Synthesis, Characterization and Performance Evaluation of Magnetic Nanostructured CoFe₂O₄ for Adsorption Removal of Contaminant Heavy Metal Ions

Abstract

Engineering magnetic cobalt ferrite (CFO) nanomaterials for environmental remediation is difficult due to regeneration (without scarifying the magnetic properties), morphology with controlled size and shape, large-scale production, and thermochemical stability. Water management globally has struggled to remove hazardous heavy metals from water environments. We show an efficient, cost-effective, and low-temperature way to make highly nanocrystalline, regenerated inverse spinel CFO nanoparticles (NPs) and nanostructured CFO microgranules with improved magnetic properties that could be used to remove heavy metal ions (Pb⁺²) from aqueous solutions without harming the environment. Magnetic investigations for CFO NPs reveal a saturation magnetization (M_S) of 3.09 μ_B/F.U. at 10 K, close to the expected value of a perfect inverted CFO structure (3.00 μ_B/F.U.). For CFO microgranules, the M_S is 5.62 μ_B/F.U. at 10 K, which is much higher than the bulk counterpart and nearly twice that of CFO NPs. Adsorption studies show that both magnetic adsorbents adsorb Pb⁺² ions through a multilayer mechanism, as critically analyzed under the pseudo-first-order, pseudo-second-order, Elovich, Bangham’s pore diffusion, and intraparticle diffusion models. CFO NPs and nanostructured CFO microgranules achieved 97.76% and 77.02% clearance efficiency, respectively.

1. Introduction

Inverse spinel cobalt ferrite (CoFe₂O₄) NPs feature extraordinary cubic magnetocrystalline anisotropy (1.8−3.0 × 10⁶ erg/cm³) and tunable electrical characteristics, making them the focus of ongoing scientific investigation and technological applications [1, 2]. The CoFe₂O₄ NPs are studied experimentally for synthesizing, characterization, and applications in biomedical, electronics, memory devices, catalysis, high-performance microwave absorbers, and magnetic resonance imaging studies [3, 4, 5, 6, 7, 8, 9, 10]. Magnetic CoFe₂O₄ NPS has currently grabbed the attention of the scientific and research community for its beneficial applications in environmental protection, particularly for contaminant and heavy metal ion adsorption [11, 12, 13, 14]. Globalization, fast industrialization, urbanization, and population growth have polluted water, air, and soil. Drinking clean water is the most practical issue. Most chemical, electronics, and energy/power companies generate wastewater with hazardous metal ions. Heavy metal ions are persistent water pollutants [15, 16, 17]. Water pollution with hazardous metal ions (Cr³⁺, Ni²⁺, Co²⁺, Cu²⁺, Cd²⁺, Ag²⁺, Hg²⁺, Pb^2+, and As²⁺) is a major environmental and public health problem [18]. Heavy metals accumulate in the environment and cause heavy-metal toxicity. Thus, chemical, physical, and biological techniques have been devised to reduce pollution [19]. Among these processes, adsorption is one of the most widely used chemical processes for removing heavy metal ions and is considered easy to operate and cost-effective [15]. Until now, many adsorbents have been used to remove heavy metal ions, [20, 21, 22] and hence, the synthesis of novel adsorbents is of great interest in water treatment technology. These adsorbents are typically made of highly porous substances that provide the required surface area for adsorption [23]. Ideal adsorbent characteristics include a strong affinity for the target and a large surface area that provides numerous adsorption sites. Adsorbents should also be highly hydrothermally stable and highly resilient to severe conditions [24]. Using magnetic nanoparticles (MNPs) as adsorbents is an attractive option for overcoming the technical challenges for the reasons outlined below: Magnetic separation is regarded as a rapid, simple, and effective method for separating magnetic particles [25, 26, 27, 28]. It has been used for mining ores, analytical chemistry, and biology. As adsorbents, various magnetic materials may be used [21, 25, 26, 27, 28]. Due to their high chemical stability and modest saturation magnetization, MNPs of CFO with a cubic spinel structure have been created and used for contaminant adsorption [29]. For instance, Li et al. [30] demonstrated that the functional magnetic graphene sheets with CFO may adsorb methyl orange. Ai et al. [31] created composites out of activated carbon and CFO to remove the malachite green color from wastewater. In addition, Farghali et al. [32] prepared CFO/CNT composites for the removal of methyl green dye from aqueous solutions; however, the material displayed relatively poor adsorption capacity, perhaps as a result of the aggregation of CFO NPs on the surfaces of CNTs and the poor interactions between the CNTs and the NPs. More tweaking is required for magnetic materials to be more effective as absorbents. Such adjustments are made to create low-cost biosorbents that are amenable to large-scale pollution removal [33]. To the best of our knowledge, the preparation of magnetic CFO NPs, carbon-activated CFO composites, and surface-functionalized CFO NPs have been reported in the literature, and many coworkers have studied their dye and heavy metal ion removal from water [6, 20, 25, 26, 27, 28, 30, 31, 32]. However, the quest for more sustainable, less time-consuming, and reproducible methods for large-scale synthesis is still being pursued. In this context, we have recently reported a simple one-pot synthesis of magnetic nanostructured CFO granules via spray drying [34]. Spray drying as a method of processing is considered environmentally friendly due to its utilization of low-cost reagents and aqueous conditions. Additionally, it is easily scalable to industrial applications. Thus, we have prepared CFO of different sizes as CFO NPs (CF1) and nanostructured CFO microgranules (CF2) and characterized both samples under identical conditions. Furthermore, we address the adsorption isotherms and kinetics studies of CF1 and CF2 to separate heavy metal ions.

