Efficient and cost-effective adsorbent brick sand nanoparticles used for removal of heavy metals

ABSTRACT

We are introducing cost effective, easily available brick sand nano-particles (BSNP) (44.208 nm) as an adsorbent in batch process for removal of heavy toxic metals such as Pb, Cd, and Cr from aqueous solution. We used two types of adsorbents, one is acid treated BSNP and other one is untreated BSNP, and investigated adsorbent properties with function of contact time, temperature, and pH. The efficiency or performance of adsorbent was characterized by AAS for percentage of removal from solution, EDS for elemental analysis to confirm the metals adsorption. The Langmuir, Freundlich, Temkin, Hill De Boer, Elovich, and Redlich-Peterson adsorption isotherms were studies to find out the quality of the adsorbent. At 5.68 to 7.6 pH, the maximum removal percentages of Pb, Cd, and Cr were 97% from the aqueous solution. The temperatures at 40℃ to 60℃, the Pb, Cd, and Cr experiments were carried out at their highest possible percentages (98%). The reaction process was pseudo-second order model and several thermodynamic factors, including Intraparticle diffusion, Elovich kinetic model, Boyd kinetic model, activation energy, Gibbs free energy, enthalpy change, and entropy change ensured the adsorbent efficiency of brick sand nanoparticles. The results of present research indicated that Brick Sand Nano-Particles (BSNP) may play a vital role as a cost-effective, efficient, and easily available adsorbent for the removal of Pb, Cd, and Cr from wastewater.

KEYWORDS:

• Brick nanoparticles
• removal
• heavy metals
• adsorption
• wastewater

Introduction

Water pollution is a severe environmental issue in the world and brings uncountable challenges. The peoples are suffering for safe water due to water pollution every time by industrial effluents. Many countries are already suffering for heavy toxic metal arsenic. Beside that, Lead, Chromium, and Cadmium pollution are added as an additional pollutant in water. It is very difficult and costly for the peoples to buy the expensive filter or community filter to remove the toxic metals for safe drinking water. Most of them are using heavy metals (like Lead, Chromium, and Cadmium) contaminated polluted water and suffering with severe health issues. Lead-acid processing and lead-acid recycling industries are the 85.18% sources of lead (Majumder, Al Nayeem, Islam, Akter, & Carter, 2021). Burning fossil fuels like coal or oil and incinerating municipal garbage like plastics and nickel-cadmium batteries are the primary sources of chromium and cadmium along with the effluent from the leather industry and the electroplating industry. By consume high level of heavy metals like Cd, Cr, and Pb, human body are suffering for life threating health issues like the liver, kidneys, blood vessels, and central nervous system disorder diseases. To remove heavy metals from wastewater, many techniques are reported in literatures, such as chemical precipitation (Oncel, Muhcu, Demirbas, & Kobya, 2013), oxidation and reduction (Khan, Sayed, Sohail, Shah, & Raja, 2019), ion exchange (Kansara, Bhati, Narang, & Vaishnavi, 2016), reverse osmosis (Ozaki, Sharma, & Saktaywin, 2002), and adsorption (Ihsanullah et al., 2016). However, all of the techniques are not popular to the user level due to high cost of media or filter or adsorbent.

To consider the cost and efficiency of the adsorbent, we are trying to find out the most efficient and economical affordable adsorbent in most popular adsorption process. There are more than 100 different types of efficient adsorbent used in adsorption process which were reported in literatures, including sawdust (Bulut & Tez, 2007), limestone (Aziz, Adlan, & Ariffin, 2008), brick clay (Sanad, Moniem, Abdel-Latif, Hossein, & El-Mahllawy, 2021), rice husk (Chuah, Jumasiah, Azni, Katayon, & Choong, 2005), tea waste (Amarasinghe & Williams, 2007), orange peel (Feng, Guo, Liang, Zhu, & Liu, 2011), sugar industry waste (Gupta, Jain, Ali, Sharma, & Saini, 2003), red mud (Gray, Dunham, Dennis, Zhao, & McGrath, 2006), coconut waste (Anirudhan & Sreekumari, 2011), powder of calcined brick (Krishna & Swamy, 2012), peanut hull (Brown, Jefcoat, Parrish, Gill, & Graham, 2000), and more.

Using crushed clay bricks as an adsorbent might remove more than 60% of Pb and Cd from wastewater (Lin, 2006). Using surkhi, a locally produced brick sand particle, Cr was removed 94% from wastewater (Karim, Dipti, & Azad, 2021). Brick kiln chimney waste was the type of adsorbent that was 87.7% effective in removal of Cr(VI) from an aqueous environment (Yang et al., 2022).

Brick particles has a great potential for removing heavy metals from wastewater as an adsorbent. Brick particles have abundant resources throughout the world. Moreover, due to its low swelling and water dispersion properties brick particles has been reported to be a better adsorbent for metals adsorption on it nano-lamellar structural silicate minerals contained surfaces (Barati, Asgari, Miri, & Eskandari, 2013). Therefore, the brick particles may widely use at a low cost in wastewater purification if we can use it without pretreatment.

In our investigation, we are using brick sand nanoparticles as an adsorbent in batch adsorption process for removal of Pb, Cd, and Cr, which is affordable in cost, easily available, efficient, and may take attention gross level user as well as industry owner. For that purpose, we take grade 3 brick and crushed it like powder. According to physical properties, the major advance of the Third-class brick is -it can absorb about 25 % moisture or water of dry weight. The major drawback of this adsorbent is a) it is very soft, b) it is not suitable for prolong time, and c) it needs to change periodically. Counting the absorption advantages, we make two types of adsorbents one is untreated and other is treated by sulfuric acid. The nanoparticles size was confirmed by XRD spectrum and calculated by using Scherrer Equation. The batch adsorption process was contact with three different condition such as 1) contact time, 2) temperature, and 3) pH. The percentage of removal of heavy metals was investigated by AAS. By the investigation of Energy Dispersive X-ray Spectroscopy (EDS), we can analysis the element presents in the sample to confirm the adsorption. Langmuir, Freundlich, Temkin, Hill DeBoer Isotherm, Elovich isotherm, and Redlich-Peterson adsorption isotherms were studies to find out the quality of the adsorbent. We also investigated the kinetics of the adsorption process by intraparticle diffusion, Boyd and Elovich kinetic model, as well as several thermodynamic factors, including activation energy, Gibbs free energy, enthalpy change, and entropy change characterizes the adsorbent efficiency of brick sand nanoparticles.

