm-1 region using a Jasco 660 plus and the CsI pelletizing technique. The morphology and elemental composition of the samples were evaluated by scanning electron microscopy using a SEM-EDX model Vega Tescan LMH II. The magnetic hysteresis loops were obtained using a VSM type 4600 with applied magnetic field of ±10kOe. In order to evaluate the electrical behaviour of the ferrites, the samples where pelletized using a 13 mm dye under 7 tons force. Silver electrodes where painted on the pellets and a Wayne-Kerr bridge was used to measure electrical resistivity and relative permittivity in controlled conditions (electrical field frequency and relative humidity).
3. Results and discussions 3.1. Structural and morphological analysis of polycrystalline samples The X-ray diffractograms presented in fig. 1. clearly illustrate the formation of pure spinel ferrites with no additional peaks. The positions and intensities of the peaks are similar and were used to calculate the mean crystallite size using Scherrer’s equation. The calculated crystallite size were plotted in relation to the increasing metal content as well as overall variation in the ternary system. Figure 2a. illustrates the variation of crystallite size in relation to the increasing metal content. All three subseries present significant variations indicating the contribution of each metal. One sample, containing no zinc, has a different value from the observed trends and cannot be clearly attributed to composition and may be influenced by errors in the synthesis protocol. The most prevalent variation can be assigned to increasing zinc content which leads to a significant reduction in crystalline size, as reported by other researchers. The zinc
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contribution is complementary to that of nickel, whilst copper appears to have minor influence with crystallite size constantly above 14 nm. The analysis of figure 2b. offers an overview of the variation in crystallite size confirming the specific influence of zinc and nickel. The left corner of the triangle (predominantly blue) indicates smallest crystallite size for the largest zinc content, while the largest values are obtained for maximum amount of nickel and increasing amounts of zinc (orange
and
red
colour),
with
the
outlying
value
corresponding
to
sample
Ni0.5Cu0.5Zn0.0Fe2O4.
Fig. 1. Diffractograms for obtained samples, grouped by increasing content in nickel (a), copper (b), zinc (c) The overall variation of the size can be assigned to the contribution of the metals with limited contribution from the synthesis protocol with small variations (5 nm) throughout the samples. The specific influence of zinc on the crystallite size has been previously observed by numerous researchers and is assigned to its stronger chemical affinity for tetrahedral sites. The specificity of zinc distribution acts as a limiting factor in the growth of the nanoparticles with an overall crystallite size reduction (Jadhav et al., 2016; Rath et al., 2002). The observed behaviour can be assigned to the distribution of metals in the sublattices of the spinel structures with zinc distributed specifically in the tetrahedral lattice while nickel and copper are mainly found in the octahedral lattice according to their preference to form inverse spinels. Taking into account these aspects the theoretical lattice parameters was calculated using the cationic distribution and equations described below for a better comparison with experimental values calculated using Bragg’s relation. The lattice parameters can help explain the variations in crystallite size, indicating changes in the crystal lattice according to composition and cationic radius of the metals present in the samples.
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Fig. 2. Crystallite size calculated from XRD date in relation to increasing metal (a) and overall variation (b)
The assumed cationic distribution places the zinc in the tetrahedral lattice, whilst nickel and copper are distributed in the octahedral lattice with equivalent iron redistribution. The generic cationic formula is presented in eq. 1 where n,c,z correspond to the metal contents in the formula NinCucZnzFe2O4 (Shannon, 1976; Tatarchuk et al., 2017). (1) Round parentheses indicate metals in the tetrahedral lattice Square parentheses indicate metals in the octahedral lattice
The radius of tetrahedral and octahedral sites where calculated using eq. 2 and 3. The Shannon cationic radiuses of the metals in the appropriate coordination were used for calculations. The theoretical lattice parameter was calculated in agreement to eq. 4 considering the anionic radius of oxygen to be 1.40 Å (Shannon, 1976). (2) (3) (4)
The graphical representation of experimental and theoretical lattice parameter values (fig. 3.) confirms the composition of the ternary system, even in the absence of elemental analysis with no outstanding values as in the case of crystallite size. The lattice parameter varies between 8.420 Å, for the nanoparticles containing maximum amount of zinc, and 8.352
6
Å, slightly smaller than the theoretical value of 8.366 Å. The observed variations confirm that the assumed cationic distribution was correct and that the larger tetracoordinated zinc cations induce an increase in lattice parameter and equivalent crystallite size reduction as suggested by other authors (Su et al., 2009; Yu et al., 2006).
Fig. 3. Variation of experimental (a) and theoretical (b) lattice parameter
Fourier transformed infrared spectroscopy was additionally used to investigate the changes in the crystal lattice as a complementary method to XRD. The FT-IR spectra recorded in the 600-250 cm-1 domain offers information regarding the changes in the crystal lattice with composition by specific peak displacements, which can be assigned to corresponding bonds. The deconvolution of the spectra presents four major peaks in the investigated region which can be assigned in accordance to bond length. Taking into account that bond length varies in the order RFe(Td) < RZn(Td) < RFe(Oh) < RNi(Oh) < RCu(Oh), similarly to bond strength, we can assume that the observed vibrations can be assigned in the same order. One exception is the case of Ni(Oh)-O and Cu(Oh)-O which cannot be differentiated even after deconvolution and are observed as a single peak. The first vibration, in the 580-550 cm-1 region, corresponding to tetracoordinated iron cations, shifts toward larger wavenumbers in the nanoparticles containing increasing amounts of zinc (fig. 4c.) when the insertion of larger zinc cations induces the shrinkage of Fe-O bonds. In the compositions containing constant or decreasing amounts of zinc the displacement is towards smaller wavenumbers due to the cumulative effect of nickel and copper cations (Shannon, 1976; Virlan et al., 2016).