2. Experimental

2.1 Sample preparation

The CFO NPs and CFO microgranules were prepared using hydrothermal [35], and spray drying processes [34]. During the preparation, stoichiometric molar amounts of Co(NO₃)₂·6H₂O and Fe(NO₃)₃·9H₂O were added into DI water and stirred well. Then pH of the solution was adjusted to 12 by adding ammonia solution (25%) and a homogeneous colloidal suspension was obtained at room temperature. For the synthesis of CFO NPs, the colloidal suspension was treated under hydrothermal conditions at 180°C for 24 h. The prepared particles were separated by centrifuge in the final solution. Finally, black precipitates were dried in an oven at 100°C overnight and designated as CF1. The colloidal solution, however, was made in the manner described above and then spray dried in a laboratory spray drier (model LU-228-Labultima; Mumbai, India). A compressed air spray nozzle created droplets between 10 and 20 μm. The aspiration flow rate was 45 m³/h, and the input temperature was controlled at 170°C. The feed pump flow rate was controlled at 2 ml/min, and the atomization pressure was regulated between 2 and 2.5 kg/cm². As a further step, a glass cyclone separator was used to gather the spray-dried powder. The spray-dried powder was dark and free-flowing. The powder was then heated overnight at 400°C to produce CF2, a spray-dried CFO powder.

2.2 Characterization

Scanning Electron Microscopy (JEOL-JSM-6360) was utilized for morphology investigations. The CF1 and CF2 powder samples were subjected to Transmission Electron Microscopy (TEM) analyses using an FEI-Technai-G2-F30 microscope equipped with a Schottky field emission gun. The powder size distribution was estimated through micrograph image analysis utilizing Image-J software. A laboratory-based facility was utilized to conduct Small Angle X-ray Scattering (SAXS) experiments. The experiment involved the recording of scattered intensities I(q) as a function of the scattering vector transfer (q = (4πsinθ)/λ)), where “2θ” represents the scattering angle and “λ” represents the X-ray wavelength (λ = 0.154 nm) [36, 37]. The distance between the sample and detector was maintained at approximately 1070 mm. The powder samples’ X-ray diffraction (XRD) patterns were obtained using a Bruker-D8-ADVANCE diffractometer. Determining lattice parameters was conducted through the Rietveld refinement methodology utilizing the FULLPROF SUITE software. After this, magnetic measurements were conducted utilizing the Quantum Design Evercool II PPMS-6000 apparatus, whereby magnetic fields were incrementally applied up to 90 ± kOe at both 10 K and 300 K. The study conducted low-pressure volumetric nitrogen adsorption-desorption measurements at a temperature of 77 K, which was maintained by a low-temperature liquid nitrogen bath. The measurements were carried out using an Autosorb iQ (Quantachrome Inc., USA) gas sorption system, with pressure levels ranging from 0 to 760 torr. Outgassing was executed under dynamic vacuum conditions (10–3 Torr) for 15 hours at a temperature of 200°C until a stable weight was attained. The study employed N2 of ultrahigh purity grade (99.999%), which underwent additional purification by utilizing calcium aluminosilicate adsorbents to eliminate minute quantities of water and other impurities before conducting the measurements. Ultra-pure helium gas (99.999% purity) was utilized to conduct warm and cold free-space correction measurements for N2 isotherms. About 200 mg of samples were used for the test, and their weight was recorded before and after outgassing to ensure that all moisture had been removed.

2.3 Adsorption experiments

2.3.1 Adsorption kinetic studies

Pb(NO₃)₂ was dissolved in DI water to make 20 mg/L Pb⁺² aqueous solutions. Following that, investigations into adsorption involved combining 20 mg of magnetic adsorbents with 50 mL of heavy metal ion solutions in an aqueous medium. The pH of the solution was modified using standardized solutions of 0.1 M NaOH and 0.1 M HCl. The dispersions obtained were subjected to magnetic stirring at room temperature, and the temporal impact was assessed over a range of time intervals spanning from 5 to 300 min (specifically, 5, 10, 15, 30, 60, 90, 120, 150, 180, 210, 240, 270, and 300 min). A volume of 2 mL of solution was obtained, and the magnetic adsorbents were eliminated through the process of magnetic separation. The quantification of the Pb + 2 ion concentration was performed using atomic absorption spectroscopy (AAS) with a Varian Spectr AA-220 instrument. Eqs. (1)and (2) were utilized to compute the quantities of metal ions adsorbed per unit mass of the adsorbent and the corresponding removal efficiencies (R).

$$q_e=\frac{V\left(C_i-C_e\right)}{1000\times S}$$E1

$$R=\left(\frac{C_i-C_e}{C_i}\right)\times 100$$E2

where C_i and C_e are the concentrations (mg/L) of the metal ions in the aqueous solution before and after the adsorption period, respectively. V denotes the volume (mL) of the aqueous solution, and S represents the amount of dry adsorbent used (g).

2.3.2 Adsorption isotherm studies

The present study investigated adsorption isotherm to examine the equilibrium relationship between adsorbents and adsorbates. The study involved the acquisition of adsorption isotherms of Pb⁺² on magnetic adsorbents, and this was achieved through the dispersion of 20 mg of magnetic adsorbent into 30 mL of Pb⁺² ion solution, with varying concentrations between 20 and 1000 mg/L at ambient temperature. The dispersions were subjected to magnetic stirring under ambient conditions, and a volume of 2 mL of the resultant solution was extracted after a duration of 30 minutes. The magnetic adsorbents were extracted through magnetic separation, and the concentration of heavy metal ions was measured using atomic absorption spectroscopy.

2.3.3 Recovery and reuse

The magnetic adsorbents, loaded with Pb⁺² (20 mg), were subjected to stirring with a 0.1 M HCl solution (10 mL) at room temperature for a duration of 3 h to facilitate desorption of the metal ions. The concentration of the metal ion in the aqueous phase was determined using AAS. Subsequently, the magnetic nanoparticles (MNPs) were subjected to neutralization using a diluted solution of 0.1 M NaOH, followed by a thorough rinse with deionized water. The colloidal magnetic adsorbents were subsequently extracted through magnetic separation. The MNPs were then subjected to further adsorption processes to assess their reusability. The magnetic adsorbents were subjected to 5 cycles of adsorption and desorption.