Materials and methods

Materials: Potassium dichromate (Merck India), cadmium sulfate hydrate (Sigma-Aldrich), Lead (II) chromate (Sigma-Aldrich), H₂SO₄ acid (Sigma-Aldrich) deionized water etc.

Collection and preparation of adsorbent

In the current study, heavy metals like Cd, Cr, and Pb were removed from aqueous solution using brick sand nanoparticles (44.208 nm-sized) as an adsorbent. Class 3 grade brick adsorbent was collected from the local area. Utilizing locally built machinery, the collected brick was broken down like powder and passed through the screener. The brick sand particle adsorbent was cleaned with deionized water and let dry for 1day in the sun. For our analytical study, two types of adsorbents were used, one was the untreated brick sand particles, and the other brick sand particles was treated with 0.5N H₂SO₄ acid.

Preparation of stock solution

Cd, Cr, and Pb stock solutions were made using an analytical method. For 500ppm of Cr, 0.706825 g of potassium dichromate was used, while for 500ppm of Cd, 0.570367 g of cadmium sulfate hydrate was used in 500 ml of stock solution. Similarly, 500 ml of a stock solution with 500 ppm Pb was created using 0.389953 g of lead chromate.

Research design and batch adsorption studies

Brick sand nanoparticles were utilized to remove the Cd, Cr, and Pb, which have specific properties that help the adsorption of heavy metals. At first, dying the brick sand nanoparticles, the percentages of moisture content in the adsorbent were calculated after the 250 g brick nanoparticles were dried in the sun for 1 day. The following equation was used to determine the moisture content (Nielsen, 2010):

(1) $\mathrm{\%}\mathrm{of}\mathrm{moisture}\mathrm{content}=\mathit{\hspace{0.17em}}\frac{\mathit{weight}\mathit{of}\mathit{moisture}}{\mathit{weight}\mathit{of}\mathit{sample}}\times 100$(1)

In our study, the sample was taken at 250 g, and the weight of moisture was 15 g. Thus, the % of moisture content was as follows:

$\mathrm{\%}\mathrm{of}\mathrm{moisture}\mathrm{content}=\mathit{\hspace{0.17em}}\frac{15}{250}\times 100\mathit{\hspace{0.17em}}=\mathit{\hspace{0.17em}}6\mathrm{\%}$

A 500 ml beaker was filled with 100 ml of a 500ppm sample of Pb, Cd, and Cr. The sample was heated to various degrees using a water bath. Samples were heated for 10 minutes at 30°C, 20 minutes at 40°C, 30 minutes at 50°C, 40 minutes at 60°C, and 50 minutes at 70°C. The effect of pH was studied at pH 5.6 to 8.7. The 98% or more than removal of Pb, Cd, and Cr were effective at lower pH 5.6 to 7.5. The pH was maintained by using NaCl pellets.

Characterizations

AAS was analyzed to determine the existing concentration of a solution in ppm. By AAS (atomic absorption spectroscopy) result, we determined the percentage of Cd, Cr, and Pb removal from the water. Residual brick sand nanoparticles were used for the elemental analysis by using Energy Dispersive X-ray Spectroscopy (EDS). The XRD investigation was performed to determine the brick sample’s particle size.

Adsorption isotherm studies

Langmuir adsorption isotherm

In 1916, Langmuir demonstrated a monolayer equation for deposited on the adsorbent’s outer surface; no further adsorption takes place on the surface after absorption. The distribution of metal ions in a system’s solid phase and liquid phase at equilibrium is represented by Langmuir. When a monolayer is deposited onto a surface with a finite number of identical sites, the Langmuir isotherm remains true. This model precludes adsorbate transmigration in the surface plane while allowing uniform deposition impulses onto the surface (Langmuir, 1918). According to these scientific presumptions, Langmuir provided the following equation:

(2) $q_e=\frac{q_{max{K}_l}}{1+K_l{C}_e}\times C_e$(2)

Langmuir adsorption parameters are determined by using the linear form of the following equation,

(3) $\mathit{\hspace{0.17em}}\frac{1}{q_e}\mathit{\hspace{0.17em}}=\mathit{\hspace{0.17em}}\frac{1}{{\mathit{q}}_{\mathit{max}}}\mathit{\hspace{0.17em}}+\mathit{\hspace{0.17em}}\frac{1}{({\mathit{q}}_{\mathit{max}}{K}_l)\times C_e}$(3)

Here, C_e =equilibrium concentration of adsorbate, q_e = amount of metal adsorbed per gram of the adsorbate at equilibrium, q_max = maximum monolayer coverage capacity, Kι = Langmuir isotherm constant.

Here, the value of q_max and Kι were found by computing $\frac{1}{q_e}$ versus $\frac{1}{C_e}$ plot in Origin Lab Software program. For plotting regression line or fitting the equation, the Langmuir isotherm may be expressed by the following equation, or equilibrium constant $R_l$ which is dimensionless.

(4) $\mathit{\hspace{0.17em}}{R}_l=\mathit{\hspace{0.17em}}\frac{1}{1+\left(1+{\mathit{K}}_{\mathit{l}}{C}_0\right)}$(4)

where, C₀ = initial concentration of the solution, R_l = a constant related to the energy of adsorption by surface area or the Langmuir constant. R_l constant indicates; if R_l >1, nature of adsorption to be either unfavorable, if R_l = 1, nature of adsorption to be favorable and linear, if, 0 < R_l <1, nature of adsorption to be favorable, if, R_l = 1, nature of adsorption to be irreversible.