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The second most intense displacements can be observed for octahedral iron (ν3). In this case the bond length is affected by both the octahedral nickel and copper (figures 4a. and 4b.) as well as tetrahedral zinc (figure 4c.). The most intense changes are induced by the increasing amounts of copper and zinc, the larger cations, with corresponding displacements toward smaller wavenumbers (Virlan et al., 2017, 2016). Minor changes can be observe for the vibrations of divalent metals in both tetrahedral and octahedral sites (480-440 cm-1 and 330-310 cm-1). The displacements can be similarly explained and are in general correlated to the cationic radius of the metals present in the sublattices. The overall variations are mainly induced by changing amounts of copper and zinc which are the largest cations. The variations can also be correlated with the lattice parameter, especially in the tetrahedral sublattice. Displacements of the bands towards larger wavenumber can be observed with decreasing zinc content and corresponding decrease in lattice parameter (Virlan et al., 2017).
Fig. 4. FT-IR spectra grouped by increasing content in nickel (a), copper (b), zinc (c)
The morphology of the samples and elemental composition were investigated using scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEMEDX). The obtained images indicate the formation of large agglomerates due to the high calcination temperature. The formation of high density structures indicate that the calcination treatment acts a preliminary sintering stage, which may prove useful in enhancing specific electrical properties of the nanoparticles (fig. 5.).
Fig. 5. SEM images of dense agglomerates containing Ni0.5Cu0.5 Zn0.0Fe2O4 nanoparticles
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The EDX analysis is crucial in confirming the composition of the nanoparticles in addition to X-ray diffraction and FT-IR spectroscopy, especially taking into account the complex composition of the samples. The calculated composition of the materials is very similar to the desired ratios as presented in fig. 6. Minor differences can assigned to the intrinsic error of the method, but the overall results confirm the formation of tertiary ferrites with well-established composition. The EDX results are in good agreement with the lattice parameter calculated from XRD data providing a double confirmation of the composition (Joshi et al., 2016).
Fig. 6. Theoretical and EDX composition of the nanoparticles
3.2. Magnetic properties The magnetic properties of the nanoparticles were assessed by obtaining the hysteresis loops using the vibrating sample magnetometry technique, from which the saturation magnetization and coercivity values were extracted. The hysteresis loops presented in fig. 7. suggest an apparent superparamagnetic behaviour for all samples but a closer look shows that some of the nanoparticles have small coercivity and remanent magnetization. An initial evaluation of the loops indicate that the samples containing large amounts of nickel account for larger saturation magnetization while samples with smaller amounts of zinc present ferrimagnetic behaviour. Upon closer inspection of the samples’ magnetization we can observe that the maximum value was obtained for the Ni0.5Cu0.2Zn0.3Fe2O4 formula closely followed by the samples with inverse Cu-Zn ratio and similar formulations (fig. 8.). At the opposite end, the sample containing no zinc has a minimum value for the saturation magnetization. Although
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the main influence may be due to the presence of nickel, the main contribution can be assigned to the mean crystallite size, which varies in a very similar manner.
Fig. 7. Hysteresis loops for all samples
The similar behaviour of both saturation magnetization and crystallite size confirms that the composition has an indirect effect on the magnetic behaviour of the samples by inducing changes in the crystallite size, previously described. Due to the very small nanoparticles the size and surface effects are dominant in establishing the magnetic behaviour and the saturation magnetization of the samples. Furthermore, when analysing the coercivity and remanent magnetization of the samples we can observe that 5 samples present ferrimagnetic characterized by coercivity between 8 and 28 Oe. From the graphical representation of coercivity (fig. 8b.) we can observe that the ferrimagnetic samples are the ones containing small amounts of zinc. Once again, comparing to the variation of crystallite size, we can observe that the composition influence is indirect by changing the size with the exception of Ni0.5Cu0.5Zn0.0 Fe2O4 samples, which has large coercivity despite having minimum saturation magnetization and very small crystallite size (Kambale et al., 2010; Rath et al., 2002). This specific behaviour observed in the case of the sample containing no zinc confirms the importance of tetrahedrally distributed zinc cations which alter the overall magnetic behaviour of the samples. This specific case can be explained due to the fact that the absence of zinc corresponds to equally distributed Fe3+ cations in the two sublattices with the overall magnetic moment assigned to the presence of nickel and copper cations in the octahedral sublattice. One of the most important aspect that can be observed by comparing the coercivity and crystallite size variation is the critical diameter of the nanoparticles, marking
10
the transition from superparamagnetic to ferrimagnetic. In the present case the critical diameter of the nanoparticles appears to be in the 14 nm region with the exception of Ni-Cu ferrite which is ferrimagnetic despite the mean size of approximately 11 nm (Jadhav et al., 2009; Rath et al., 2002).
