Abstract

In this work, mixed oxides of LaxCa1-xMnO3 perovskite type (x = 0, 0.5 and 1.0) were synthesized through modified proteic method using collagen and calcination process at 700 °C/2 h in order to remove the commercial textile dye Bezaktiv Blue S-MAX from water. Oxides were characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), N2 physisorption, scanning electron microscopy (SEM) and point of zero charge (PZC) techniques while the dye only by the first two techniques. The XRD showed that perovskite monophase was obtained for x = 0.5 and 1.0. However, for x = 0, the low crystalline perovskite phase was obtained in the midst of precursor oxides. FTIR showed the adsorption process did not damage the adsorbents structure. The successful obtained materials have meso and macroporous with slit or cavity shape, rough surface and particles with varying sizes. The pseudo-second-order model was the one that best fit the kinetic data. The process must occur through electrostatic surface interactions between the adsorbent surface and the dye molecule. For the equilibrium study, Langmuir isotherm is the most suitable when using LaMnO3 adsorbent, while Freundlich isotherm was better suited when used the other two materials. The adsorbents were termally regenerated and reused five times. The best performance was exhibited by LaMnO3.

HIGHLIGHTS

  • Successful preparation of LaxCa1-xMnO3 (x = 0.5 and 1.0) materials using low cost method.

  • Effective removal of Bezaktiv Blue dye using LaxCa1-xMnO3.

  • The LaMnO3 maintained your adsorptive capacity throughout regeneration cycles.

  • The La0.5Ca0.5MnO3 became a catalyst after the second regeneration and the CaMnO3 had its structure consolidated throughout regeneration cycles.

  • The materials pores contributed to adsorption process.

Graphical Abstract

Graphical Abstract
Graphical Abstract

INTRODUCTION

Recently, nanomaterials used as synthetic adsorbents have attracted a lot of interest from researchers because their distinct properties such as electronic conduction, considerable specific surface area, high surface area/volume ratio (compared to microporous adsorbents), highly active sites presence, small amount of mass used and the ability to modify its surface properties. In addition, the ease of synthesis at low cost and the considerably greater adsorptive capacity are also significant advantages. Since nanomaterials exhibit unique properties, research on them is gradually becoming more popular and meaningful (Chen 2011; Tavakkoli & Yazdanbakhsh 2013; Rakass et al. 2018).

In general, scientific research on new adsorbents can be divided into two lines. In the first, it seeks to take advantage of agro-industrial waste or abundant raw materials available in nature. In the second, it looks to synthesize new adsorbents or develop the existing ones. In the first research line, adsorbents have low cost to obtain, as they are cheap by their very nature and not require elaborate processing before being used. However, the adsorptive capacities presented are not always high, requiring large adsorbent masses for the effluent treatment. In addition, these adsorbents cannot be recovered due to their own structural condition, being loaded with adsorbate and thus generating another waste, which disposal can not be done randomly. Another negative point is the fact these materials have a varied composition, requiring a homogenization pretreatment before being used.

On the other hand, the second research line involves synthesis of adsorbents that are homogeneous, have a well-defined composition and considerable adsorptive capacity and can be regenerated without suffering structural damage, thus maintaining their adsorptive capacity over several adsorption cycles, can be reused several times. Because they are carefully synthesized, these materials may even have other properties of industrial interest, such as selectivity over an effluent composed of various substances.

Within second research line appear the perovskite type mixed oxides, a new class of adsorbent materials with ABO3 structure, A being generally an alkali metal, alkaline earth or rare earth and B a metal of external transition block (Tanaka & Misono 2001; Zhu et al. 2014; Attfield et al. 2015). This materials class has been subject of numerous studies in recent decades due to its easy obtaining and because they are applied in the industrial, environmental and technological sectors. Its outstanding properties are catalytic, redox, adsorptive, electrical, optical, magnetic, ferroelectric and superconductivity, in addition to generally shown of good mechanical, thermal and hydrothermal stability. All of them depends on composition, oxide structure and synthesis method (Peña & Fierro 2001; Hardin et al. 2014; Zhu et al. 2014; Fernandes et al. 2020; Lemos et al. 2020). Perovskite type oxides are generally synthesized by means of synthesis methodologies that employ high temperatures and calcination times, besides many chemical reactants and steps. In recent years, the modified proteic method has stood out for being an alternative, low cost and less aggressive to the environment to produce those materials. In addition, the temperatures typically required to obtain the crystalline phase are generally lower (Santos et al. 2018; Ribeiro 2019; Souza 2019; Fernandes et al. 2020; Lemos et al. 2020; Nascimento et al. 2020).

These oxides constitute a very versatile compounds class, since their properties can be easily modified/manipulated or by changing the chemical composition, through the introduction of doping elements (cations partial replacement), or by modifications in the synthesis methods (Tummino et al. 2017). When the La3+ ions of LaMO3 are replaced by alkaline earth ions, like Ca2+, forming La1-xCaxMO3-δ, a positive charge can be generated. If the M cations have different oxidation states, the charge neutrality can be maintained through the oxygen vacancies formation and changes in the other cations valence states. Because of this, these mixed oxides can have electrical conductivity, catalytic activity, mechanical properties and colossal magnetoresistance (Tavakkoli et al. 2014). In addition, A-site doping with calcium is expected to maintain/improve the lanthanum manganite adsorptive properties and decrease obtaining cost, since the calcium reagent is cheaper than lanthanum reagent.