Freundlich adsorption isotherm

For heterogeneous surfaces, the Freundlich adsorption isotherm scientifically described the adsorption properties and characteristics in 1859. Freundlich (1906), postulated an equation for multilayer adsorption onto heterogeneous adsorption process. Freundlich proposed the following empirical equation for modeling data:

(5) $Q_e=K_f{C}_e^{\frac{1}{n}}$(5)

For data modeling, the following log-based linear formula is found:

(6) $\mathit{log}{Q}_e=\mathit{log}{K}_f+\frac{1}{n}\mathit{log}{C}_e$(6)

where, K_f = Freundlich adsorption isotherm constant, n= adsorption intensity of this process, Ce = equilibrium concentration of adsorbate, and Q_e = amount of metal absorbed per gram of adsorbent at equilibrium stage. The constant K_f indicates the adsorption capacity of this process, while $\frac{1}{n}$ is a function of strength in an adsorption process. It assumes that, if n = 1, then the partition of the two phases is independent, if $\frac{1}{n}$ <1, then it indicates a normal adsorption, if $\frac{1}{n}$ >1, then it indicates cooperative adsorption. The linear square method is widely used to describe n and K_f parameter. We used Origin program for fitting the linear data model.

Temkin adsorption isotherm

The adsorbate–adsorbate interactions on the adsorption process were drive by Temkin isotherm model (Temkin & Pyzhev, 1940). The Temkin isotherm is valid only for an intermediate range of ion concentrations (Shahbeig, Bagheri, Ghorbanian, Hallajisani, & Poorkarimi, 2013).

Temkin Adsorption isotherm implies that the heat of adsorption of all molecules in the layer would not be decreased logarithmically but linearly by ignoring the extremely low and high concentration value. In the implementation of this equation between absorbate and absorbent, the maximum value of binding energies was carried out by plotting Q_e against lnC_e and the constant of this equation is determined by the linear equation y = mx + c. Temkin equation follows:

(7) $Q_e=\frac{\mathit{RT}}{b}\ln \left(A_T{C}_e\right)$(7)

(8) $Q_e=\frac{\mathit{RT}}{b_T}\mathit{ln}{A}_T+\left(\frac{\mathit{RT}}{b}\right)\mathit{ln}{C}_e$(8)

Lets, $\mathit{B}=\frac{\mathit{RT}}{b_T}$

Thus, the equation is written following linear form,

(9) $Q_e=\mathit{Bln}{A}_T+\mathit{Bln}{C}_e$(9)

Here, $A_T=$ the binding constant of Temkin isotherm, b_T = the Temkin isotherm constant, R= the molar gas constant, T= the temperature variable at K, and B= the constant related to heat sorption.

Hill isotherm model

Hill-Deboer Model. The Hill-Deboer isotherm model describes a case where there is mobile adsorption as well as lateral interaction among adsorbed molecules (De Boer, 1953; Hill, 1946). The linearized form of this isotherm equation is as follows (Kumara, Ramalingamb, Kiruphac, Murugesanc, & Vidhyarevicsivanesam, 2010):

(10) $\mathit{ln}\left(\frac{C_e\left(1-\mathit{\theta }\right)}{\mathit{\theta }}\right)-\frac{\theta }{1-\mathit{\theta }}=-\mathit{ln}{K}_1-\frac{K_2\mathit{\theta }}{\mathit{RT}}$(10)

where $K_1$ is Hill-Deboer constant and $K_2$ is the energetic constant of the interaction between adsorbed molecules. Equilibrium data from adsorption experiments can be analyzed by plotting ln [$\mathit{Ce}$ (1 − $\mathit{\theta }$)/$\mathit{\theta }$] − $\mathit{\theta }$/(1 − $\mathit{\theta }$) versus $\mathit{\theta }$ [Kumara et al. 20010].

Elovich isotherm model

The literature claims that Dusart et al (Dusart, Bouabane, & Mazet, 1991) published research article is the first original source of the Elovich adsorption isotherm. But, Roginsky and Zeldovich was the first exponential function proposed authors (Landsberg, 1955; Roginsky & Zeldovich, 1934). The equation was rearranged by (Dusart, Bouabane, & Mazet, 1991), and the equation is consist of two parameters in it linear form. The Elovich adsorption isotherm linear form is given as below (Ayawei, Ebelegi, & Wankasi, 2017):

(11) $\ln \frac{{\mathrm{q}}_{\mathrm{e}}}{{\mathrm{C}}_{\mathrm{e}}}=\mathrm{lnK}{\mathrm{q}}_{\mathrm{m}}-\frac{{\mathrm{q}}_{\mathrm{e}}}{{\mathrm{q}}_{\mathrm{m}}}$(11)

The maximum adsorption capacity and adsorption constant can be calculated from ln qe/Ce versus qe curve slope and intercept, respectively. The Elovich model can study the adsorption rate based on absorption capacity on heterogeneous surfaces.

(Dusart, Bouabane, & Mazet, 1991) used the linearized Elovich equation to describe the adsorption of amino acids by activated carbon. A follow-up study published in 1995 by Ferrandon et al. applied the linearized expression to the adsorption data of several water pollutants. The Elovich equation lay dormant until the mid-2000s when [(Mec¸abih, Kacimi, & Bouchikhi, 2006)], Hamdaoui [(Hamdaoui, 2006)], and Hamdaoui and Na rechoux [(Hamdaoui & Naffrechoux, 2007)] used the linearized Elovich expression to fit the adsorption data of several water contaminants. Today, when using the Elovich equation to fit water contaminant adsorption data, the linearized form is preferred.