3. Results and discussion

3.1 Morphology, microstructure, and crystal structure

The surface morphology of pristine CF1 and CF2 samples is shown in Figure 1(a, b). Nearly spherical morphology with mean particle size (D_mean) ∼19.84 nm and 111 nm, for CF1 and CF2, respectively, is evident from Figure 1. The CF2 microgranules were made up of subunit entities. When the dimension is reduced to the nanoscale, two effects are expected: (i) a significant increase in relative surface area and (ii) a significant increase in the number of atoms on the surface [38]. These effects enhance the chemical reactivity of the surface atoms of nanoparticles. Therefore, at the nanoscale, the Vander Waals forces of attraction as well as the magnetic force of attraction tend to increase, and subunit entities tend to attract each other, resulting in the assembly of subunit particles in a regular or irregular manner [38]. The size distribution (Figure 1(c,d)) for CF1 is observed to be symmetrical with small polydispersity index (σ ≤ 0.25) compared to CF2 (σ = 0.43), indicating that the CF1 NPs are well confined to a limited diameter range which is desirable for practical applications. Figure 1(c) shows the differences between calculated and observed XRD patterns as well as the crystalline phases of CF1 and CF2. A Rietveld refinement was carried out until the fitment produced a goodness factor (χ²) ∼ 1. The Rietveld refinement of XRD data confirms the single-phase cubic spinel structure of both samples, which are devoid of impurities. The obtained refinement parameters, the discrepancy factor (R_wp), expected values (R_exp), and χ² for CF1 and CF2, are (76.5, 66.20), (74.34, 64.31), and 1.06, in good accord with the reported parameters for ferrite systems [1, 34]. The presence of intense diffraction peaks indicates that both samples are crystalline. All peaks were indexed based on the structure of cubic spinel ferrite (JCPDS card number 22-1086) with a space group of Fd3m (2 2 7). However, the diffraction peaks of CF1 are relatively broad, which can be attributed to its reduced crystallite size. The average crystallite size was estimated using the integral width of the diffraction lines, Scherrer’s formula, background subtraction, and correction for instrumental broadening; For CF1 and CF2, the estimated values are ∼20.05 ± 0.05 nm and ∼ 26.41 ± 0.05 nm, respectively. The lattice parameters for CF1 (8.385 Å) and CF2 (8.373 Å) differ marginally from one another and deviate slightly from those of cobalt ferrite in bulk (8.394 Å). This could be the result of nano-size effects [1, 34]. The calculated X-ray density for CF1 and CF2 is 5.509 g/cm³ and 5.314 g/cm³, respectively. Refinement of experimental data for CF1 demonstrates an ideal inverse cation distribution as [Fe⁺³]_Tet{Co⁺²Fe⁺³}_OctO⁻²₄, whereas CF2 deviates from this ideal by transferring some Co⁺² cations to the tetrahedral A-site, and cation distribution for CF2 is, that is, [Co⁺²_(0.6875)Fe⁺³_(0.3125)]_Tet{Co⁺²_(0.3125)Fe⁺³_(1.6875)}_OctO⁻²₄. A detailed study of the structural and magnetic properties of both samples was made by first-principles calculations and was reported in our earlier work [29].

Figure 1.

Morphological study of CF1 (a) and CF2 (b) (inset shows particle size distribution), and Rietveld refined XRD patterns for CF1 and CF2 samples (c).

The TEM micrographs of CF1 and CF2 samples are shown in Figure 2(a-d). CF1 results in a spherical shape with some cubic-like morphology, as shown in Figure 2a. The average size of particles is ∼16.11 nm (shown by the red circle and cube). The high magnification image (Figure 2b) shows the CF1 samples are composed of small NPs with a spherical shape. Most NPs have a size smaller than 10 nm (range of 5.0–6.5 nm) (see inset of Figure 2b). These NPs are self-assembled in a spherical, close-packed super-lattice due to the high degree of uniformity in diameter. On the other hand, CF2 shows a quasi-spherical shape with cube morphology (Figure 2(c,d)). The average size is 22 nm, and the magnified image shows the dominance of cube morphology for CF2 (Figure 2d). The electron diffraction patterns shown in Figure 2(e,f) obtained through the selected area electron diffraction (SAED) technique exhibit diffuse rings that can be attributed to the (220), (311), (400), (511), and (440) crystallographic planes of the CoFe₂O₄ cubic structure. The manifestation of CFO’s polycrystalline character is apparent through the existence of numerous diffraction rings in the corresponding SAED patterns for both specimens. Note that the particle boundary is well defined for both the samples and an isolated cube shows (inset of Figure 2c) more clearly a size of 16.77 nm. Figure 3(a–f) show HRTEM images of CF1 and CF2 samples, respectively. The clear lattice boundary in the HRTEM image illustrates the high crystallinity of both samples. The periodic fringe spacing of (0.253–0.2256 nm), 0.22 nm, and 0.18 nm corresponds to the (311), (400), and (511) planes of cubic CoFe₂O₄ as observed for CF1 (Figure 3(a–c)). The periodic fringe spacing of 0.21 nm, 0.155 nm, 0.312 nm, 0.284 nm, and 0.25 nm corresponds to the (400), $\left(0\bar{4}\bar{4}\right)$, $\left(02\bar{2}\right)$, (220), and (311) planes of cubic CoFe₂O₄ as observed for CF2 (Figure 3(d–f)), which matches with JCPDS card no. 22-1086.

Figure 2.

TEM images of CF1 (a, b) and CF2 (c, d) along with a magnified view (inset) with a scale bar of 5 nm and SAED patterns of CF1 (e) and CF2 (f) samples.

Figure 3.

HR-TEM images of CF1 (a–c) and CF2 (d–f).

3.2 Size and confined structure

The basic size of particles, morphology, and corelation of interlocked nanostructure in terms of structure factor can be well understood using SAXS analysis.

The SAXS profile for CF1 and CF2 are shown in Figure 4(a,b). It is observed that the scattering profiles could be best represented by the following contributions:

Figure 4.