Fig. 8. Variation of saturation magnetization (a) and coercivity (b) of the samples in relation to composition
3.3. Electrical properties. The electrical behaviour of the samples represents one of the most important characteristics of mixed ferrites which have great potential in the development of humidity and gas sensors. In order for the materials to be useful in designing sensor they must have both electrical resistivity and permittivity that can be easily measured and be dependent on environmental conditions. The nanoparticles are characterized by large electrical resistivity in the 10 7-109 Ωm at 20 Hz with decreasing values towards larger frequencies (fig. S-1.). This behaviour is characteristic for most spinel ferrites in accordance to the typical Maxwell-Wagner-Debye relaxation (Petrila et al., 2016; Tudorache et al., 2016). The effective values are slightly smaller than that of similar samples obtained through the classical ceramic method suggesting the materials have good potential towards electrical component development. The studies reported by Shrotri et al. indicate increased electrical resistivity on similar compositions but the mean crystallite size and density of the samples differs significantly due the higher
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thermal treatment of the samples, characteristic for the ceramic method (Jadhav et al., 2016; Shrotri et al., 1999). The electrical resistivity was found to decrease approximately 4 orders of magnitude with increasing frequency up to 2·107 Hz. When analysing the dependence of electrical resistivity we can observe a significant decrease with humidity, indicating their sensing capability towards water vapours and perhaps other organic components (fig. S-2). The sample Ni0.3Cu0.2Zn0.5Fe2O4 presents the largest electrical resistivity throughout the investigated domain but has moderate variation in relation to relative humidity. At the opposite end Ni0.5Cu0.4Zn0.1Fe2O4 has the smallest resistivity at small frequencies but varies significantly with humidity (Verma et al., 1999). The relative permittivity of the samples has a similar behaviour in relation to electrical current frequency (fig. S-3) and opposed behaviour in relation to humidity (fig. S-4), as it would be expected. The samples have rather small electrical permittivity with the smallest value observed once again for the Ni0.3Cu0.2 Zn0.5Fe2O4 sample, while the maximum value at 20 Hz was observed for the sample containing no zinc. The relative permittivity of the samples has classical inverse variation as electrical resistivity with the same two samples outstanding with maximum and minimum variations. Similar studies reported by Jadhav et al. using the citrate method report higher relative permittivity values, but the samples in question had a higher nickel content and cannot be easily compared. No other studies involving similar large numbers of similar composition were found. The existing references include a small number of samples and do no offer comparable results on the whole domain (Jadhav et al., 2016; Raghavender et al., 2011).
The firs evaluation towards the sensing capabilities of the nanoparticles were obtained by measuring the variations of electrical resistivity and relative permittivity in the presence of
12
water vapours. In the first case, the electrical resistivity decreases by up to two orders of magnitude for samples containing large amounts of nickel, while the relative permittivity increased up to 6 times for the Ni0.5Cu0.4Zn0.1 Fe2O4 sample, closely followed by similar nickel rich combinations. These aspects lead to a primary conclusion that zinc has an overall negative effect on the sensing capabilities of the nanoparticles in the presence of water vapours. The evaluation of fig. S-2. and S-4. offers just a preliminary assessment of their sensing capabilities and does not offer an exact measure of their sensibility. To better assess the nanoparticles’ potential the relative sensibility was calculated in relation to both resistivity and permittivity. In order to normalize the sensibility for both measuring regimes the sensibility in permittivity regime was calculated as ԐR100%/ ԐR0% while in the resistivity regime the inverse logarithmic normalization was used lg(ρ 0%)/ lg(ρ100%). In both cases, in the ternary diagrams represented in fig. 9a. and 9b. maximum sensibility is denoted by orange-red hues.
Fig. 9. Sensitivity of the samples in resistivity (a) and relative permittivity (b) measuring mode and morphological characteristics of the samples (c, d)
In permittivity sensing mode we can observe enhanced sensibility for samples contained maximum amount of nickel and similar amount of copper and zinc, which is the Ni0.5Cu0.2Zn0.3 Fe2O4 and Ni0.5Cu0.4Zn0.1Fe2O4 samples. Comparing the variation of sensibility with the variation of crystallite size we can deduce a similar influence as in the case of magnetic properties, with larger particles presenting increased sensibility. Although in general smaller crystallite size means larger active surface and better adsorption of the measured species in our case the variations are rather small and compensated by the agglomeration observed by electron microscopy. In this case the crystallite size influence can be assigned to
13
the classical interparticle conduction mechanism based on electron or hole ¨hoping¨ and the formation of the Schotky barrier which is strongly dependent on the agglomeration of the nanoparticles (Mirzaei et al., 2016). In resistivity sensing mode we can observe similar behaviour with increase sensibility for nickel rich samples but the maximum sensibility in this case is assigned to the Ni0.4Cu0.4Zn0.2 Fe2O4 sample. In this case conduction mechanism is more evident as the sensitivity can be correlated with the sample density as calculated from XRD data. In other words the increase in density generates a closer packing of the nanoparticles in the agglomerates observed by electron microscopy and subsequent reduction in the Schotky barrier. The overall sensing capabilities of the nanoparticles are strongly dependent on the nanoparticles composition and are favoured by the presence of nickel due to the prevalence of p type conduction generated by the presence of nickel cations as opposed to samples containing large amount of zinc or copper which rely on n type conduction. Furthermore the sensibility of the samples is strongly influenced by morphological parameters as crystallite size and density, which strongly affect the electrical properties of the samples, as in most cases. These parameters can be subsequently modified and the present study offers a preliminary evaluation of the materials.
4. Conclusions The co-precipitation method described offers a simple and efficient approach to obtaining large amounts of pure ferrites with little influence over their morphology as confirmed by XRD data. The composition of the nanoparticles was confirmed by both the lattice parameter values and elemental analysis. The calculated crystallite size and density are strongly dependent on the composition of the nanoparticles. The presence of zinc induces the 14
largest changes in crystallite size and lattice parameter owing to its larger cationic volume. At low zinc content the magnetic behaviour changes from superparamagnetic to ferrimagnetic, most likely due to the larger crystallite size. The critical diameter marking this transition was identified in the 14 nm region. All the samples are characterized by large electrical resistivity and small relative permittivity. The electrical resistivity has a typical variation to current frequency with small differences between the samples. The relative permittivity has a similar behaviour but indicates a small number of samples with outlying changes. The electrical properties of the materials are strongly dependant to the presence of water vapours, suggesting they can be used for sensor development. The most sensitive samples contain large amounts of nickel and copper suggesting the important of hole conduction (p-type conduction) specific for the presence of nickel. Crystallite size and density also play a major role in determining the electrical behaviour of the samples suggesting subsequent calcinations can modify the microstructure and sensing capability of the samples. The study confirms the efficiency of the method and points towards nickel rich samples as potential sensing materials after appropriate treatments.