Redlich-peterson isotherm

The Redlich-Peterson isotherm is three-parameter isotherms. The Redlich-Peterson isotherm model is a combination of the Langmuir and Freundlich isotherms. This model is an empirical isotherm model incorporating three parameters. The principle of this adsorption isotherm is a mix of both Langmuir and Freundlich equations and does not follow ideal monolayer adsorption (Brouers & Al-Musawi, 2015). The Redlich-Peterson isotherm model can be expressed linear form as follows (Kiseler, 1958):

(12) $\mathit{ln}\frac{C_e}{q_e}=\mathit{\beta ln}{C}_e-\mathit{lnA}$(12)

where A = constant of Redlich-Peterson isotherm, β is exponent that lies between 0 and 1, Ce = concentration of the adsorbent at equilibrium liquid-phase, and qe = equilibrium of adsorbate loading on the adsorbent. The plot of (Ce/qe) versus ln Ce can determination the Redlich-Peterson constants, where slope is β and intercept is A (Brouers & Al-Musawi, 2015; Chan, Cheung, Allen, & McKay, 2012; Ng, Cheung, & McKay, 2002; Wu, Liu, Wu, & Tseng, 2010). The linear Redlich-Peterson isotherm model is dependent on concentration. This is versatile model in both homogeneous or heterogeneous systems and the adsorption equilibrium is representing a wide range of concentration of adsorbate (Gimbert, Morin-Crini, Renault, Badot, & Crini, 2008; Sips, 1948).

Adsorption diffusion models

Intra-particle diffusion model

Webber-Morris expressed an important adsorption process that the uptake of adsorbates varies proportionally with the square root of time (t ^1/2) (Ben Hamissa, Brouers, Ncibi, & Seffen, 2014). The linear form of the model is as follows (Vadivelan & Vasanth, 2005);

(13) $q_t=K_{\mathit{IPD}}{t}^{\frac{1}{2}}+\mathit{C}$(13)

Here, K_IPD is intra-particle diffusion constant, and C is intercept. The intra-particle diffusion alone is the rate determining step then a plot of qt vs t ^1/2 appears as a linear graph.

Elovich model

The Elovich model is describing adsorption processes that follow second-order

kinetics which is involved in chemical absorption without desorption of the products in the surface of the heterogeneous nature adsorbent (Cheung, Porter, & McKay, 2000; Dotto & Pinto, 2011). The Elovich model can be written as follows (Elovich & Larionov, 1962; Wu, Tseng, & Juang, 2009):

(14) $q_t=\frac{\ln \left(\mathit{\alpha \beta }\right)}{\beta }+\frac{\mathit{lnt}}{\beta }$(14)

Here, qt is adsorption capacity at any time t, α is initial adsorption rate, β is adsorption constant. Both the initial adsorption rate (α) and adsorption constant (β) can be determined from a linear plot of qt versus lnt.

Boyd model

The Boyd model predicts the rate determining step (slow step) involved in the adsorption process. The Boyd kinetic model is represented by the following expression (Badruzzman, Westerhoff, & Knappe, 2004; Boyd, Schubert, & Adamson, 1947):

(15) $\mathit{F}=\frac{q_t}{q_e}=\left(1-\frac{6}{{\pi }^2}\right)\mathit{exp}-B_t$(15)

(16) $B_t=-0.4977-\mathit{ln}\left(1-\mathit{F}\right)$(16)

Here, F = the fraction of metal ion adsorbed at any time, t; q_e = amount adsorbed at equilibrium, q_t = amount adsorbed at time t, and B_t = mathematical function of F. If B_t vs t plot is a straight line, then the processes is governed by Boyd kinetic model.

Pseudo-second order reaction equation

The expression of Pseudo-first order reaction is

(17) $\frac{d{q}_t}{\mathit{dt}}=k_n{\left(q_e-q_t\right)}^{\mathit{n}}$(17)

When n = 2, the expression is obtained for the pseudo-second-order reaction, which follows:

(18) $\frac{d{q}_t}{\mathit{dt}}=k_n{\left(q_e-q_t\right)}^2$(18)

By integrating this equation, the conditions t = 0 and t = t gives the following form:

(19) $q_t=\frac{q_e^2\times k_2\mathit{t}}{q_e{k}_2\mathit{t}+1}$(19)

The linear mathematical model of this equation is written in the following form

(20) $\frac{t}{q_t}=\frac{1}{k_2{q}_e^2}+\frac{1}{q_t}\mathit{t}$(20)

where, $q_e$= the amount of adsorbate in the adsorbent at equilibrium, $k_2$=constant rate for second order, t = contract time between solid and liquid

Thermodynamics

In this process, thermodynamics parameters such as free energy of adsorption, the heat of the adsorption process, and standard entropy changes can be evaluated by the following equation

(21) $k_d=\frac{q_e}{C_e}$(21)

where k_d is adsorption distribution coefficient. The k_d values are used to determine Gibbs’s free energy at different temperature ranges. Thus, the Gibbs free energy is

(22) $\mathrm{\Delta }{G}^0=-\mathit{RTln}{K}_d$(22)

where, ΔG⁰ = is the free energy of the adsorption process(kJ/mol), T= is the Kelvin temperature, and R=is the universal gas constant (8.316 J/mol K)

The distribution constant of this process is determined by the term’s standard entropy changes and temperature changes function

(23) $\mathit{ln}{K}_d=\frac{\mathrm{\Delta }{H}^0}{\mathit{RT}}+\frac{\mathrm{\Delta }{S}^0}{\mathit{R}}$(23)

where, ΔH⁰ = is the heat of adsorption (kJ/mol), ΔS⁰ = is the standard entropy changes (kJ/mol). The values of ΔH⁰ and ΔS⁰ are obtained by plotting 1/t vs ln k(l) in origin lab software.

Result and discussion

In Figure 1, XRD peaks are very sharp and having high intensity which means particles are solid crystalline. We calculated the particles size using Scherrer equation and obtained size was 44.2107825 nm. The particles size indicating the brick sand particles are nanoparticles in size. The Scherrer equation which was used to estimate the average particle size of brick particles as follows:

Figure 1. XRD spectrum of brick sand particles.

(24) $\mathit{D}=\frac{\mathit{K\lambda }}{\mathit{\beta COS\theta }}$(24)

Here, D = Crystalline Size (nm), β = Line Broadening at FWHM -Radians, θ (Bragg Angle) = Peak Position (Radians), and λ = X-ray Wavelength K= Scherrer Constant, 2θ= 26.68542 degrees.