SAXS profile for CF1 (a), and CF2 (b) and the calculated size distribution are shown in (c), and (d).

$$I_{\mathit{CF}1}\left(q\right)=\sum _{i=1}^3{I}_i.\left(q\right)$$E3

and

$$I_{\mathit{CF}2}\left(q\right)=\sum _{i=1}^2{I}_i.\left(q\right)$$E4

Here, “I_i” denote the contribution to scattering intensity from “i^th” component. For an ensemble of interacting spherical particles, under local monodisperse approximation, intensity can be written as: [37]

$$I_i\left(q\right)=C_i{\int }_0^{\infty }{P}_i\left(q,R\right)S_i\left(q,R\right)R_i^6{D}_i\left(R\right)\mathit{dR}$$E5

Here, C_i is a scale factor, P_i(q, R) and S_i (q, R) signify the form factor and interparticle structure factor for the “i^th” component, respectively. Assuming the spherical shape of particles with radius R, the form factor P (q, R) is expressed as [39]:

$$P_i\left(q,R\right)={\left[\frac{3\mathit{sin}\left(\mathit{qR}\right)-\mathit{\text{qRcos}}\left(\mathit{qR}\right)}{{\left(\mathit{qR}\right)}^3}\right]}^2$$E6

D_i(R) represents the size distribution of “i^th” component and is assumed to be a normalized log-normal distribution: [40]

$$D_i\left(R\right)=\frac{N_i}{\sqrt{2\pi {\sigma }^2}R}\mathit{exp}\left[-{\left[\mathit{ln}\left(\raisebox{1ex}{$R$}\!\left/ \!\raisebox{-1ex}{$R_0$}\right.\right)\right]}^2/2{\sigma }^2\right]$$E7

where N_i represents the normalization factor, R₀ and σ (0 < σ < 1) represent the median radius and polydispersity index for the i^th distribution, respectively. As the magnetic attraction among the particles play a significant role, consideration of an attractive potential was necessary during the detailed fitting procedure and mass fractal type structure factor was considered for I₃(q) and I₂(q) contributions for CF1 and CF2, respectively. In such a case, S(q) can be represented as:

$$S_i\left(q\right)=1+\frac{D}{r_0^D}{\int }_0^{\infty }{r}^{D-3}h\left(r,\xi \right)\frac{\mathit{sin}\left(\mathit{qr}\right)}{\mathit{qr}}{r}^2\mathit{dr}$$E8

$$\text{with}\mathrm{cut}-\mathrm{off}\text{function}\mathrm{as}:h\left(r,\xi ,\alpha \right)=\mathit{exp}\left[-{\left(\frac{r}{\xi }\right)}^{\alpha }\right]$$E9

where r-dimension of individual scattering objects, ξ (x_i)- size of aggregate or cut-off length for the fractal correlations, and D-fractal dimension (1 < D < 3). The model scattering intensity, as mentioned above using Eqs. (3) and (4), was fitted to the scattering data for CF1 and CF2 using the nonlinear least square method. It was found that the mass fractal model [41] could describe the present scattering profiles when compared to the hard-sphere and sticky hard-sphere model [42]. For an attractive potential, the intensity at low q increases because of the formation of aggregated structure. Figure 4(c,d) shows the size distribution obtained from SAXS profile for the CF1 and CF2 samples while the parameters derived are summarized in Table 1. It is worth mentioning that the real space and scattering space are connected by Fourier transform; thus, the region-I, II for CF1 and region-I for CF2 of the scattering profile primarily contain information about large length scales. Similarly, region-III for CF1 and region-II for CF2 contains information about the structure and correlation at a smaller length scale (i.e., correlated nanoparticles and nano-meso-pores, etc.). In case of CF1, the scattering model suggests that the CF1 NPs are composed of spherical shape particles of median size R_o ∼ 8.33 nm with polydispersity index (σ) ∼ 0.20 (region I). This is further evident in Figure 2(a), that is, TEM images of CF1. Furthermore, region II and III shows the presence of spherical shape particles of Ro ∼ 3.03 nm and 1.15 nm with σ ∼ 0.25 and 0.17, respectively. The values agree with TEM data. The smaller value of σ manifests the monodisperse size behavior of NPs. Furthermore, structure factor analysis shows that the NPs are composed of monomer with radius ∼ 1.37 nm, fractal dimension (D) is found to be ∼2.24, which results in a maximum size of ∼4.50 nm. It is more clearly seen in the inset of image Figure 2b. In the case of CF2, as discussed above, the high magnetization value of nanostructured CFO develops strong magnetic attraction that results in stable microgranules. For CF2, the scattering model fitted using Eq. (7) suggests that the basic CF2 sample is composed of particles of mean radius ∼4.35 nm with polydispersity index ∼0.25. Note that region I cover 80% of whole q range and simultaneously, the interparticle structure factor suggests the maximum aggregate size ∼15.15 nm as seen in TEM analysis. The remaining 20% of q range suggests a mean diameter of 3.18 nm. However, the mean size of microgranules as seen from SEM micrographs is D_mean ∼ 111 nm and this suggests that the limitation of accessible low “q” range in SAXS. However, SAXS profile strongly suggests that the CF2 sample is nanostructured microgranules. Moreover, it was observed that characteristic dimension of individual scattering objects (r₀) and maximum aggregate size (x_i) value is smaller (r₀ ∼ 1.37, x_i = 4.50) for CF1 compared to CF2 (r₀ ∼ 3.98, x_i = 15.15). Note that the size obtained from TEM is well agreed with the SAXS analysis.

Sample	Region	R0 (nm)	σ (nm)	r0 (nm)	xi (nm)	D	Α
CF1	I	8.33	0.20	1.37	4.50	2.24	2.95
	II	3.03	0.25
	III	1.15	0.17
CF2	I	4.35	0.25	3.98	15.15	2.25	2.27
	II	1.54	0.25

Table 1.

Structural parameters obtained from small angle scattering experiments.

3.3 Magnetism

Figure 5 shows the experimental magnetization (M-H) loops for CF1 and CF2 measured at 10 K and 300 K temperatures, and the magnetic parameters obtained are enlisted in Table 2. The observed value of M_S at 10 K is ∼73.69 emu/g (3.09 μ_B/F.U.) and 133.79 emu/g (5.62 μ_B/F.U.) for CF1 and CF2, respectively. Considering the formula [Co⁺²_(1-x)Fe⁺³_(x)]_Tet{Co⁺²_(x)Fe⁺³_(2-x)}_OctO⁻²₄ to describe the cation distribution in the spinel structure of CFO and assuming that Fe³⁺ and Co²⁺ ions have magnetic moment of 5 μ_B and 3 μ_B, respectively, and the inversion parameter is obtained to be δ ∼ 0.97 and 0.345 for CF1 and CF2, respectively. This value of inversion parameter indicates that the crystal structure of CF1 is very close to the inverse spinel and non-stoichiometric inverse spinel behavior for CF2 sample. Notice that, for CF1, the experimental M_S value is close to the theoretical value of ideal inverse CFO structure (∼3.00 μ_B/F.U.). For CF2, M_S is remarkably high compared to the bulk counterpart, and almost double as compared to M_S value reported for CFO NPs [2, 3]. This remarkable increased M_S value for CF2 may be attributed to the non-stoichiometric cations (Fe⁺³, Co⁺²) distribution among the octahedral and tetrahedral sites as compared to ideal spinel structure [Fe⁺³]{Co⁺²Fe⁺³}O⁻² as predicted by the XRD analyses.