The authors have no conflicts of interest to declare. Acknowledgements: This work was partially funded by NanoQFerrox-Syst project approved by MENCS Romania and IUCN Dubna. Tiberiu Roman is gratefully acknowledged for SEM-EDX measurements.
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Supplemental information
Fig. S-1. Electrical resistivity dependence on current frequency
Fig. S-2. Electrical resistivity dependence on relative humidity
Fig. S-3. Relative permittivity dependence on current frequency
Fig. S-4. Relative permittivity dependence on relative humidity
19
245–252.
List of figures
Fig. 1. Diffractograms for obtained samples, grouped by increasing content in nickel (a), copper (b), zinc (c) Fig. 2. Crystallite size calculated from XRD date in relation to increasing metal (a) and overall variation (b) Fig. 3. Variation of experimental (a) and theoretical (b) lattice parameter Fig. 4. FT-IR spectra grouped by increasing content in nickel (a), copper (b), zinc (c) Fig. 5. SEM images of dense agglomerates containing Ni0.5Cu0.5 Zn0.0Fe2O4 nanoparticles Fig. 6. Theoretical and EDX composition of the nanoparticles Fig. 7. Hysteresis loops for all samples Fig. 8. Variation of saturation magnetization (a) and coercivity (b) of the samples in relation to composition Fig. 9. Sensitivity of the samples in resistivity (a) and relative permittivity (b) measuring mode and morphological characteristics of the samples (c, d)
Fig. S-1. Electrical resistivity dependence on current frequency Fig. S-2. Electrical resistivity dependence on relative humidity Fig. S-3. Relative permittivity dependence on current frequency Fig. S-4. Relative permittivity dependence on relative humidity
20
Fig. 1. Diffractograms for obtained samples, grouped by increasing content in nickel (a), copper (b), zinc (c)
Fig. 2. Crystallite size calculated from XRD date in relation to increasing metal (a) and overall variation (b)
21
Fig. 3. Variation of experimental (a) and theoretical (b) lattice parameter
Fig. 4. FT-IR spectra grouped by increasing content in nickel (a), copper (b), zinc (c)
22
Fig. 5. SEM images of dense agglomerates containing Ni0.5Cu0.5 Zn0.0Fe2O4 nanoparticles
Fig. 6. Theoretical and EDX composition of the nanoparticles
Fig. 7. Hysteresis loops for all samples
23
Fig. 8. Variation of saturation magnetization (a) and coercivity (b) of the samples in relation to composition
Fig. 9. Sensitivity of the samples in resistivity (a) and relative permittivity (b) measuring mode and morphological characteristics of the samples (c, d) Electronic supplemental material 24
Fig. S-1. Electrical resistivity dependence on current frequency
Fig. S-2. Electrical resistivity dependence on relative humidity
25
Fig. S-3. Relative permittivity dependence on current frequency
Fig. S-4. Relative permittivity dependence on relative humidity
26
S1878-5352(18)30063-7 https://doi.org/10.1016/j.arabjc.2018.03.005 ARABJC 2274
To appear in:
Arabian Journal of Chemistry
Received Date: Accepted Date:
9 October 2017 3 March 2018
Please cite this article as: C. Virlan, F. Tudorache, A. Pui, Tertiary NiCuZn ferrites for improved humidity sensors: a systematic study, Arabian Journal of Chemistry (2018), doi: https://doi.org/10.1016/j.arabjc.2018.03.005
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Tertiary NiCuZn ferrites for improved humidity sensors: a systematic study Constantin Virlan1, Florin Tudorache2, Aurel Pui1,* 1
Faculty of Chemistry, “Alexandru Ioan Cuza” University of Iasi, Carol I Bd., no. 11, 700506 Iasi, Romania
2
Research Center on Advanced Materials and Technologies, Interdisciplinary Research Department—Field Science, Alexandru Ioan Cuza University of Iasi, Bd. Carol I, Nr. 11, 700506 Iasi, Romania *
[email protected]
Abstract Numerous works on ferrites containing three or more divalent cations offer incomplete data thus failing to offer clear correlations between composition and properties. This study describes the magnetic and electrical behaviour of spinel ferrites containing Ni, Cu and Zn. Samples with the general formula Ni0.nCu0.cZn0.zFe2O4, with all metals varying between 0 and 0.5, were obtained by the co-precipitation method. The structure of the samples was investigated by X-ray diffraction and infrared spectroscopy, confirming the formation of pure spinel ferrites. The lattice parameter calculation and EDX analysis confirm the composition of the samples. The magnetic behaviour of the nanoparticles is, in general, superparamagnetic with few samples that present small coercivity, corresponding to larger crystallite size. The measurements indicate the superparamagnetic size limit to be of about 14 nm, which can be observed for samples with low zinc content. The electrical properties of the nanoparticles vary with the composition with dominant indirect effect through microstructural changes. All samples have a similar behaviour in relation to electrical current frequency, with variations of about one order of magnitude resistivity. The relative permittivity indicates several samples that change behaviour, such as nickel rich samples, that significantly decrease relative permittivity at higher frequencies compared to zinc rich samples with smaller variations. All samples present variations in the presence of water vapour but increased sensitivity towards
1
humidity was observed for nickel rich samples characterized by larger crystallite size and density. Keywords: Ferrite, co-precipitation, superparamagnetic, humidity sensors
1. Introduction Spinel ferrites are extremely versatile materials with numerous applications. They can be easily obtained by chemical methods such as co-precipitation (Gherca et al., 2014; Lu et al., 2007), hydrothermal (Naseri et al., 2013; Zhao et al., 2008), sol-gel (Zandi Khajeh et al., 2015; Zhang et al., 2014b), or ceramic synthesis (Murugesan et al., 2014). Alternative physical methods include laser ablation, spray pyrolysis (Laurent et al., 2008), etc., but require a more complex set-up and specific precursors. The large variety of formulations allowed these materials to be used in different applications. Spinel ferrites are highly promising catalysts for environmental remediation and organic chemistry (Casbeer et al., 2012; Nidheesh et al., 2013; Zhang et al., 2014a) as well as adsorbents for undesired species (Hua et al., 2012) as proven by numerous studies. In recent years, attention turned towards their use in biomedical applications as drug carriers (Sharifi et al., 2012), contrast agents (Sathya et al., 2016) and in cancer treatment by magnetic hyperthermia (Deatsch and Evans, 2014). Ferrites containing nickel and copper are some of the most appealing compositions for gas and humidity sensor applications due to their large electrical resistivity and sensibility for both oxidizing and reducing agents (Su et al., 2009; Yu et al., 2006). The use of zinc is justified by the specific replacement of iron species located in the tetrahedral lattice due to its predisposition to form normal spinels (Habibi et al., 2014; Shrotri et al., 1999). A brief literature study indicates that numerous studies that approach tertiary ferrites contain a limited number of samples whilst maintaining one of the divalent metals constant or varying within a small interval. 2