The Figure 2 is illustrating the impact of contact time on Pb, Cd, and Cr metal removal from aqueous solution using both untreated and treated BSNP as an adsorbent. Figure 2(a) is representing effect of contact time using untreated BSNP, and Figure 2(b) is representing effect of time using treated BSNP as an adsorbent. In case of removal of Cr, there is no effect contact time in both adsorbent as it is shown in Figure 2(a, b). And in both cases, 99% Cr metal was removed from aqueous solution. But in case of Pb and Cd, time is showing a significant in both adsorbent as we can see in Figure 2. In removal of Pb metal, removal % of Pb metal fluctuated with respect to time and maximum 97 % of Pb metal was removed for untreated BSNP and 98.385 % for treated BSNP within time 20 min. In removal of Cd metal, time has also significant effect like Pb metal. The maximum 96 % of Cd metal was removed at 40 min using treated BSNP as shown in Figure 2.

Figure 2. a) removal of heavy metals (pb, Cd & Cr) using untreated and b) removal of heavy metals (pb, Cd & Cr) using treated brick nano particles as an adsorbent with respect to time.

The effect of temperatures is showed in the following Figure 3. In the removal rate of Cr metal, there were no significant effect on temperature range with this experiment in both adsorbent. But in case of Pb, temperature has a significant effected. At temperature 40°C, treated BSNP had the highest removal rate 98.365% for Pb as shown in Figure 3(b), while at temperature 40°C and 60°C, untreated BSNP demonstrated the effective removal rate 97%. At 30°C temperature, the removal rate of Cd metal using untreated BSNP was the highest (94.865%), while at 40°C temperature, it was the lowest (90.2%) as shown in Figure 3(a, b), respectively. However, the removal rate for Cr metal was nearly 99% which was the highest among Pb, Cr, and Cd as shown in Figure 3.

Figure 3. a) removal of heavy metals (pb, Cd & Cr) using untreated and b) removal of heavy metals (pb, Cd & Cr) using treated brick nano particles as an adsorbent with respect to temperature.

Acidic and basic media were used to show the removal of Pb, Cd, and Cr, as indicated in the Figure 4. Removal of Pb with untreated BSNP in slightly basic conditions was best. At pH 7.35 to 7.6, the maximum removal rate which was 97% as shown in Figure 4(a). By lowering the pH, the removal rate of Pb was significantly reduced. Cd had the maximum removal percentage for untreated brick particles at a 6.12 pH of 94.865% as shown in Figure 4(a). However, treated brick particles resulted in higher Cd removal percentages at lower pH (at 5.3), which was 99.2% represented by Figure 4(b). Both treated and untreated brick particles were found to have more than sufficient removal capability in mildly basic and acidic environments. Both slightly acidic and basic media successfully removed over 98.9% to 99 % of Cr from untreated and treated brick particle environments as shown in Figure 4.

Figure 4. a) removal of heavy metals (pb, Cd & Cr) using untreated and b) removal of heavy metals (pb, Cd & Cr) using treated brick nano particles as an adsorbent with respect to pH.

The pH of the solution is an important parameter influencing the adsorption process and it can also affect the adsorbent surface charge (Mayakaduwa et al., 2016). This important property can be explained by analyzing point of zero charge. The point of zero charge value of brick sand nanoparticles is 7.5 as shown in Figure 5. From the Figure 5, when pH of the solution is lower than pHpzc, the surface of the adsorbent is predominating by negative charge, on the other hand, at the pH values above pHpzc, the surface charge of the adsorbent is positive in charge. Therefore, we may conclude that the adsorption process is dominating by slightly acidic to slightly basic media and which is agreed with the applied experimental condition.

Figure 5. Point zero charge (pH_PZC) for adsorbent.

Studies on adsorption isotherms

Langmuir Adsorption Isotherm Model: In this research was investigated for both untreated and treated BSNP adsorbent efficiency in which experimental data of Pb were fitted with Langmuir adsorption isotherm model using origin Lab software. And from the plot $\frac{1}{q_e}$ versus $\frac{1}{C_e}$ and equation 4(4) $\mathit{\hspace{0.17em}}{R}_l=\mathit{\hspace{0.17em}}\frac{1}{1+\left(1+{\mathit{K}}_{\mathit{l}}{C}_0\right)}$(4) , calculated the values of $R_l$ were 0.857083 and 0.9397008, respectively, which were less than 1 as mentioned in Table 1. Following the Langmuir Adsorption Isotherm assumption, the untreated and treated brick particles is showed favorable adsorption isotherm performance. The results showed that the untreated and treated BSNP have good correlation coefficients ($R^2$= 0.9998 and 1) which is indicating an acceptable agreement between experimental data and isotherm parameters as shown in Table 1 and shown in Figure 6(a, b).

Figure 6. Fitting of Longmuir adsorption isotherm model for removal of heavy metals using untreated and treated brick sand nanoparticles for Pb [(a) &; (b)], Cd [(c) &; (d)] and Cr [(e) &; (f)].

Table 1. Summary of the three-adsorption model used in the study for removal of heavy toxic metals (pb, Cd & Cr) from aqueous solution using brick sand nanoparticles as an adsorbent.

Toxic Heavy Metals	Brick Nano Particle	Langmuir Adsorption Isothermal Model		Freundlich Adsorption Isothermal Model		Temkin Adsorption Isothermal Model
		Rl	R^2	1/n	R^2	$B_T$ (Jmole^−1)	$K_T$ ($\mathit{Lm}{g}^{-1}$)	R^2
Pb	Untreated	0.857083	0.9998	0.9974	0.9992	0.35882	0.141688	0.9895
	Treated	0.9397008	1	0.99874	1	0.21589	0.2387362	0.9899
Cd	Untreated	1.03829702	0.99998	1.0033	0.99997	10.498	0.07633	0.99018
	Treated	0.40585348	1	0.99815	1	5.233	0.24189	0.82805
Cr	Untreated	1.19629317	0.9996	1.00113	0.99971	0.5603	0.487737	0.99839
	Treated	0.621451968	0.9997	0.99561	0.999784	0.4918	0.557	0.99598

In case of Cd metal, the $R_l$ values for untreated and treated BSNP were 1.0382970 and 0.405853488, respectively, as shown in Table 1. Among the two values of $R_l$, one being more significant than one and the other being less than 1. In agreement with the Langmuir Adsorption Isotherm assumption, untreated brick particles show unfavorable isotherm performance, while treated brick particles showed the best ability. Both untreated and treated brick particles have decent correlation coefficients (=0.99998 and 1), noting experimental data and isotherm parameters negotiated as shown in Figure 6(c, d).