Therefore, the objective of this work is to synthesize the oxides with LaxCa1-xMnO3 (x = 0, 0.5 and 1.0) perovskite structure through the modified proteic method, characterize them using X-ray diffractometry (XRD), Fourier transform infrared spectroscopy (FTIR), N2 physisorption, scanning electron microscopy (SEM) and point of zero charge (PZC) determination techniques and evaluate them in removal of Bezaktiv Blue S-MAX (BB) dye dissolved in aqueous solution. Finally, a study was carried out on the regenerability and reuse of obtained adsorbents, evaluating the adsorptive capacity behavior throughout the adsorption–regeneration cycles. There is very little information in the literature about BB. Its safety data sheet (Bestchem 2020) only indicates that dye is reactive, bifunctional, chemically stable, metal-free and highly soluble in water.

METHODS

Synthesis of La1-xCaxMnO3

The La1-xCaxMnO3 (x = 0, 0.5 and 1.0) materials were synthesized through modified proteic method similar to that reported by Santos et al. (2018), however, with some modifications. The modifications in the synthesis methodology were proposed in order to reduce the temperature and time for calcination of the materials to obtain the desired phase. The experimental parameters for the synthesis (temperature, time, and mass, among others) were defined based on previous studies (Santos et al. 2018; Ribeiro 2019; Souza 2019). Stoichiometric calculations have been made for obtain 4 g of each final material after calcinations. Initially, manganese nitrate (Mn(NO3)2.4H2O – Neon, 98.8%) was dissolved in 200 mL of distilled water at room temperature (23 °C), under stirring, for 5 min. Then, lanthanum nitrate (La(NO3)3.6H2O – Dinâmica, 95%) (for obtain materials with x = 0) or calcium nitrate (Ca(NO3)2.4H2O – Synth, 99%) (for obtain materials with x = 1.0) was added to system and left stirring for another 5 min. To obtain materials with x = 0.5, the nitrates of lanthanum and calcium are added at same time. Soon after, the system temperature was raised to 70 °C to then slowly add the chelating agent collagen (Nutrigold do Brasil), the temperature required for better dissolution of this. This done, the system was kept under agitation for another 30 min. The obtained solution was then concentrated in an oven at 120 °C for 2 h and then pre-calcined in a muffle furnace, under following heating schedule: from 30 to 120 °C, remaining for 30 min; from 120 to 250 °C, remaining for 30 min; from 250 to 350 °C, remaining for 1 h. This procedure was done under a heating rate of 5 °C/min. A material with brittle consistency was obtained, which was broken and then calcined at 700 °C for 2 h, under 10 °C/min heating rate. Three dark powder solids were obtained: CaMnO3 (CMO), La0.5Ca0.5MnO3 (LCMO) and LaMnO3 (LMO).

Characterization of La1-xCaxMnO3

X-ray analysis were recorded by PANAnalytical analyser with Empyrean CuLFF x-ray tube performed under radiation Cu-Kα (λ = 0.15406 nm), with scanning step of 0.026°, scanning range of 15°–60°, under tension of 40 Kv and current of 40 mA. The crystallite size was calculated using the Scherrer Equation applied in the structure main peak (Tavakkoli & Yazdanbakhsh 2013; Sanaeishoar et al. 2014; Tabari et al. 2017). The specific surface areas (SBET) were determined by nitrogen adsorption–desorption using Brunauer–Emmett–Teller (BET) method. The total pore volumes and pore diameter were calculated in the desorption branch of isotherm, applying Barrett–Joyner–Halenda (BJH) method. The N2 physisorption isotherms were built in MICROMERITICS equipment, ASAP model, at 77 K. FTIR analyses were recorded by SHIMADZU equipment, IR-PRESTIGE model, obtained on wavenumber range between 4,000 and 400 cm−1, using the KBr pellet method. The materials surface texture was analyzed using HITACHI scanning electron microscopy, model TM 3,000, with magnification of 800x to LMO and LCMO and 500x to CMO. The PZC was done based by equilibrium method in a bath system, proposed by Smiciklas et al. (2000), in the pH value on that difference between pH before and after the experiment is zero. Data linear regression provides the most appropriate value. All experiments were made at room temperature (23 °C).

Adsorption tests

Before adsorption tests, dye analytical curve was built and its photodegradation study was carried out. The analytical curve (Fig. S1) purpose is obtain the maximum absorbance of solution as a concentration function. The photodegradation study (Fig. S2) was done to assess whether dye solution is stable under adsorption tests experimental conditions (laboratory brightness, temperature, degree of agitation), but in the adsorbent absence. Both experiments are used to obtain the dye characteristic wavelength. For more details, see the Supplementary Material. The commercial dye studied, BB, was obtained from a local textile industry.

Then, the adsorption tests were carried out in batch mode, single stage, in which removal kinetics and adsorption equilibrium of dye solutions with 10, 30 and 50 ppm (mg/L) initial concentrations were evaluated. Before experiments, the adsorbents were dried in an oven at 80 °C for 30 min. In erlenmeyer flasks, 20 mg of adsorbent were contacted with 20 mL of dye solution, under stirring and at room temperature (23 °C). Dye removal was evaluated at following times: 10, 20, 30, 40, 50, 60, 70, 80 and 90 min. At the end of each time, the adsorbents loaded with dye were removed by filtration with qualitative paper-filter and centrifugation at 3,500 rpm for 5 min. The resultant solution absorbance was measured in the spectrophotometer at wavelength λ = 601 nm, referring to dye maximum absorbance (see Fig. S2). The adsorption experiments were carried out in triplicate with the solution at an initial pH adjusted to 3. This value was chosen because the PZC's results and preliminary tests carried out revealed that removal capacity increases with decreasing the solution pH. The solution concentration, adsorption capacity (or adsorbate concentration in the adsorbent) and removal efficiency were calculated using Equations (1)–(3), respectively. Figure 1 shows the methodology of adsorption tests and regeneration study.
formula
(1)
formula
(2)
formula
(3)
where C represents the dye concentration in the solution (mg/L), A the absorbance, V the solution volume (L), m the adsorbent mass (g) and q the amount of dye adsorbed by the adsorbent (mg/g). The sub-index zero represents the values for initial time (zero), in the adsorbent absence. This experimental data was used in kinetic and equilibrium studies.
Figure 1

Methodology of the adsorption tests and regeneration study.