Figure 5.

M-H curve for CF1 (a, b) and CF2 (c, d) at 10 K and 300 K.

Sample	Temperature (K)	Saturation magnetization(MS) (emu/gm)	Saturation magnetization (MS) (μB/F.U.)	Remanent magnetization (Mr) (emu/g)	Coercivity (HC) (Oe)	Squarness ratio Mr./Ms
CF1	300	57.313 ± 0.287	2.407 ± 0.012	5.966 ± 0.030	109 (± 0.109)	0.104
	10	73.687 ± 0.368	3.095 ± 0.015	48.608 ± 0.243	5575 (± 5.575)	0.660
CF2	300	123.09 ± 0.615	5.170 ± 0.026	40.450 ± 0.202	1410 (± 1.410)	0.329
	10	133.79 ± 0.669	5.620 ± 0.028	96.50 ± 0.483	2118 (± 2.118)	0.721

Table 2.

Magnetic parameters obtained from M-H measurements at 10 K and 300 K.

3.4 BET analysis

Notably, gas absorption (BET) techniques are appropriate for probing surface areas in porous materials. As illustrated in Figure 6, N₂ adsorption-desorption isotherms were measured to ascertain the absorptive capacity of magnetic adsorbents for gas absorption. According to the IUPAC classification observed for CF1 and CF2, the N2 gas adsorption-desorption isotherm exhibits a type IV curve and an H3 hysteresis loop. This behavior indicates that mesopores predominate [43]. The hysteresis of type H3 reveals the random distribution and interconnection of pores. Because adsorption and desorption isotherms exhibit distinct behaviors to the pore network at a relative pressure of 0.45 (for N₂ at 77 K), these pore properties significantly influence the desorption isotherm more than the adsorption isotherm. A BET surface area measurement was performed to ascertain the prepared material’s surface area. Using the BET multipoint method, the specific surface area of CF1 and CF2 was determined to be 57.66 m²/g and 24.67 m²/g, respectively. Thus, both magnetic adsorbents are porous, and it is noteworthy that the average pore size of CF1 is more significant than that of CF2 (7.347 nm vs. 4.994 nm). It is evident that the specific surface area, pore availability, and affinity between the adsorbate and adsorbent significantly influence the adsorption capacity, which indicates the presence of active sites for the absorption of additional Pb⁺² ions.

Figure 6.

BET hysteresis curve during adsorption and desorption for CF1 and CF2.

3.5 Adsorption studies

3.5.1 Adsorption kinetics studies

Figure 7(a) shows the effect of time on the Pb⁺² ions concentration at room temperature (RT) during adsorption experiments and it is seen that the Pb⁺² concentration decreases with increasing time for the magnetic adsorbents. Although, Pb⁺² concentration decreases with a relatively slow rate for CF2 compared to CF1. However, Pb⁺² concentration decreases rapidly, up to 3.70 mg/L within 90 mints compared to initial Pb⁺² concentration (i.e., 20 mg/L) when CF1 was used for adsorption. Whereas for CF2 a rapid decrease of Pb⁺² concentrations up to 13.40 mg/L was observed within 30 mints compared to initial Pb⁺² concentration as 20 mg/L. Figure 7(b) shows the effect of time on the adsorption capacity of Pb⁺² at RT. In case of nano-adsorbent, at the beginning (up to 90 min), the rate of adsorption is relatively fast and further the rate increases gradually and finally slows down to attain equilibrium indicating a decrease in the number of available sites as the adsorption proceeds. On the other hand, for CF2 the rate of adsorption was observed to be fast up to 30 mints and the adsorption capacity increases from 16.51 to 35.76 mg/g with a relatively slow rate and then attains equilibrium. Moreover, the adsorption process reaches equilibrium within 210 mints and 240 mints for CF1 and CF2, respectively. Figure 7(c) depicts the time-dependent removal efficiency of Pb⁺² ions. Here, 50% of the Pb⁺² ions were completely absorbed in the first 30 min by CF1 compared to the initial concentration (20 mg/L) of Pb⁺² as the removal efficiency was observed to be 51.09%; whereas CF2 attains 49.51% removal efficiency in 270 min. It is noticeable that the maximum removal efficiency was observed to be 97.76% and 77.02% for CF1 and CF2, respectively. And, the maximum adsorption capacity (q_e,exp) was observed to be 48.88 mg/g and 38.51 mg/g for CF1 and CF2, respectively. It demonstrates that the adsorption capacity of CF1 is slightly more compared to CF2. This more adsorption observed for CF1 could be attributed to the larger specific surface area of porous adsorbents that offers the higher surface energy for adsorbing heavy metal ions. Since adsorption is particle diffusion controlled, an increase in pore increases the number of accessible sites, hence increases the amount of adsorbate (Pb⁺²) on the adsorbent [44, 45, 46]. The effect of particle size on adsorption of the metal ions from aqueous solutions has been reported [46] and our results are well agreed.

Figure 7.

Effect of time on the Pb concentration (mg/L) (a), Adsorption capacity (q_t) (b) and removal efficiency (R %) (c), during adsorption process for magnetic adsorbents.

To understand the detailed adsorption mechanism, its kinetics are analyzed by a few models based on the adsorption equilibrium. The experimental data were fitted to the pseudo-first-order [47], pseudo-second-order [47], intraparticle diffusion, Bangham’s pore diffusion, Boyd kinetic model, and Elovich models; these equations are shown in Table 3.