In case of Cr metal, the value of R_l both untreated and treated BSNP were 1.19629317 and 0.405853488, respectively. The one $R_l$ value was greater than one and the other less than 1 as shown in Table 1, according to the Langmuir Adsorption Isotherm assumption, untreated brick particles have unfavorable adsorption isotherm performance, whereas treated brick particles have the better isotherm performance (R_l was 0.405853488). The untreated and treated brick particles both have a good correlation coefficients (R² =0.9996 and 0.9997), indicating that the experimental data and isotherm parameters agreed well enough as shown both in Table 1 and Figure 6(e, f).

Freundlich Adsorption Isotherm Model: Table 1 showed that the 1/n values for both the untreated and treated brick particles are 0.9974 and 0.99874 for removal of Pb metal and which were less than 1. The Freundlich Adsorption Isotherm indicated that it is favorable for isotherm and that the adsorbent quality is outstanding. The value of R² for untreated and treated brick particles has values of 0.9992 and 1, indicating a better adsorption isotherm as shown in figure 7(a, b).

Figure 7. Fitting of Freundlich adsorption isotherm model for removal of heavy metals using untreated and treated brick sand nanoparticles for Pb [(a) &; (b)], Cd [(c) &; (d)] and Cr [(e) &; (f)].

In case of Cd metal, the 1/n values for untreated and treated BSNP were 1.0033 and 0.99815, respectively, as shown in Table 1. From the 1/n value, it can be easily assumed that untreated BSNP was less effective adsorbent than the treated BSNP adsorbent. The fitting of experimental data with Freundlich adsorption isotherm using origin software, the R² values were 0.99997 and 1 for untreated and treated BSNP adsorbents, respectively. The resultant values were indicating that the adsorbent meets the Freundlich Adsorption Isotherms as shown in figure 7(c, d).

For removal of Cr metal, the 1/n values for untreated and treated BSNP were 1.00113 and 0.99516, respectively. The 1/n value of treated BSNP was better than untreated BSNP adsorbent. The R² values for untreated and treated BSNP were 0.99971 and 0.999784, respectively, showing that both adsorbents satisfied the Freundlich Adsorption Isotherms as shown in Figure 7(e, f) respectively.

Temkin Adsorption Isotherm Model: The untreated and treated BSNP Temkin Adsorption Isotherm for Pb metal are shown in Figure 8. In both untreated and treated BSNP adsorbents, the value of R² were nearly 1 as shown in figure 8(a, b), respectively. This value indicates that both of the adsorbents perform exceptionally well. The values of B_T were 0.35882 and 0.21589 for untreated and treated BSNP adsorbents, the K_T values were 0.141688 and 0.2387362, indicating that it performed Temkin Adsorption Isotherm signify good as shown in Table 1.

Figure 8. Fitting of Temkin adsorption isotherm model for removal of heavy metals using untreated and treated brick sand nanoparticles for Pb [(a) &; (b)], Cd [(c) &; (d)] and Cr [(e) &; (f)].

For removal of Cd metal, using untreated and treated BSNP adsorbents, the B_T and K_T values in Temkin Adsorption Isotherm were significantly good, which were 10.498 and 0.07633, 5.233 and 0.24189, respectively, as shown in Table 1. These values are indicating that both the untreated and treated BSNP adsorbents are satisfied Temkin Adsorption Isotherm very well. The values of R² are 0.99018 and 0.82805, which indicates that untreated BSNP was perform well and treated one as shown in Figure 8(c, d).

In case of removal Cr metal, the values of R² both untreated and treated BSNP adsorbents were 0.99839 and 0.99598 as shown in Table 1 and figure 8(e, f). The values of R² are indicating that both untreated and treated BSNP adsorbents are better performing in removal of Cr metal and satisfied Temkin Adsorption Isotherm. The B_T and K_T values for untreated and treated BSNP were 0.5603 and 0.487737 and 0.4918 and 0.557, respectively, as shown in Table 1.

Hill De Boer Adsorption Isotherm: The Figure 9 is representing the experimental data fitted with Hill De Boer adsorption isotherm. The Hill-Deboer constant K₁ is describing the interaction between adsorbent and adsorbate. The higher value of K₁ indicates a good interaction between adsorbent-adsorbate (Ragadhita & Nandiyanto, 2021). The K₁ values are showing in Table 2. The all values of K₁ are very favorable for adsorbent and adsorbate interaction. But due to the value of R² in the adsorption process, the Cr metal removal percentage became higher than others.

Figure 9. Fitting of Hill De Boer adsorption isotherm model for removal of heavy metals using untreated and treated brick sand nanoparticles for Pb, Cd, and Cr.

Table 2. Summary of the hill de boer, elovich and redlich-petersons adsorption model used in the study for removal of heavy toxic metals (pb, Cd & Cr) from aqueous solution using brick sand nanoparticles as an adsorbent.