Figure 1

Methodology of the adsorption tests and regeneration study.

Regeneration and reuse study

The adsorbent loaded with dye, recuperated of adsorption tests, was dried in an oven at 80 °C to then be regenerated by heating to 700 °C, under the same calcination condition used in adsorbent synthesis, in order to decompose the adsorbed dye and recover the adsorbent. This temperature and methodology for the regeneration of the adsorbent was chosen based on previous studies (Santos et al. 2018; Ribeiro 2019; Souza 2019). Then, the regenerated adsorbent was reused five times, using consecutive masses of 100, 80, 60, 40 and 20 mg. The reuses were carried out under same conditions as adsorption tests, but only for the time of 90 min. The reuse study employed three concentrations (10, 30 and 50 ppm), but for the last two, only one reuse, with 20 mg, was done (Figure 1). The SHIMADZU UV-visible Spectrophotometer, UV-1,800 model, was used to obtain analytical curve and to do the photodegradation study and the adsorption and regeneration tests.

RESULTS AND DISCUSSION

Characterization of adsorbents

The LMO XRD pattern (Figure 2), available in Nascimento et al. (2020), confirmed the achievement of orthorhombic LaMnO3 perovskite phase (ICSD n° 82,226; Chen et al. 2013; Sanaeishoar et al. 2014; Farhadi et al. 2017). A small relative amount of manganese oxide II (ICSD n° 643,195) was also observed as secondary phase. This oxide was probably generated by excess of manganese reagent which, at high calcination temperature and in contact with common atmosphere oxygen, was converted to manganese oxide II, maintaining oxidation state of its respective reagent. The LCMO XRD pattern (Figure 2) showed only orthorhombic La0.5Ca0.5MnO3 perovskite phase (ICSD n° 155,406; González-Calbet et al. 1999; Walha et al. 2007; Mahata et al. 2017; Mo et al. 2018; Ben Moumen et al. 2019), confirming the mixed oxide obtaining. Both oxides obtained have nanometric crystallite size (Table 1).

Table 1

Structural and textural properties of synthesized adsorbents

LMOLCMOCMO
Crystal size (nm) 18.0 23.7 – 
SBET (m2/g) 12 11 
Total pore volume (cm3/g) 0.0674 0.1165 0.0599 
Pore diameter (nm) 38.2 41.5 29.0 
PZC 7.5 7.7 9.0 
LMOLCMOCMO
Crystal size (nm) 18.0 23.7 – 
SBET (m2/g) 12 11 
Total pore volume (cm3/g) 0.0674 0.1165 0.0599 
Pore diameter (nm) 38.2 41.5 29.0 
PZC 7.5 7.7 9.0 
Figure 2

XRD patterns for LMO (Nascimento et al. 2020), LCMO and CMO. LaMnO3 (*), La0.5Ca0.5MnO3 (+), CaMnO3 (°), MnO (#).

Figure 2

XRD patterns for LMO (Nascimento et al. 2020), LCMO and CMO. LaMnO3 (*), La0.5Ca0.5MnO3 (+), CaMnO3 (°), MnO (#).

Comparing LMO and LCMO diffractograms, it is noted that doping with calcium promoted a small displacement of peaks to greater angles and the disappearance of peak located at 53°. This variation is subtle because of Ca2+ (114 ) and La3+ (117.2 ) ions radii are similar. In addition, there was an increase in crystallite size, 18 to 23.7 nm (Table 1), caused by the emergence of oxygen vacancies, generated to compensate the structure loads, unbalanced due to incorporation of a cation (Ca2+) with a lower charge than pre-existing metal (La3+) (Tavakkoli et al. 2014; Tabari et al. 2017). Compared to other works like of Tavakkoli et al. (2014) (750 °C/9 h), Mahata et al. (2017) (800 °C/12 h) and Ben Moumen et al. (2019) (850 °C/8 h), the synthesis method employed achieved synthesized crystalline and practically monophasical perovskites employing a low cost chelating agent and a relative softer calcination condition.

The CMO XRD pattern (Figure 2) presents several poorly defined peaks with low intensity, in addition to a lot of noise and an inclined baseline. This means that a cohesive and well-defined single-phase structure was not obtained, but an aggregate of compounds. In addition, it is possible that there are still products from synthesis precursors that are not desired phase. The comparison with ICSD n° 258,991 crystallographic chart indicates that peaks present at angular positions 34° and 48.8° refer to orthorhombic CaMnO3. However, these are not the most intense of diffractogram, nor are they well defined. The most intense diffractogram peak, located around 17.4°, can be indexed to reagent Ca(NO3)2.4H2O (ICDD 156,304) and to compound Ca(NO2)2.4H2O (ICDD-PDF 00-038-0636). This indicates that part of reagents was not reacted during the synthesis, suggesting that perovskite phase formation has only just started. The other peaks present in the diffractogram refers to calcium and manganese II oxides, mostly the first. The most likely explanation is that the calcination condition employed (700 °C/2 h) was not sufficient to generate monophasic and crystalline CaMnO3 perovskite. This explanation is supported by literature, as no study used a temperature less than 800 °C to obtain CaMnO3 (Muro et al. 2005; Du et al. 2014; Han et al. 2014a, 2014b; Goian et al. 2015; Mo et al. 2018).