Model	Linear equation	Plot	Calculated coefficient
Pseudo-first-order a	$\mathit{log}\left(q_e-q_t\right)=\mathit{log}\left(q_e\right)-\frac{k_{1}t}{2.303}$	log (qe – qt) vs. (t)	k1 = −slope× 2.030 qe = e^intercept
Pseudo-second-order b	$\left(\frac{t}{q_t}\right)=\left(t\right)\left(\frac{1}{q_e}\right)+\left(\frac{1}{k_2{q}_e^2}\right)$	(t/qt) vs. (t)	K2 = slope^2/intercept qe = slope^−1
Intraparticle diffusion c	$q_t=k_i{t}^{0.5}+C$	(qt) vs. (t^0.5)	Ki = slope C = intercept
Bangham’s pore diffusiond	$\mathit{log}\left[\mathit{log}\left(\frac{C_i}{C_i-m{q}_t}\right)\right]=\mathit{log}\left(\frac{K_{b}m}{2.303V}\right)+\mathit{\text{αlog}}\left(t\right)$	log [log(Ci/Ci-mqt)] vs. log(t)	α = slope Kb = e^intercept × 2.030×V/m
Boyd kinetice	$F=1-\frac{6}{{\pi }^2}\mathit{exp}\left(-B_t\right)$; $B_t=-0.4977-\mathit{ln}\left(1-F\right)$	—	—
Elovich modelf	$q_t=\mathit{\beta ln}\left(\mathit{\alpha \beta }\right)+\beta \left(t\right)$	qt vs. ln(t)	β = slope; Intercept = β ln(αβ)

Table 3.

Mathematical equations applied in the kinetic adsorption study of Pb⁺² ions onto CF1 and CF2.

^{a, b}

Where k₁(mint)⁻¹_, k₂ (g.mg ⁻¹mint⁻¹) are the pseudo-first- and second-order rate constant and q_e (mg/g) and q_t (mg/g) are the adsorption capacity of Pb⁺² onto adsorbent at equilibrium and at a given contact time t (min), respectively.

^c

k_i(mg/g.min^1/2) is the intraparticle diffusion rate constant and C(mg/g) is a constant that gives an idea about the thickness of the boundary layer.

^d

C_i is the initial adsorbate concentration in liquid phase, m is the weight of the adsorbent, q_t(mg/g) same as described above, V is volume of the solution, and α (α < 1), K_b(L/gm) are constants.

^e

F is the fraction of solute adsorbed at different contact time t and parameter B_t is a mathematical function of F, which is given by F = q_t/q_e.

^f

α(mg /g min) is the initial sorption rate and the parameter β (mg/g min^1/2) is related to the extent of surface coverage and activation energy for chemisorption.

Figure 8(a,b) shows the pseudo-first- and second-order kinetic model plot for CF1 and CF2, from which k_1, k_2, and q_e,cal values are extracted and enlisted in Table 4. It has been observed that for both the adsorbents, the correlation coefficient (R²) calculated using pseudo-second-order model, was found to be larger (0.9948 ± 0.0058 and 0.9413 ± 0.0058) than those observed for pseudo-first-order model (0.9541 ± 0.145 and 0.9192 ± 0.145). The experimental (q_e,exp) and calculated (q_e,cal) values of q_e using pseudo-first-order model do not match with each other, whereas the values are in good agreement with each other using pseudo-second-order for CF1. On the other hand, the q_e,exp and q_e,cal values using pseudo-first-order and second-order-model match with each other for CF2 absorbent. Thus, based on R² value, we can predict that the overall adsorption of Pb²⁺ onto magnetic adsorbents followed the pseudo-second-order model. Moreover, for CF1, values of q_e,cal and q_e,exp were found to be larger (54.95 mg/g and 48.88 mg/g) than those observed for CF2 adsorbents (40.00 mg/g and 38.51 mg/g) as per the pseudo-second-order model.

Figure 8.

Lagergren pseudo-first-order (a), Lagergren pseudo-second-order (b), Bangham’s pore diffusion (c), intraparticle diffusion (d) model, and plots of calculated Boyd parameter (B_t,cal) versus time (t) (e), experimental Boyd parameter (B_t,exp) versus time (t) (f), and Elovich model plot and (g) for CF1 and CF2 adsorbents.

Pseudo-first-order model
Sample	k1 (×10^−3) (mint)^−1	qe,cal (mg/g)	R^2
CF1	8.98	41.69	0.9541
CF2	9.67	38.01	0.9192
Pseudo-second order model
Sample	qe,exp (mg/g)	k2 (×10^−4) (g.mg ^−1mint^−1)	qe,cal (mg/g)	R^2
CF1	48.88	5.90	54.95	0.9948
CF2	38.51	6.18	40.0	0.9413
Intra-particle diffusion model
Sample	ki (mg /g min^1/2)		C (mg/g)	R^2
CF1	2.94		7.85	0.9308
CF2	1.96		4.58	0.9891
Bangham’s pore diffusion model
Sample	Α		kb (L/gm)	R^2
CF1	0.73		7.48	0.9779
CF2	0.49		9.50	0.9611
Elovich model
Sample	α (mg /g min)		β (mg/g min ^1/2)	R^2
CF1	0.040		10.70	0.9847
CF2	0.125		7.30	0.9127

Table 4.

Characteristics of the pseudo-first-order, pseudo-second-order, intra-particle diffusion, Bangham’s pore diffusion, and Elovich kinetics model, along with the correlation coefficient (R²) for the adsorption of Pb⁺² (initial concentration C_i = 20 mg/L) onto the CF1 and CF2 adsorbents.

From a mechanistic viewpoint, it is crucial to identify the steps involved during the adsorption process. Thus, the intraparticle diffusion model [43] has been used to identify the steps involved during adsorption process. Figure 8(c) shows the intraparticle diffusion model plot for CF1 and CF2 and here, the non-zero value of C implies that the adsorption mechanism is governed by both film diffusion and intra-particle diffusion [47, 48]. In the experiment for CF1, the plot shows three main portions: initial linear but rapid increase (15%) portion, steep portion (70%), and a later horizontal potion (15%), whereas for CF2 adsorbents Figure 8(c) shows two distinct portion as first linear steep portion (90%) and later horizontal portion (10%). In the case of CF1, the initial rapid portion attributed to boundary layer diffusion effect or external mass transfer effect. The boundary layer effect is more dominant in CF1 compared to CF2 as C value is large (see Table 5). However, the steep linear portion implies that the intraparticle diffusion could also be the main sorption mechanism in this part of plot. The later portions were slow and controlled by the equilibrium diffusion mechanism, which occurred when the rate of sorption and desorption are insignificant. Moreover, 90% of the linear steep region observed for CF2 indicates that intraparticle diffusion is more dominant for CF2. In addition, the rate of adsorption process (k_i) of CF1 (2.935 ± 0.252 mg.g⁻¹mint^−0.5) is larger than the CF2 (1.096 ± 0.059 mg.g⁻¹mint^−0.5). The diffusion rates (Table 5) decrease with the increase in contact time for both adsorbents. This is because the heavy metal ions diffuse into the inner structure of the adsorbent and the pores for diffusion become smaller. The free path of the ions in the pore decreases and the ions may also be blocked. Here, the deviation of straight lines from the origin (Figure 8(c)) was observed for both the adsorbents. It may be due to the difference in rate of mass transfer in the initial and final stages of adsorption. Further, such deviation of straight line from the origin indicates that the pore diffusion is not the sole rate-controlling step.