Toxic Heavy Metals	Brick Nano Particle	Hill De Boer Adsorption Isothermal Model		Elovich Adsorption Isothermal Model		Redlich-Peterson Adsorption Isothermal Model
		K1	R^2	Qm (mg/g)	R^2	B	-lnA	R^2
Pb	Untreated	11.93448	0.9625	−1.85224	0.02307	0.0026	3.99523	0.00905
	Treated	6.7565	0.9839	−206.6518	0.98382	0.00126	3.99601	0.3993
Cd	Untreated	17.89942	0.9169	7542.30	0.85139	−0.0033	1.26206	0.3057
	Treated	4.46943	0.95487	22716.63	0.87029	0.00185	1.24438	0.90882
Cr	Untreated	14.96071	0.99426	−123.8708	0.99483	0.00126	3.99601	0.3993
	Treated	8.97856	0.9942	−101.8680	0.9951	0.00439	2.29971	0.1056

Elovich Adsorption Isotherm: The Figure 10 is representing the experimental data fitted with Hill De Boer adsorption isotherm. The Hill-Deboer constant K₁ is describing the interaction between adsorbent and adsorbate. The higher value of K₁ indicates a good interaction between adsorbent-adsorbate [(Ragadhita & Nandiyanto, 2021)]. The K₁ values are showing in Table 2. The all values of K₁ are very favorable for adsorbent and adsorbate interaction. But due to the value of R² in the adsorption process, the Cr metal removal percentage became higher than others.

Figure 10. Fitting of Elovich adsorption isotherm model for removal of heavy metals using untreated and treated brick sand nanoparticles for Pb, Cd, and Cr.

Redlich-Petersons Adsorption Isotherm: The sorption of heavy toxic metal onto BSNP following the linear form of Redlich–Peterson isotherm was shown in Figure 11 which were plotted between ln (Ce/qe) versus ln (Ce). However, the Redlich–Peterson isotherm equation contains three unknown parameters. Redlich–Peterson isotherm equation was fitted with the experimental data and a theoretical data to maximize the determination of coefficient R² from the linearized form.

Figure 11. Fitting of redlich-petersons adsorption isotherm model for removal of heavy metals using untreated and treated brick sand nanoparticles for Pb, Cd, and Cr.

The Redlich–Peterson constants were calculated and their corresponding linear regression coefficient of determination were summarized in Table 2. It was observed from the Table 2 that the R² value for Redlich–Peterson isotherm was found to be more or less nearer to the best-fit Langmuir, Freundlich, Temkin, Elovich and Hill De Boer adsorption isotherm.

Adsorption diffusion models

Intra-particle diffusion model

From equation of the model, linear plotted qt versus t^0.5 were presenting in Figure 12. And the diffusion model data are summarized at Table 3. If the line pass through the origin means intraparticle diffusion is the sole rate-limiting step. If it was observed that plotted line did not go through the origin indication that intraparticle diffusion was not only the rate-limiting step, it is a multiple steps like boundary layer diffusion or other processes take place (Özacar, 2003). From the Figure 10, the kinetic modeling has shown that adsorption capacity increased with increasing temperature which recommends chemisorption reaction between the adsorbent and the metal ions (Ho & Ofomaja, 2006)]. As adsorption temperature increased, the mobility of the metal ions reduced because of chemisorption similarly reducing the diffusion of the metal ions into the adsorbent of the inner surface, resulting the reduction of intraparticle diffusion rate constant with temperature. Besides that, the values of intercept, C, recommended boundary diffusion or surface adsorption. It may conclude that surface adsorption was prominent as temperature was increased.

Figure 12. Fitting of Intra-particle diffusion model for removal of heavy metals using untreated and treated brick sand nanoparticles for Pb, Cd, and Cr.

Table 3. Summary of the intra-particle diffusion, elovich and boyd kinetic model used in the study for removal of heavy toxic metals (pb, Cd & Cr) from aqueous solution using brick sand nanoparticles as an adsorbent.

Metal		Intra-particle Diffusion			Elovich Kinetic Model			Boyd Model
	Condition	Kdiff	C	R^2	α	β	R^2	Intercept	R^2
Pb	Untreated	4.61352×10^−4	0.18989	0.14026	714.28	2.36×10^55	0.22069	3.52277	0.13186
	Treated	0.00401	0.15138	0.04375	207.90	7.11×10^11	0.01074	0.81317	1.0
Cd	Untreated	−8.99×10^−4	0.19008	0.12459	−418.41	3.44×10^−33	0.14999	2.90154	0.02762
	Treated	−0.00164	0.19616	0.09359	−261.09	1.053	0.08731	3.01221	0.79268
Cr	Untreated	−1.14152×10^−4	0.1985	0.36085	−0.405	2.66	0.28722	7.78749	0.98392
	Treated	−1.1051×10^−4	0.19864	0.24158	−0.334	2.80	0.30242	6.49053	0.05287

Elovich Kinetic Model: The assumption of t>>t0 and validity of Equation (14)(14) $q_t=\frac{\ln \left(\mathit{\alpha \beta }\right)}{\beta }+\frac{\mathit{lnt}}{\beta }$(14) is checked by the linear plot of qt vs. ln t. Table 3 shows that when the mass of sorbent is fixed, the increase in concentration caused the constant a to increase but the constant b to decrease. When the concentration was fixed, the effect of increasing the mass of sorbent increased the constants a and b. The Figure 13 are showing Elovich kinetic model for removal of the heavy toxic metals Pb, Cd, and Cr. The kinetic model data are summarized in Table 3. The R² values of the adsorption isotherm are indicating that the adsorption processes are favorable except the removal of Pb using untreated BSNP.

Figure 13. Fitting of Elovich kinetic model for removal of heavy metals using untreated and treated brick sand nanoparticles for Pb, Cd, and Cr.

Boyd Model: The diffusion is the limiting step of the adsorption process, due to the adsorbent particle. The experimental data were adjusted to the Boyd kinetic expression as shown in Figure 14. The experimental data did not pass through the origin and did not follow absolutely the linear trend. This experimental data was demonstrated and controlled both by the effects of intraparticle diffusion and external diffusion. The kinetic model data are summarized in Table 3.

Figure 14. Fitting of Boyd model for removal of heavy metals using untreated and treated brick sand nanoparticles for Pb, Cd, and Cr.

Pseudo 2nd Order Reaction Kinetics: The Pb kinetics parameter for untreated and treated brick particles for removal of Pb metal is shown in Table 4 and Figure 15. Untreated brick particles K₂ value is −84.1668, which is negative. But in treated brick particles K₂ value is 0.69481055, which is positive. The value of R² for untreated and treated brick particles is 0.99967 and 0.84244, respectively. R² values indicate that untreated brick particles are better performed than treated brick particles.