The aim of this study is to obtain a clear overview on the magnetic and electrical properties of tertiary ferrites by using a comprehensive approach. A short literature review indicates a problem in this direction, with numerous works on a small number of samples providing a limited description of the ternary systems with no reference values to refer to. This study proposes the synthesis and characterization of 15 samples with all three metals alternatively varying between 0 and 0.5, thus covering a wider domain and offering an overview over the combined influence of each metal. The structure of the study will therefore ensure a preliminary assessment of the samples towards their use in the development of electrical components, emphasizing their applicability as humidity and gas sensors.
2. Materials and methods 2.1. Nanoparticle synthesis The nanoparticles where obtained using the co-precipitation method in the presence of carboxymethylcellulose described elsewhere (Pui et al., 2011; Virlan et al., 2016). The metal precursors where metal chlorides solubilized in water, carefully mixed in the desired ratios for each samples. The precipitating agent was NaOH 3M, added once the temperature is stable at 80 °C. The mixture was maintained in the heating mantle for one hour and the pH of the product is carefully monitored and maintained above 12. The brownish precipitate is repeatedly washed with distilled water to remove excess surfactant and a major part of NaCl, the main side product. After drying in air at 110 °C the samples were calcined at 500°C for 6 hours. The final product is again washed with distilled water until the complete removal of NaCl.
3
2.2. Sample characterization The samples were characterized by means of X-ray diffraction using a LabX 600 diffractometer, infrared spectra were recorded in the 600-250 cm-1 region using a Jasco 660 plus and the CsI pelletizing technique. The morphology and elemental composition of the samples were evaluated by scanning electron microscopy using a SEM-EDX model Vega Tescan LMH II. The magnetic hysteresis loops were obtained using a VSM type 4600 with applied magnetic field of ±10kOe. In order to evaluate the electrical behaviour of the ferrites, the samples where pelletized using a 13 mm dye under 7 tons force. Silver electrodes where painted on the pellets and a Wayne-Kerr bridge was used to measure electrical resistivity and relative permittivity in controlled conditions (electrical field frequency and relative humidity).
3. Results and discussions 3.1. Structural and morphological analysis of polycrystalline samples The X-ray diffractograms presented in fig. 1. clearly illustrate the formation of pure spinel ferrites with no additional peaks. The positions and intensities of the peaks are similar and were used to calculate the mean crystallite size using Scherrer’s equation. The calculated crystallite size were plotted in relation to the increasing metal content as well as overall variation in the ternary system. Figure 2a. illustrates the variation of crystallite size in relation to the increasing metal content. All three subseries present significant variations indicating the contribution of each metal. One sample, containing no zinc, has a different value from the observed trends and cannot be clearly attributed to composition and may be influenced by errors in the synthesis protocol. The most prevalent variation can be assigned to increasing zinc content which leads to a significant reduction in crystalline size, as reported by other researchers. The zinc
4
contribution is complementary to that of nickel, whilst copper appears to have minor influence with crystallite size constantly above 14 nm. The analysis of figure 2b. offers an overview of the variation in crystallite size confirming the specific influence of zinc and nickel. The left corner of the triangle (predominantly blue) indicates smallest crystallite size for the largest zinc content, while the largest values are obtained for maximum amount of nickel and increasing amounts of zinc (orange
and
red
colour),
with
the
outlying
value
corresponding
to
sample
Ni0.5Cu0.5Zn0.0Fe2O4.
Fig. 1. Diffractograms for obtained samples, grouped by increasing content in nickel (a), copper (b), zinc (c) The overall variation of the size can be assigned to the contribution of the metals with limited contribution from the synthesis protocol with small variations (5 nm) throughout the samples. The specific influence of zinc on the crystallite size has been previously observed by numerous researchers and is assigned to its stronger chemical affinity for tetrahedral sites. The specificity of zinc distribution acts as a limiting factor in the growth of the nanoparticles with an overall crystallite size reduction (Jadhav et al., 2016; Rath et al., 2002). The observed behaviour can be assigned to the distribution of metals in the sublattices of the spinel structures with zinc distributed specifically in the tetrahedral lattice while nickel and copper are mainly found in the octahedral lattice according to their preference to form inverse spinels. Taking into account these aspects the theoretical lattice parameters was calculated using the cationic distribution and equations described below for a better comparison with experimental values calculated using Bragg’s relation. The lattice parameters can help explain the variations in crystallite size, indicating changes in the crystal lattice according to composition and cationic radius of the metals present in the samples.