The BB XRD pattern (Fig. S3) shows a crystalline structure presence with several well-defined peaks that refer to sodium sulfate (ICDD 81,506) and sodium chloride (ICDD 169,462) presence. However, it is important to note that dyes are complex organic structures. Therefore, these two inorganic compounds are not only dye constituents, but they are the most crystallines. Another important point is that some compounds with sodium practically have peaks at angular positions 31.8° and 45.5°. Therefore, there is a possibility that sodium compound is not sodium chloride. The sulfate presence in considerable quantity indicates the dye is reactive. The XRD is quite similar to that obtained by Rodrigues (2016) for the same dye. There is little information available about this dye.

The dye FTIR spectrum (Figures 35) showed three characteristic bands: the first around 3,447 cm−1, related to O-H bond stretching, probably present due to physically adsorbed water, the second between 1,551 and 1,508 cm−1, referring to C = N bond symmetrical stretching, and the third around 1,394 cm−1, referring to symmetrical stretching of C = N groups present in the aromatic ring (Rodrigues 2016). Figure 3 also is available in Nascimento et al. (2020).

Figure 3

FTIR spectra for BB (dye) and LMO before and after adsorption.

Figure 3

FTIR spectra for BB (dye) and LMO before and after adsorption.

Figure 4

FTIR spectra for BB (dye) and LCMO before and after adsorption.

Figure 4

FTIR spectra for BB (dye) and LCMO before and after adsorption.

Figure 5

FTIR spectra for BB (dye) and CMO before and after adsorption.

Figure 5

FTIR spectra for BB (dye) and CMO before and after adsorption.

The adsorbents spectra (Figures 35) show bands around 600 and 400 cm−1, characteristics of metal-oxygen bonds. In the case of perovskite oxides well synthesized, LMO and LCMO, these bands are related to υ M-O bonds stretching and δ M-O-M bonds vibration modes, respectively (Hashemian & Foroghimoqhadam 2014; Tavakkoli & Moayedipour 2014; Tavakkoli et al. 2014; Farhadi et al. 2017). For CMO, these bands are related to metal-oxygen bonds of precursor oxides and also of perovskite phase.

The only evidence that adsorbent is loaded with dye is the slight elongation of band located between 1,551 and 1,508 cm−1. The probable explanation for the dye characteristics bands absence in loaded adsorbent spectrum is due to the low concentration of dye solution employed, resulting in a small absolute amount of adsorbed dye. The characteristic bands maintained their position and intensity, indicating the oxides maintained its structure after adsorption process. This fact suggests the adsorption did not occur through strong interactions, implying a regeneration process that can remove/decompose adsorbate without destroying adsorbent structure will maintain the perovskite adsorptive capacity (Nascimento et al. 2020).

The synthesized materials shown low specific area (Table 1), something already indicated by literature (Zhu et al. 2014; Tabari et al. 2017; Mo et al. 2018; Santos et al. 2018; Fernandes et al. 2020). In addition, the pore diameter classifies them as mesoporous, which must be linked to agglomerated nanoparticles formation (Mo et al. 2018). The doped oxide showed higher specific superficial area (SBET), total pore volume and pore diameter, which may be due to pores formation in stacked crystal particles (Mo et al. 2018).

The three N2 physisorption isotherms (Figures 68) are characteristic of macroporous or nonporous materials, and have a hysteresis loop, typical of systems composed by particles aggregates in plate or lamella forms, generating pores with a slit or cavity shape (Thommes et al. 2015), in agreement with stated by Mo et al. (2018). This same profile (similar isotherms and hysteresis) has been presented by other studies with perovskite oxides (Du et al. 2014; Han et al. 2014b; Sanaeishoar et al. 2014; Ding et al. 2017; Tabari et al. 2017; Mo et al. 2018). The pore diameter distribution curves for LMO and LCMO (inset of Figures 6 and 7) confirm the meso and macropores presence, mostly mesopores. The fact these curves are continuous and have only a maximum region indicates the materials structure is organized, with pores diameter in a narrow values range. The same is not shown by pore diameter distribution curves for CMO (inset of Figure 8), confirming that the structure obtained is not organized and has pores/cracks with varying diameters, something already indicated by XRD (Figure 2).

Figure 6

Nitrogen adsorption isotherm and corresponding pore diameter distribution curves (inset) of LMO.

Figure 6

Nitrogen adsorption isotherm and corresponding pore diameter distribution curves (inset) of LMO.

Figure 7

Nitrogen adsorption isotherm and corresponding pore diameter distribution curves (inset) of LCMO.

Figure 7

Nitrogen adsorption isotherm and corresponding pore diameter distribution curves (inset) of LCMO.

Figure 8

Nitrogen adsorption isotherm and corresponding pore diameter distribution curves (inset) of CMO.

Figure 8

Nitrogen adsorption isotherm and corresponding pore diameter distribution curves (inset) of CMO.

The LMO and LCMO PZCs are around neutrality (Table 1), similar to other literature works that have studied homologous materials (Couto et al. 2020; Fernandes et al. 2020; Ribeiro et al. 2020). Thus, contact with acidic solutions leaves the net surface charge of adsorbent positive while contact with basic solutions leaving the adsorbent surface with negative net charge. Preliminary tests carried out revealed that removal capacity increases with decreasing the solution pH. That is, the more positive the adsorbent surface is, greater the dye removal. This is a strong indication that dye is anionic when in aqueous solution. The CMO PZC is equal to 9.0 (Table 1). This difference in relation to other adsorbents is probably due to CMO was not constituted predominantly by CaMnO3 oxide, but by a mixture of oxides with low crystallinity, as seen in XRD (Figure 2). The PCZ increase with calcium entry into structure may be related to the fact that doping promotes partial replacement of La3+ cation by Ca2+, generating anionic vacancies in structure.