Model	Linear equation	Plot	Calculated coefficient
Langmuira	$\left(\frac{C_e}{q_e}\right)=\left(\frac{1}{b{q}_m}\right)+\left(\frac{C_e}{q_m}\right)$	(Ce/qe) vs. (Ce)	b = slope/intercept qm = slope^−1
Freundlichb	$q_e=k_f{C}_e^{1/n}$	log (qe) vs. log (Ce)	kf = 10^intercept n = slope^−1
Tempkinc	$q_e=k_1\mathit{ln}\left(k_2\right)+k_1\mathit{ln}\left(C_e\right)$	ln(Ce) vs. (qe)	k1 = slope^−1 k2 = 10^intercept

Table 5.

Mathematical equations applied in adsorption isotherm study of Pb⁺² ions onto CF1 and CF2 samples.

^a

q_e(mg.g⁻¹) is the amount of Pb⁺² ions adsorbed per unit mass of magnetic adsorbent, C_e is the concentration of adsorbate in the solution at equilibrium, q_m(mg.g⁻¹) is the maximum uptake per unit mass of adsorbent or monolayer adsorption capacity, and b(g.mg⁻¹) is the Langmuir constant related to the adsorption energy.

^b

where C_e and q_e have the same meaning as in the Langmuir isotherm, k_f(mg.g⁻¹) is the Freundlich constant related to the adsorption capacity (mg/g), and n is the empirical parameter representing the energetic heterogeneity of the adsorption sites (dimensionless).

^c

k₁(L/g) is related to the heat of adsorption (L/g), and k₂ is the dimensionless Tempkin isotherm constant.

The experimental data were further analyzed to determine the slow step occurring in the present system using Bangham’s pore diffusion model [48] and Bangham’s pore diffusion model plots are shown in Figure 8(d). It is found to be linear with R² of ∼0.9779 and ∼ 0.9611 for CF1 and CF2, respectively, which confirm that the adsorption is pore-diffusion controlled for both the adsorbents. Pore diffusion is more dominant for nano-adsorbent compared to micro-adsorbent as pore presence were already seen in BET isotherm curve.

Additionally, the actual rate-controlling step involved in the adsorption process was determined by Boyd kinetic model [49]. Using the q_e,exp and q_e,cal values, the corresponding experimental Boyd (B_t,exp) and calculated Boyd (B_t,cal) parameters were obtained and as shown in Figure 8(e,f), the plots are linear but do not pass through the origin, and the R² of linear fitting indicates that the external mass transport mainly governs the rate-limiting process of adsorption of Pb⁺² onto the magnetic adsorbents and it is more prominent in magnetic nano-adsorbent [50].

Moreover, the data were further analyzed using the Elovich model [51] and the linear form of Elovich model is presented in Table 3. The unknown constants α and β were obtained and listed in Table 4. Elovich model plot is shown in Figure 8(g). The kinetic curve of adsorption of Pb⁺² was demonstrated to be good fitting with the Elovich model for CF1 and CF2 as the R² is observed to be near to unity. However, from Table 4 it is seen that the diffusional rate-limiting is more prominent for the nano-adsorbent compared to nanostructured microgranules adsorbent during the adsorption process.

3.5.2 Adsorption isotherm studies

The Langmuir and Freundlich models were utilized to fit the experimental data, as presented in Table 5. These equations are widely employed in the analysis of adsorbate-adsorbent interactions. The Langmuir adsorption model [52] postulates the existence of a maximum capacity for adsorption, which corresponds to a state of complete saturation of the adsorbent surface by a monolayer of adsorbate molecules. It is commonly assumed that the process of adsorption occurs at distinct and uniform sites located within the absorbent material. Upon occupation of a site by Pb⁺² ions, subsequent adsorption at said site is precluded. The determination of q_m and b values is achieved through the computation of the slope and intercept of the linear graph of (C_e/q_e) against (C_e), as illustrated in Figure 9(a). The resulting parameters are presented in Table 6. The correlation coefficient of the isotherm exhibits a moderate level of association. It is possible to predict the effectiveness of the adsorption process using the dimensionless equilibrium parameter, R_L [53]. It is calculated as R_L = 1/(1+b C_i), where b and C_i represent the adsorption constant and initial concentration (mg/L) of the solute, respectively. According to literature [54], the isotherm’s shape can be categorized as unfavorable (R_L > 1), linear (R_L = 1), favorable (0 < R_L < 1), or irreversible (R_L = 0), based on the value of R_L. Figure 9(b) displays the R_L values that were computed for varying initial concentrations of Pb⁺². The range of R_L values falls within the interval of 0 to 1, thus indicating the favorable nature of the adsorption process [55]. Monolayer adsorption capacity was observed to be high (1951.98 mg/g) for CF2 compared to CF1 (1382.74 mg/g). The Langmuir isotherm plots were not perfectly linear as evidenced by the moderate R² values (see Table 6), which represent that Pb⁺² ions adsorption is not completely homogenous monolayer surface adsorption process for both the magnetic adsorbents. Thus, isotherm data were further analyzed using Freundlich isotherm (see Figure 9(c)), which [50, 56] assumes that adsorption occurs on a heterogeneous surface through a multilayer adsorption mechanism and that the adsorbed amount increases with concentration according to the Freundlich isotherm. According to Table 6, it is observed that the R² values are very close to 1, which indicates that the experimental adsorption isotherms are very well modeled by the Freundlich equation compared to Langmuir model. Meanwhile, the value of n for Freundlich model is greater than 1, indicating the adsorption of Pb⁺² also exhibits a favorable shape. Adsorption is considered satisfactory when the Freundlich constant n takes values within the range 1–10. Furthermore, Tempkin plots for magnetic adsorbents are shown in Figure 9(d) and Tempkin isotherm constant are listed in Table 6. The values of R² are greater than the data modeled by Langmuir isotherm but less than the Freundlich isotherm. Importantly, the heat of adsorption of microgranules is higher (150.31 L/g) than the nano-adsorbent (135.31 L/g).