5
Fig. 2. Crystallite size calculated from XRD date in relation to increasing metal (a) and overall variation (b)
The assumed cationic distribution places the zinc in the tetrahedral lattice, whilst nickel and copper are distributed in the octahedral lattice with equivalent iron redistribution. The generic cationic formula is presented in eq. 1 where n,c,z correspond to the metal contents in the formula NinCucZnzFe2O4 (Shannon, 1976; Tatarchuk et al., 2017). (1) Round parentheses indicate metals in the tetrahedral lattice Square parentheses indicate metals in the octahedral lattice
The radius of tetrahedral and octahedral sites where calculated using eq. 2 and 3. The Shannon cationic radiuses of the metals in the appropriate coordination were used for calculations. The theoretical lattice parameter was calculated in agreement to eq. 4 considering the anionic radius of oxygen to be 1.40 Å (Shannon, 1976). (2) (3) (4)
The graphical representation of experimental and theoretical lattice parameter values (fig. 3.) confirms the composition of the ternary system, even in the absence of elemental analysis with no outstanding values as in the case of crystallite size. The lattice parameter varies between 8.420 Å, for the nanoparticles containing maximum amount of zinc, and 8.352
6
Å, slightly smaller than the theoretical value of 8.366 Å. The observed variations confirm that the assumed cationic distribution was correct and that the larger tetracoordinated zinc cations induce an increase in lattice parameter and equivalent crystallite size reduction as suggested by other authors (Su et al., 2009; Yu et al., 2006).
Fig. 3. Variation of experimental (a) and theoretical (b) lattice parameter
Fourier transformed infrared spectroscopy was additionally used to investigate the changes in the crystal lattice as a complementary method to XRD. The FT-IR spectra recorded in the 600-250 cm-1 domain offers information regarding the changes in the crystal lattice with composition by specific peak displacements, which can be assigned to corresponding bonds. The deconvolution of the spectra presents four major peaks in the investigated region which can be assigned in accordance to bond length. Taking into account that bond length varies in the order RFe(Td) < RZn(Td) < RFe(Oh) < RNi(Oh) < RCu(Oh), similarly to bond strength, we can assume that the observed vibrations can be assigned in the same order. One exception is the case of Ni(Oh)-O and Cu(Oh)-O which cannot be differentiated even after deconvolution and are observed as a single peak. The first vibration, in the 580-550 cm-1 region, corresponding to tetracoordinated iron cations, shifts toward larger wavenumbers in the nanoparticles containing increasing amounts of zinc (fig. 4c.) when the insertion of larger zinc cations induces the shrinkage of Fe-O bonds. In the compositions containing constant or decreasing amounts of zinc the displacement is towards smaller wavenumbers due to the cumulative effect of nickel and copper cations (Shannon, 1976; Virlan et al., 2016).
7
The second most intense displacements can be observed for octahedral iron (ν3). In this case the bond length is affected by both the octahedral nickel and copper (figures 4a. and 4b.) as well as tetrahedral zinc (figure 4c.). The most intense changes are induced by the increasing amounts of copper and zinc, the larger cations, with corresponding displacements toward smaller wavenumbers (Virlan et al., 2017, 2016). Minor changes can be observe for the vibrations of divalent metals in both tetrahedral and octahedral sites (480-440 cm-1 and 330-310 cm-1). The displacements can be similarly explained and are in general correlated to the cationic radius of the metals present in the sublattices. The overall variations are mainly induced by changing amounts of copper and zinc which are the largest cations. The variations can also be correlated with the lattice parameter, especially in the tetrahedral sublattice. Displacements of the bands towards larger wavenumber can be observed with decreasing zinc content and corresponding decrease in lattice parameter (Virlan et al., 2017).
Fig. 4. FT-IR spectra grouped by increasing content in nickel (a), copper (b), zinc (c)
The morphology of the samples and elemental composition were investigated using scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEMEDX). The obtained images indicate the formation of large agglomerates due to the high calcination temperature. The formation of high density structures indicate that the calcination treatment acts a preliminary sintering stage, which may prove useful in enhancing specific electrical properties of the nanoparticles (fig. 5.).
Fig. 5. SEM images of dense agglomerates containing Ni0.5Cu0.5 Zn0.0Fe2O4 nanoparticles
8
The EDX analysis is crucial in confirming the composition of the nanoparticles in addition to X-ray diffraction and FT-IR spectroscopy, especially taking into account the complex composition of the samples. The calculated composition of the materials is very similar to the desired ratios as presented in fig. 6. Minor differences can assigned to the intrinsic error of the method, but the overall results confirm the formation of tertiary ferrites with well-established composition. The EDX results are in good agreement with the lattice parameter calculated from XRD data providing a double confirmation of the composition (Joshi et al., 2016).
Fig. 6. Theoretical and EDX composition of the nanoparticles
3.2. Magnetic properties The magnetic properties of the nanoparticles were assessed by obtaining the hysteresis loops using the vibrating sample magnetometry technique, from which the saturation magnetization and coercivity values were extracted. The hysteresis loops presented in fig. 7. suggest an apparent superparamagnetic behaviour for all samples but a closer look shows that some of the nanoparticles have small coercivity and remanent magnetization. An initial evaluation of the loops indicate that the samples containing large amounts of nickel account for larger saturation magnetization while samples with smaller amounts of zinc present ferrimagnetic behaviour. Upon closer inspection of the samples’ magnetization we can observe that the maximum value was obtained for the Ni0.5Cu0.2Zn0.3Fe2O4 formula closely followed by the samples with inverse Cu-Zn ratio and similar formulations (fig. 8.). At the opposite end, the sample containing no zinc has a minimum value for the saturation magnetization. Although
9
the main influence may be due to the presence of nickel, the main contribution can be assigned to the mean crystallite size, which varies in a very similar manner.