The SEM images (Figure 9) show that synthesized adsorbents have an irregular, spongy and rough surface, with many cavities and little agglomeration, mainly of small particles, which must have grown on larger ones. In addition, the particles have varying sizes and shapes. This helps to explain the good adsorptive behavior. The same was seen in some works in literature (Oliveira et al. 2010; Tavakkoli & Yazdanbakhsh 2013; Han et al. 2014b; Mo et al. 2018; Rakass et al. 2018; Farhadi & Mahmoudi 2019; Lemos et al. 2020; Ribeiro et al. 2020). The porous structure was probably favored during synthesis, in the gases released from chelating agent decomposition (Oliveira et al. 2010).

Figure 9

SEM images for LMO (a), LCMO (b) and CMO (c).

Figure 9

SEM images for LMO (a), LCMO (b) and CMO (c).

Adsorption tests

The dye analytical curve (Fig. S1) shows that it complies with Beer's Law, with mass absorptivity equal to 24.9 L·cm−1·g−1, and has a characteristic wavelength equal to 601 nm. The photodegradation study (Fig. S2) reveals that dye solution is stable under experimental conditions.

In general, perovskite type oxides have low specific areas, with better results in processes involving surface interactions and/or electron transfer (Zhu et al. 2014). Therefore, there was not evaluated kinetic models that consider internal diffusion stage in adsorbent. Then, only pseudo-first- (PFO) and pseudo-second- (PSO) order models, both formulated considering adsorptions stage on sites as slowest in the process (Ho & McKay 1999; Febrianto et al. 2009; Largitte & Pasquier 2016), were evaluated. In addition, these two models (Equations (4) and (5), respectively) are the most applied to study dyes adsorption on perovskite oxides to date (Yazdanbakhsh et al. 2011; Farhadi et al. 2017; Santos et al. 2018; Farhadi & Mahmoudi 2019; Couto et al. 2020; Fernandes et al. 2020; Lemos et al. 2020; Nascimento et al. 2020).
formula
(4)
formula
(5)
where q and qe are the adsorption capacities (or concentration in the adsorbent) at a time t and in equilibrium, respectively, and k1 and k2 are the adsorption rate constants of pseudo-first- and pseudo-second-orders, respectively. These parameters were calculated by non-linear regression of experimental data to Equations (4) and (5).

The graphs (Figures 1012) and Table 2 analysis shows that LMO and LCMO performed well, especially for 10 and 30 ppm initial concentrations. For CMO, a less effective behavior occurred, probably due to not being made up of perovskite monophase. In addition, it can be said that doping with calcium makes adsorbents less effective and slower (less kinetic constant). The PSO model is one that best describes the process kinetic behavior in all cases, although PFO model also provides reliable results. This evaluation was made based on R2 (linear correlation coefficient) and χ2 (Pearson correlation coefficient) fitting parameters and comparing experimental and estimated qe.

Table 2

Kinetic parameters estimated in data nonlinear regression and removal efficiency values