Figure 9.

Equilibrium Langmuir isotherms of Pb⁺² ions by CF1 and CF2 adsorbents (a), calculated R_L values at different initial concentrations of Pb⁺² for CF1 and CF2 (b), equilibrium Freundlich isotherms of Pb⁺² ions by CF1 and CF2 (c), and Tempkin adsorption isotherm plots for CF1 and CF2.

Langmuir
Sample	b × 10^−3 (g.mg^−1)	qm (mg.g^−1)	R^2
CF1	1.360	1382.74	0.727
CF2	0.917	1951.98	0.730
Freundlich
Sample	kf (mg.g^−1)	N	R^2
CF1	3.30	1.21	0.998
CF2	2.67	1.14	0.999
Tempkin
Sample	k1 (L/g)	k2	R^2
CF1	135.31	0.069	0.7893
CF2	150.31	0.063	0.7764

Table 6.

Characteristics of Langmuir, Freundlich, and Tempkin isotherm for the adsorption of Pb⁺² onto the CF1 and CF2 along with the R² value.

From the above discussion, the overall adsorption of Pb⁺² ions occurs through a multilayer adsorption mechanism for both the magnetic adsorbents. Moreover, the presence of adsorbate-adsorbate interactions was also observed during Pb²⁺ adsorption process, as checked by Tempkin isotherm. However, on the basis of R² value, the order of kinetic model followed for nano-adsorbent is as follows for the experimental data; Pseudo-second-order > Elovich model > Bangham’s pore diffusion model > pseudo-first-order > intraparticle diffusion model. On the other hand, for microgranules adsorbent the order of kinetic model is as follows; intraparticle diffusion model> Bangham’s pore diffusion model> pseudo-second-order> pseudo-first-order> Elovich model. Importantly, monolayer adsorption capacity observed to be high (1951.98 mg/g) for CF2 compared to CF1 (1382.74 mg/g) along with higher heat of adsorption for CF2 (150.31 L/g) than the CF1 (135.31 L/g), which suggest that the adsorption capacity of nanostructured CFO microgranules can be enhanced further by various modifications.

3.5.3 Regeneration study

In five consecutive cycles, the regeneration and re-adsorption of magnetic adsorbents showed a 99% regeneration rate, indicating that donor sites on the surface of magnetic adsorbents and Pb⁺² ions are reversible. In conclusion, magnetic absorbents and Pb⁺² ions did not form strong bonds. Thus, the whole adsorption and desorption process does not include chemical redox reactions. Interestingly, the adsorption capacity of CF2 with greater size drops significantly during desorption with the rise in cycle number due to its low adsorption rate and small capacity.

Figure 10 display the SEM micrographs and elemental mappings of both adsorbents following adsorption and desorption experiments. The morphology of both adsorbents is preserved. The observation of a uniform distribution of Pb⁺² ions adsorbed on the MNPs confirmed the adsorption of Pb⁺² by the MNPs. Pb⁺² was, however, preferentially adsorbed on the particle’s surface rather than in its substance. As anticipated, the quantification of the elements confirmed that the CFO NPs were the source of the high concentrations of Co, Fe, and O. Evidently, the relatively low concentration of Pb⁺² was produced by ion adsorption on the surface of MNPs. Significantly, CF1 contained higher concentrations of Pb + 2 ions than CF2.

Figure 10.

Elemental distribution of Co, Fe, O, and Pb after the adsorption study for CF1(a) and CF2 (b).

4. Conclusions

We have successfully synthesized the novel CFO NPs and nanostructured CFO microgranules and systematically investigated their physicochemical properties. Our results show that both the CFO nanoparticles and nanostructured CFO microgranules favors inverse spinel structure with spherical and quasi-spherical morphology. For nanostructured CFO microgranules, M_S value is remarkably high (5.62 μ_B/F.U.) compared to the bulk counterpart, and almost double as compared to M_S value reported for CFO NPs. Our studies show that overall, the adsorption of Pb⁺² ions occurs through a multilayer adsorption mechanism for magnetic nano- and nanostructured micro-adsorbents. Moreover, the existence of adsorbate-adsorbate interactions was also observed during Pb²⁺ adsorption process as checked by Tempkin isotherm. Monolayer adsorption capacity was observed to be high (1951.98 mg/g) for nanostructured micro-adsorbents compared to nano-adsorbents (1382.74 mg/g) along with higher heat of adsorption of nanostructured micro-adsorbents (150.31 L/g) than the nano-adsorbents (135.31 L/g), which suggests that the adsorption capacity of nanostructured CFO microgranules can be enhanced further by various modifications. The proposed magnetic nano-adsorbents and nanostructured microgranules can be successfully applied for the removal of other heavy metal ions from aqueous solutions and complex industrial wastes.

Acknowledgments

S. M. Ansari gratefully acknowledges the financial support from BARC, Mumbai (Grant code: GOI–E-175). We thankfully appreciate the support of Dr. R. S. Devan, Metallurgy Engineering and Materials Science, Indian Institute of Technology (IIT), Indore, India in obtaining the FESEM micrographs. We thankfully appreciate the technical support of Mr. N. Patil and Dr. A. Supekar, Department of Geology, Savitribai Phule Pune University, Pune, India for providing the atomic absorption spectroscopy facility. The authors are also thankful to Prof. S. J. Sangode, Department of Geology, Savitribai Phule Pune University, Pune for providing BET measurements facility. The authors at the University of Texas at El Paso acknowledge, with pleasure, support from the National Science Foundation (NSF) with NSF-PREM grant #DMR-1827745.

Conflict of interest

The authors declare no conflict of interest.