Fig. 7. Hysteresis loops for all samples
The similar behaviour of both saturation magnetization and crystallite size confirms that the composition has an indirect effect on the magnetic behaviour of the samples by inducing changes in the crystallite size, previously described. Due to the very small nanoparticles the size and surface effects are dominant in establishing the magnetic behaviour and the saturation magnetization of the samples. Furthermore, when analysing the coercivity and remanent magnetization of the samples we can observe that 5 samples present ferrimagnetic characterized by coercivity between 8 and 28 Oe. From the graphical representation of coercivity (fig. 8b.) we can observe that the ferrimagnetic samples are the ones containing small amounts of zinc. Once again, comparing to the variation of crystallite size, we can observe that the composition influence is indirect by changing the size with the exception of Ni0.5Cu0.5Zn0.0 Fe2O4 samples, which has large coercivity despite having minimum saturation magnetization and very small crystallite size (Kambale et al., 2010; Rath et al., 2002). This specific behaviour observed in the case of the sample containing no zinc confirms the importance of tetrahedrally distributed zinc cations which alter the overall magnetic behaviour of the samples. This specific case can be explained due to the fact that the absence of zinc corresponds to equally distributed Fe3+ cations in the two sublattices with the overall magnetic moment assigned to the presence of nickel and copper cations in the octahedral sublattice. One of the most important aspect that can be observed by comparing the coercivity and crystallite size variation is the critical diameter of the nanoparticles, marking
10
the transition from superparamagnetic to ferrimagnetic. In the present case the critical diameter of the nanoparticles appears to be in the 14 nm region with the exception of Ni-Cu ferrite which is ferrimagnetic despite the mean size of approximately 11 nm (Jadhav et al., 2009; Rath et al., 2002).
Fig. 8. Variation of saturation magnetization (a) and coercivity (b) of the samples in relation to composition
3.3. Electrical properties. The electrical behaviour of the samples represents one of the most important characteristics of mixed ferrites which have great potential in the development of humidity and gas sensors. In order for the materials to be useful in designing sensor they must have both electrical resistivity and permittivity that can be easily measured and be dependent on environmental conditions. The nanoparticles are characterized by large electrical resistivity in the 10 7-109 Ωm at 20 Hz with decreasing values towards larger frequencies (fig. S-1.). This behaviour is characteristic for most spinel ferrites in accordance to the typical Maxwell-Wagner-Debye relaxation (Petrila et al., 2016; Tudorache et al., 2016). The effective values are slightly smaller than that of similar samples obtained through the classical ceramic method suggesting the materials have good potential towards electrical component development. The studies reported by Shrotri et al. indicate increased electrical resistivity on similar compositions but the mean crystallite size and density of the samples differs significantly due the higher
11
thermal treatment of the samples, characteristic for the ceramic method (Jadhav et al., 2016; Shrotri et al., 1999). The electrical resistivity was found to decrease approximately 4 orders of magnitude with increasing frequency up to 2·107 Hz. When analysing the dependence of electrical resistivity we can observe a significant decrease with humidity, indicating their sensing capability towards water vapours and perhaps other organic components (fig. S-2). The sample Ni0.3Cu0.2Zn0.5Fe2O4 presents the largest electrical resistivity throughout the investigated domain but has moderate variation in relation to relative humidity. At the opposite end Ni0.5Cu0.4Zn0.1Fe2O4 has the smallest resistivity at small frequencies but varies significantly with humidity (Verma et al., 1999). The relative permittivity of the samples has a similar behaviour in relation to electrical current frequency (fig. S-3) and opposed behaviour in relation to humidity (fig. S-4), as it would be expected. The samples have rather small electrical permittivity with the smallest value observed once again for the Ni0.3Cu0.2 Zn0.5Fe2O4 sample, while the maximum value at 20 Hz was observed for the sample containing no zinc. The relative permittivity of the samples has classical inverse variation as electrical resistivity with the same two samples outstanding with maximum and minimum variations. Similar studies reported by Jadhav et al. using the citrate method report higher relative permittivity values, but the samples in question had a higher nickel content and cannot be easily compared. No other studies involving similar large numbers of similar composition were found. The existing references include a small number of samples and do no offer comparable results on the whole domain (Jadhav et al., 2016; Raghavender et al., 2011).
The firs evaluation towards the sensing capabilities of the nanoparticles were obtained by measuring the variations of electrical resistivity and relative permittivity in the presence of
12
water vapours. In the first case, the electrical resistivity decreases by up to two orders of magnitude for samples containing large amounts of nickel, while the relative permittivity increased up to 6 times for the Ni0.5Cu0.4Zn0.1 Fe2O4 sample, closely followed by similar nickel rich combinations. These aspects lead to a primary conclusion that zinc has an overall negative effect on the sensing capabilities of the nanoparticles in the presence of water vapours. The evaluation of fig. S-2. and S-4. offers just a preliminary assessment of their sensing capabilities and does not offer an exact measure of their sensibility. To better assess the nanoparticles’ potential the relative sensibility was calculated in relation to both resistivity and permittivity. In order to normalize the sensibility for both measuring regimes the sensibility in permittivity regime was calculated as ԐR100%/ ԐR0% while in the resistivity regime the inverse logarithmic normalization was used lg(ρ 0%)/ lg(ρ100%). In both cases, in the ternary diagrams represented in fig. 9a. and 9b. maximum sensibility is denoted by orange-red hues.