AdsorbentInitial conc. (ppm)PFOPSORemoval efficiency (%)
LMO 10 qe = 8.953 ± 0.042 mg/g qe = 9.046 ± 0.049 mg/g 92 
k1 = 0.362 ± 0.052 min−1 k2 = 0.255 ± 0.082 g·mg−1·min−1 
R2 = 0.998 R2 = 0.999 
χ2 = 0.0136 χ2 = 0.0085 
30 qe = 24.711 ± 0.172 mg/g qe = 25.118 ± 0.189 mg/g 85 
k1 = 0.323 ± 0.052 min−1 k2 = 0.058 ± 0.017 g·mg−1·min−1 
R2 = 0.997 R2 = 0.998 
χ2 = 0.2323 χ2 = 0.1254 
50 qe = 31.229 ± 0.558 mg/g qe = 32.831 ± 0.657 mg/g 63 
k1 = 0.188 ± 0.033 min−1 k2 = 0.013 ± 0.003 g·mg−1·min−1 
R2 = 0.979 R2 = 0.989 
χ2 = 2.2654 χ2 = 1.2152 
LCMO 10 qe = 9.226 ± 0.066 mg/g qe = 9.470 ± 0.052 mg/g 95 
k1 = 0.252 ± 0.026 min−1 k2 = 0.089 ± 0.012 g·mg−1·min−1 
R2 = 0.996 R2 = 0.999 
χ2 = 0.0334 χ2 = 0.0089 
30 qe = 17.201 ± 0.408 mg/g qe = 18.191 ± 0.500 mg/g 63 
k1 = 0.185 ± 0.042 min−1 k2 = 0.021 ± 0.007 g·mg−1·min−1 
R2 = 0.964 R2 = 0.979 
χ2 = 1.2090 χ2 = 0.6844 
50 qe = 27.162 ± 0.750 mg/g qe = 28.323 ± 1.046 mg/g 56 
k1 = 0.257 ± 0.106 min−1 k2 = 0.020 ± 0.013 g·mg−1·min−1 
R2 = 0.949 R2 = 0.961 
χ2 = 4.3161 χ2 = 3.3496 
CMO 10 qe = 4.971 ± 0.112 mg/g qe = 5.346 ± 0.136 mg/g 55 
k1 = 0.145 ± 0.025 min−1 k2 = 0.048 ± 0.012 g·mg−1·min−1 
R2 = 0.969 R2 = 0.984 
χ2 = 0.0848 χ2 = 0.0442 
30 qe = 17.487 ± 0.371 mg/g qe = 18.865 ± 0.505 mg/g 63 
k1 = 0.133 ± 0.020 min−1 k2 = 0.012 ± 0.003 g·mg−1·min−1 
R2 = 0.974 R2 = 0.983 
χ2 = 0.9031 χ2 = 0.5800 
50 qe = 24.426 ± 0.546 mg/g qe = 26.876 ± 0.543 mg/g 53 
k1 = 0.110 ± 0.015 min−1 k2 = 0.006 ± 0.001 g·mg−1·min−1 
R2 = 0.973 R2 = 0.991 
χ2 = 1.819 χ2 = 0.5662 
AdsorbentInitial conc. (ppm)PFOPSORemoval efficiency (%)
LMO 10 qe = 8.953 ± 0.042 mg/g qe = 9.046 ± 0.049 mg/g 92 
k1 = 0.362 ± 0.052 min−1 k2 = 0.255 ± 0.082 g·mg−1·min−1 
R2 = 0.998 R2 = 0.999 
χ2 = 0.0136 χ2 = 0.0085 
30 qe = 24.711 ± 0.172 mg/g qe = 25.118 ± 0.189 mg/g 85 
k1 = 0.323 ± 0.052 min−1 k2 = 0.058 ± 0.017 g·mg−1·min−1 
R2 = 0.997 R2 = 0.998 
χ2 = 0.2323 χ2 = 0.1254 
50 qe = 31.229 ± 0.558 mg/g qe = 32.831 ± 0.657 mg/g 63 
k1 = 0.188 ± 0.033 min−1 k2 = 0.013 ± 0.003 g·mg−1·min−1 
R2 = 0.979 R2 = 0.989 
χ2 = 2.2654 χ2 = 1.2152 
LCMO 10 qe = 9.226 ± 0.066 mg/g qe = 9.470 ± 0.052 mg/g 95 
k1 = 0.252 ± 0.026 min−1 k2 = 0.089 ± 0.012 g·mg−1·min−1 
R2 = 0.996 R2 = 0.999 
χ2 = 0.0334 χ2 = 0.0089 
30 qe = 17.201 ± 0.408 mg/g qe = 18.191 ± 0.500 mg/g 63 
k1 = 0.185 ± 0.042 min−1 k2 = 0.021 ± 0.007 g·mg−1·min−1 
R2 = 0.964 R2 = 0.979 
χ2 = 1.2090 χ2 = 0.6844 
50 qe = 27.162 ± 0.750 mg/g qe = 28.323 ± 1.046 mg/g 56 
k1 = 0.257 ± 0.106 min−1 k2 = 0.020 ± 0.013 g·mg−1·min−1 
R2 = 0.949 R2 = 0.961 
χ2 = 4.3161 χ2 = 3.3496 
CMO 10 qe = 4.971 ± 0.112 mg/g qe = 5.346 ± 0.136 mg/g 55 
k1 = 0.145 ± 0.025 min−1 k2 = 0.048 ± 0.012 g·mg−1·min−1 
R2 = 0.969 R2 = 0.984 
χ2 = 0.0848 χ2 = 0.0442 
30 qe = 17.487 ± 0.371 mg/g qe = 18.865 ± 0.505 mg/g 63 
k1 = 0.133 ± 0.020 min−1 k2 = 0.012 ± 0.003 g·mg−1·min−1 
R2 = 0.974 R2 = 0.983 
χ2 = 0.9031 χ2 = 0.5800 
50 qe = 24.426 ± 0.546 mg/g qe = 26.876 ± 0.543 mg/g 53 
k1 = 0.110 ± 0.015 min−1 k2 = 0.006 ± 0.001 g·mg−1·min−1 
R2 = 0.973 R2 = 0.991 
χ2 = 1.819 χ2 = 0.5662 
Figure 10

Non-linear regression curves of LMO adsorbent to PFO and PSO models.

Figure 10

Non-linear regression curves of LMO adsorbent to PFO and PSO models.

Figure 11

Non-linear regression curves of LCMO adsorbent to PFO and PSO models.

Figure 11

Non-linear regression curves of LCMO adsorbent to PFO and PSO models.

Figure 12

Non-linear regression curves of CMO adsorbent to PFO and PSO models.

Figure 12

Non-linear regression curves of CMO adsorbent to PFO and PSO models.

The PSO model was formulated considering that process limiting step is an adsorption mechanism (Aksu & Tezer 2000), which can be a chemisorption, where an electrons exchange or sharing occurs between adsorbent and adsorbate (Ho & McKay 1999). Therefore, the results are physically based, after all, as previously mentioned, the processes involving perovskite oxides occur mainly through electron transfer and/or surface interactions, after all, they are little porous structures (Zhu et al. 2014), with electrostatic attraction between dye molecules and adsorbent surface being the predominant mechanism (Al-Degs et al. 2008; Yazdanbakhsh et al. 2011; Tavakkoli & Moayedipour 2014; Tavakkoli et al. 2014; Farhadi & Mahmoudi 2019).

The results also show that initial concentration increases promote an adsorption capacity (qe) increase and a decrease in removal efficiency and adsorption rate kinetic constant. These same conclusions were obtained by other literature works (Yazdanbakhsh et al. 2011; Farhadi et al. 2017; Farhadi & Mahmoudi 2019). The removal percentage decrease occurs due to adsorption sites saturation. As for kinetic constant, although mass gradient between phases (that is, the difference between adsorbent concentration at equilibrium and a time t (qqe)), and the removal rates are higher at higher initial concentrations, it should be kept in mind that a higher initial concentration will promote dye molecules competition for adsorption sites, slowing down the process. In addition, these molecules' length and volume can be large enough to promote stereochemical impediment when linked to adsorptive sites, making it difficult to adsorb other molecules to neighboring sites and thus slowing down the process. Another explanation, given by Hashemian & Foroghimoqhadam (2014), states this phenomenon probably occurs due to electrostatic repulsion between adsorbed anionic dye molecules and those present in solution. All this analysis is valid for LMO and LCMO. For CMO, although initial concentration increase has decreased adsorption kinetic constants values, the removal percentage showed an unusual behavior, probably due to adsorbent being composed by a cluster of different oxides.