Fig. 9. Sensitivity of the samples in resistivity (a) and relative permittivity (b) measuring mode and morphological characteristics of the samples (c, d)
In permittivity sensing mode we can observe enhanced sensibility for samples contained maximum amount of nickel and similar amount of copper and zinc, which is the Ni0.5Cu0.2Zn0.3 Fe2O4 and Ni0.5Cu0.4Zn0.1Fe2O4 samples. Comparing the variation of sensibility with the variation of crystallite size we can deduce a similar influence as in the case of magnetic properties, with larger particles presenting increased sensibility. Although in general smaller crystallite size means larger active surface and better adsorption of the measured species in our case the variations are rather small and compensated by the agglomeration observed by electron microscopy. In this case the crystallite size influence can be assigned to
13
the classical interparticle conduction mechanism based on electron or hole ¨hoping¨ and the formation of the Schotky barrier which is strongly dependent on the agglomeration of the nanoparticles (Mirzaei et al., 2016). In resistivity sensing mode we can observe similar behaviour with increase sensibility for nickel rich samples but the maximum sensibility in this case is assigned to the Ni0.4Cu0.4Zn0.2 Fe2O4 sample. In this case conduction mechanism is more evident as the sensitivity can be correlated with the sample density as calculated from XRD data. In other words the increase in density generates a closer packing of the nanoparticles in the agglomerates observed by electron microscopy and subsequent reduction in the Schotky barrier. The overall sensing capabilities of the nanoparticles are strongly dependent on the nanoparticles composition and are favoured by the presence of nickel due to the prevalence of p type conduction generated by the presence of nickel cations as opposed to samples containing large amount of zinc or copper which rely on n type conduction. Furthermore the sensibility of the samples is strongly influenced by morphological parameters as crystallite size and density, which strongly affect the electrical properties of the samples, as in most cases. These parameters can be subsequently modified and the present study offers a preliminary evaluation of the materials.
4. Conclusions The co-precipitation method described offers a simple and efficient approach to obtaining large amounts of pure ferrites with little influence over their morphology as confirmed by XRD data. The composition of the nanoparticles was confirmed by both the lattice parameter values and elemental analysis. The calculated crystallite size and density are strongly dependent on the composition of the nanoparticles. The presence of zinc induces the 14
largest changes in crystallite size and lattice parameter owing to its larger cationic volume. At low zinc content the magnetic behaviour changes from superparamagnetic to ferrimagnetic, most likely due to the larger crystallite size. The critical diameter marking this transition was identified in the 14 nm region. All the samples are characterized by large electrical resistivity and small relative permittivity. The electrical resistivity has a typical variation to current frequency with small differences between the samples. The relative permittivity has a similar behaviour but indicates a small number of samples with outlying changes. The electrical properties of the materials are strongly dependant to the presence of water vapours, suggesting they can be used for sensor development. The most sensitive samples contain large amounts of nickel and copper suggesting the important of hole conduction (p-type conduction) specific for the presence of nickel. Crystallite size and density also play a major role in determining the electrical behaviour of the samples suggesting subsequent calcinations can modify the microstructure and sensing capability of the samples. The study confirms the efficiency of the method and points towards nickel rich samples as potential sensing materials after appropriate treatments.
The authors have no conflicts of interest to declare. Acknowledgements: This work was partially funded by NanoQFerrox-Syst project approved by MENCS Romania and IUCN Dubna. Tiberiu Roman is gratefully acknowledged for SEM-EDX measurements.
15
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Supplemental information
Fig. S-1. Electrical resistivity dependence on current frequency
Fig. S-2. Electrical resistivity dependence on relative humidity
Fig. S-3. Relative permittivity dependence on current frequency
Fig. S-4. Relative permittivity dependence on relative humidity
19
245–252.
List of figures
Fig. 1. Diffractograms for obtained samples, grouped by increasing content in nickel (a), copper (b), zinc (c) Fig. 2. Crystallite size calculated from XRD date in relation to increasing metal (a) and overall variation (b) Fig. 3. Variation of experimental (a) and theoretical (b) lattice parameter Fig. 4. FT-IR spectra grouped by increasing content in nickel (a), copper (b), zinc (c) Fig. 5. SEM images of dense agglomerates containing Ni0.5Cu0.5 Zn0.0Fe2O4 nanoparticles Fig. 6. Theoretical and EDX composition of the nanoparticles Fig. 7. Hysteresis loops for all samples Fig. 8. Variation of saturation magnetization (a) and coercivity (b) of the samples in relation to composition Fig. 9. Sensitivity of the samples in resistivity (a) and relative permittivity (b) measuring mode and morphological characteristics of the samples (c, d)
Fig. S-1. Electrical resistivity dependence on current frequency Fig. S-2. Electrical resistivity dependence on relative humidity Fig. S-3. Relative permittivity dependence on current frequency Fig. S-4. Relative permittivity dependence on relative humidity
20
Fig. 1. Diffractograms for obtained samples, grouped by increasing content in nickel (a), copper (b), zinc (c)
Fig. 2. Crystallite size calculated from XRD date in relation to increasing metal (a) and overall variation (b)
21
Fig. 3. Variation of experimental (a) and theoretical (b) lattice parameter
Fig. 4. FT-IR spectra grouped by increasing content in nickel (a), copper (b), zinc (c)
22
Fig. 5. SEM images of dense agglomerates containing Ni0.5Cu0.5 Zn0.0Fe2O4 nanoparticles
Fig. 6. Theoretical and EDX composition of the nanoparticles
Fig. 7. Hysteresis loops for all samples
23
Fig. 8. Variation of saturation magnetization (a) and coercivity (b) of the samples in relation to composition
Fig. 9. Sensitivity of the samples in resistivity (a) and relative permittivity (b) measuring mode and morphological characteristics of the samples (c, d) Electronic supplemental material 24
Fig. S-1. Electrical resistivity dependence on current frequency
Fig. S-2. Electrical resistivity dependence on relative humidity
25
Fig. S-3. Relative permittivity dependence on current frequency
Fig. S-4. Relative permittivity dependence on relative humidity
26