The adsorption kinetic constant is a parameter that depends not only the initial concentration, but also on other experimental conditions, such as agitation degree and system temperature. In addition, it is also a function of adsorbent–adsorbate interaction and adsorbents properties, such as porosity and surface morphology. As an example of this last factor, Fernandes et al. (2020) synthesized LaNiO3 using three different methodologies and used it to remove Congo Red. The results showed that presented kinetic constant was different, although materials obtained were the same, but obtained by different methods and, therefore, presented textural properties that were also different. In addition, it was noted that initial concentration increase promoted higher error bars. The probable explanation must be the same as kinetic constant behavior. Another explanation is that adsorbent surface coverage is heterogeneous, which can facilitate/hinder adsorption of new molecules, generating more random results that were evident in higher initial concentrations.

Compared to other studies that used LaMnO3 as a dye adsorbent, the LMO performed better (Table 3), removing a higher percentage in shorter contact time. The adsorbent-adsorbate interactions must be favorable to BB dye. These results also show that synthesis method chosen is effective, as it produced a material with greater adsorptive capacity under milder synthesis conditions and employing a cheaper complexing agent (collagen).

Table 3

Comparative use of LaMnO3 as adsorbent

Synthesis methodDyeInitial conc.Equilibrium timeRemoval efficiencyReference
MP BB 10 ppm 90 min 92% This work and Nascimento et al. (2020)  
MP BB 30 ppm 90 min 85% This work 
MP BB 50 ppm 90 min 63% This work 
AC MB 25 ppm 240 min 43% Farhadi et al. (2017)  
AC MO 25 ppm 240 min 9% Farhadi et al. (2017)  
MP CR 50 ppm 120 min 63% Santos et al. (2018)  
Synthesis methodDyeInitial conc.Equilibrium timeRemoval efficiencyReference
MP BB 10 ppm 90 min 92% This work and Nascimento et al. (2020)  
MP BB 30 ppm 90 min 85% This work 
MP BB 50 ppm 90 min 63% This work 
AC MB 25 ppm 240 min 43% Farhadi et al. (2017)  
AC MO 25 ppm 240 min 9% Farhadi et al. (2017)  
MP CR 50 ppm 120 min 63% Santos et al. (2018)  

Note: MP, Modified proteic; AC, Auto combustion; MB, Methylene Blue; MO, Methyl Orange; CR, Congo Red.

The adsorption equilibrium was evaluated using Langmuir and Freundlich models in their linear forms (Equations (6) and (7)), where qmax is the adsorption capacity (or adsorbent concentration) of saturated monolayer, Ce is the equilibrium adsorbate concentration in the liquid phase, KL is the adsorption equilibrium constant, also known as the Langmuir constant, KF is the characteristic constant related to the adsorption capacity, also known as the Freundlich constant, n is the characteristic constant related to adsorption intensity, or favorability degree of adsorption (Vijayaraghavan et al. 2006; Chatterjee et al. 2007; Febrianto et al. 2009; Hashemian & Foroghimoqhadam 2014; Aljeboree et al. 2017). These parameters were determined by plotting the curve Ce/qe vs Ce in Equation (6) and ln qe vs ln Ce in Equation (7).
formula
(6)
formula
(7)

The Langmuir model is the one that best represents adsorption equilibrium when used the LMO adsorbent while Freundlich model is most suitable for LCMO and CMO (Table 4). The Freundlich isotherm is applicable to almost all experimental adsorption–desorption systems, especially for those highly heterogeneous, or that have varying affinity (Vijayaraghavan et al. 2006; Febrianto et al. 2009), this being the probable explanations for its adjustment to CMO, a material composed by an agglomerate of oxide phases. The monolayer adsorption capacity values (qmax) (Table 4) presented are in accordance with this new adsorbents class (Yazdanbakhsh et al. 2011; Tavakkoli & Moayedipour 2014; Farhadi & Mahmoudi 2019). Doping with calcium reduced this parameter, making adsorbent less effective, something already proven in kinetic studies.

Table 4

Estimated parameters in linear regressions for equilibrium study

Langmuir
Adsorbentqmax (mg/g)KL (L/mg)R2
LMO 33.75 0.54 0.999 
LCMO 29.77 0.34 0.922 
CMO – – 0.184 
Freundlich
AdsorbentnKF (mg1–1/n·L1/n·g−1)R2
LMO 2.55 11.06 0.905 
LCMO 3.64 10.97 0.958 
CMO 1.05 1.45 0.933 
Langmuir
Adsorbentqmax (mg/g)KL (L/mg)R2
LMO 33.75 0.54 0.999 
LCMO 29.77 0.34 0.922 
CMO – – 0.184 
Freundlich
AdsorbentnKF (mg1–1/n·L1/n·g−1)R2
LMO 2.55 11.06 0.905 
LCMO 3.64 10.97 0.958 
CMO 1.05 1.45 0.933 

The constant n (Table 4) indicates that the process is favorable to LCMO and has a linear behavior for CMO (Febrianto et al. 2009). Although they also indicate that process is physical (Febrianto et al. 2009; Aljeboree et al. 2017), it should be kept in mind that adsorption process on perovskite oxides very likely is chemical. Although they are materials with a low total area, the crystals' small size (Table 1) may have contributed to obtain meso and macropores in cracks and cavities shape. In addition, the relatively high pore diameters and volumes (Table 1) help explain the perovskites good performance as dye adsorbents, after all, they are bulky molecules. Therefore, in dye adsorption case, the three textural parameters, specific surface area, pore diameter and pore volume, must be taken into account instead of just a specific area. In addition, it should be kept in mind this materials class performance is a function of electronic exchanges and/or surface interactions (Zhu et al. 2014).

Regeneration and reuse tests

Figure 13 shows BB dye removal efficiencies for the LMO regeneration cycles. For the reuse of LMO adsorbent at 10 ppm dye initial concentration solution (Figure 13), the increase in removal percentages throughout reuses is due to the presence of more adsorptive sites in contact with dye (greater mass of adsorbent). However, comparing virgin adsorbent and last reuse, both with the same mass, it was found that adsorption capacity was maintained. When using the dye solution at 30 ppm and LMO material (Table 5), same affirmation can be done, but only in one cycle. However, for dye solution at 50 ppm, a considerable increase in removal percentage is observed (Table 5). The reason is that regeneration could have promoted structure recrystallization, after all, it is performed at the same calcination condition. So, more adsorptive sites may have appeared.

Table 5

Removal efficiency obtained in adsorbents reuse at 30 and 50 ppm

AdsorbentInitial conc.Virgin1st reuse
LMO 30 ppm 85% 79% 
50 ppm 63% 80% 
LCMO 30 ppm 63% 80% 
50 ppm 56% 80% 
CMO 30 ppm 63% 77% 
50 ppm 53% 72% 
AdsorbentInitial conc.Virgin1st reuse
LMO 30 ppm 85% 79% 
50 ppm 63% 80% 
LCMO 30 ppm 63% 80% 
50 ppm 56% 80% 
CMO 30 ppm 63% 77% 
50 ppm 53% 72% 
Figure 13

Removal efficiency for LMO regeneration cycles.

Figure 13

Removal efficiency for LMO regeneration cycles.

For LCMO material at 10 ppm dye initial concentration, adsorption occurred only for first reuse. Thereafter, the solution after process changed color from blue to purple, which weakened as the process progressed. In addition, the absorbance peak was dislocated for lowers wavelengths values (Figure 14). The conclusion is that after the regeneration process, adsorbent starts to act as a catalyst that increased its catalyst activity throughout regeneration cycles, because the absorbance curve intensity decreased. For 30 and 50 ppm initial concentrations, only adsorption occurs and both presented a higher removal than obtained value to the virgin adsorbent (Table 5). This increase can be also attributed to structure recrystallization that occurred in regeneration process.

Figure 14

Absorbance curves for dye 10 ppm solutions adsorbed by regenerated LCMO.

Figure 14

Absorbance curves for dye 10 ppm solutions adsorbed by regenerated LCMO.

For CMO, the results show that removal percentage increase throughout regeneration cycles and stabilized from third reuse onwards (Figure 15), probably due to CaMnO3 phase consolidation. The comparison between virgin and fifth reuse show that percentages' increase did not occur because greater mass of adsorbent. At 30 and 50 ppm initial concentrations, removal percentages increased because same reason (Table 5). The results suggest that a phase with sites more active became more dominant in the material.

Figure 15

Removal efficiency for CMO regeneration cycles.

Figure 15

Removal efficiency for CMO regeneration cycles.

The study of regeneration and reuse of adsorbent suggest that the adsorbent structure is free of the dye after the regeneration step, it is possible that this is an indication that the pores/cavities of material have a slit shape, as this format facilitates for the adsorbed molecules to leave.

Previous studies on the maintenance of the perovskite structure after dye adsorption tests, and calcination at temperatures above 700 °C, for material regeneration and reuse have indicated that this structure is stable and maintained after these conditions (Santos et al. 2018; Ribeiro 2019; Souza 2019). In addition, the efficiency of the adsorbent for new dye adsorption (reuse) tests has been reported to be increased in some cases.

CONCLUSIONS

The LMO and LCMO syntheses were successful, generating respective perovskite nanomaterials with orthorhombic structure. Compared to other works, the synthesis method employed achieved synthesized crystalline and practically monophasical perovskites employing a low cost chelating agent and a relatively softer calcination condition. The CMO synthesis did not generate a perovskite monophase due to low temperature and calcination time employed. FTIR spectra showed adsorption process does not damage adsorbent structure. All adsorbents synthesized have meso and macropores with slit or cavity shape. The adsorbents' surface is rough and have particles with different sizes and shapes. The LMO and LCMO PCZ is around neutrality and removal efficiency is better at acidic pHs, indicating the dye is anionic. The kinetic studies shows that PSO model is the one that best represents the process. For equilibrium, Langmuir model best represent the process to LMO while Freundlich model was most adequate to other adsorbents. The regeneration process maintained LMO adsorptive capacity over five cycles.

Although they are materials with low specific surface area, the pore size is relatively large and the irregular and texturally rough surface explains the good adsorptive behavior. In addition, a complex electronic structure of perovskite type oxides must have attracted complex dye molecules. Even doped oxide has a good cost–benefit ratio, since calcium reagent is much cheaper than others, which has the best overall performance is LMO, as it presented a considerable adsorptive capacity and remained stable during all adsorption–regeneration cycles.

ACKNOWLEDGEMENTS

The authors are grateful at Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. In addition, the authors are grateful too at ‘Flat Icons’ for icons in some images.

DATA AVAILABILITY STATEMENT

All relevant data are included in the paper or its Supplementary Information.

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