Figure 15. Kinetic for removal of heavy metals using untreated and treated brick sand nanoparticles for Pb [(a) &; (b)], Cd [(c) &; (d)] and Cr [(e) &; (f)].

Table 4. Pseudo 2nd order reaction kinetics parameter for untreated and treated brick sand nano-particles for removal of heavy toxic metals Pb, Cd, and Cr.

Metals	Condition	K2	R^2
Pb	Untreated	−84.1668	0.99967
	Treated	0.69481055	0.84244
Cd	Untreated	−20.6399	0.998
	Treated	−5.92044	0.9846
Cr	Untreated	−83.021	0.9998
	Treated	−1495.65	0.9999

The Cd metal kinetics parameter for untreated and treated brick sand particles for removal of Cd metal is shown in Table 4 and Figure 15. K₂ value for untreated and treated brick particles is −20.6399 and −5.9204, both negative. It is indicated that both are performed well as an adsorbent. The value of R² is 0.998 and 0.9846 for untreated and treated brick particles.

In case Cr metal, kinetics parameter for untreated and treated brick sand nanoparticles for removal of Cr metal is shown in Table 4 and Figure 15. Untreated brick particles and treated brick particles K₂ values are −83.021 and −1495.65, which are negative. The value of R² for untreated and treated brick particles are 0.9998 and 0.9999, respectively. R² values indicate that untreated and treated brick particles are good as adsorbents

To get insight into the adsorption process, study the thermodynamic characteristics of the process, such as activation enthalpy ΔH⁰ (KJmol⁻¹), activation entropy ΔS (Jmol⁻¹ K⁻¹) in the below Table.

Thus, the temperature-dependent free Gibb’s energy is

(25) $\mathrm{\Delta }{\mathrm{G}}^0=\mathrm{\Delta }{\mathrm{H}}^0-\mathrm{T}\text{DeltaS}$(25)

Here, T is the temperature of the process 30°C to 70° C.

Now, when we utilized different temperature ranges, we noticed that the ΔG⁰ values were all positive, suggesting that the adsorption reaction needed some external energy source to turn reactants into products. When the value ofΔH⁰ positive, it suggests that the adsorption process is endothermic, and when the values are negative, it indicates that the adsorption phenomena are exothermic. ΔH value of untreated and treated Pb is 49.012 and 2.0537, respectively, which are positive. For the assumption of thermodynamics, we decide that this reaction occurs endothermically. But for Cd, ΔH⁰ value is −37.032 and 34.21. Similarly, we say that untreated brick particle’s reaction is exothermic and treated brick nanoparticle’s reaction is endothermic. In Cr, ΔH⁰ is negative for both treated and untreated brick particles. Therefore, we say that untreated and treated Cr reaction is exothermic Table 5.

Table 5. Thermodynamics parameter for untreated and treated brick sand nano-particles (pb, Cd, and Cr) using R = 8.316 Jmol⁻¹K^−1..

Metals	ΔH^0 (Jmol^−1)	ΔS (Jmol^−1 K^−1)	T (Temperature)	ΔG^0
Pb (untreated brick nanoparticles)	49.012	−33.135	30° C = 303 K	10.08
			40° C = 313 K	10.42
			50°C = 323 K	10.75
			60° C = 333K	11.08
			70°C = 343 K	11.41
Pb (treated brick nanoparticles)	2.0537	−33.250	30° C = 303 K	10.07
			40° C = 313 K	10.4
			50°C = 323 K	10.74
			60° C = 333K	11.07
			70°C = 343 K	11.4
Cd (untreated brick nanoparticles)	−37.032	−10.51	30° C = 303 K	3.14
			40° C = 313 K	3.25
			50°C = 323 K	3.35
			60° C = 333K	3.46
			70°C = 343 K	3.56
Cd (treated brick nanoparticles)	34.21	−10.29	30° C = 303 K	3.15
			40° C = 313 K	3.25
			50°C = 323 K	3.35
			60° C = 333K	3.46
			70°C = 343 K	3.56
Cr (untreated Brick Particles)	−69.64	−19.39	30° C = 303 K	5.80
			40° C = 313 K	5.99
			50°C = 323 K	6.19
			60° C = 333K	6.38
			70°C = 343 K	6.58
Cr (untreated brick nanoparticles)	−86.33	−19.449	30° C = 303 K	5.80
			40° C = 313 K	6.00
			50°C = 323 K	6.19
			60° C = 333K	6.39
			70°C = 343 K	6.58

Bot h the magnitude and the sign of ΔS in the adsorption response offer an indication of whether or not it is a dissociative or an associative mechanism. The negative result may suggest that the associative mechanism is involved in adsorption. During the process of adsorption, the activation of a complex between the adsorbate and the adsorbent is what eventually leads to the formation of order. A negative number indicates that the internal structures of the adsorbent undergo no significant change throughout the adsorption process. This is another consequence of the ΔS value. The positive result may indicate that the dissociative mechanism is involved in adsorption. For the assumption of thermodynamics, we say that all untreated and treated brick particles have involved a dissociative adsorption mechanism [Table 5]. When temperatures are high, adsorption processes like this are not likely to be successful.

EDS results of untreated brick particle samples and untreated and treated brick particles sample are shown below in Figures 16 and 17, respectively. The untreated brick sample clearly shows that there is no Pb, Cd, and Cr present and the weight% and atomic% are also zero (inset table in the Figures). To compare with the Figures 16(b) and 17(a) (untreated and treated brick particles for Pb), it is found that for untreated brick particles, Pb is present in the sample 1.74 its weight% and 0.17 its atomic%, and for treated brick particles Pb is present 1.41 its weight% and 0.14 atomic%. The results are indicting the adsorption of Pb metal in untreated and treated bricks sand nanoparticles surface in significant manner. To compare with untreated brick particles sample and untreated and treated brick particles with Cd sample Figure 16(a,c) and Figure 17(b), it is clearly shown that Cd is present in untreated brick particles sample it’s 0.63 weight% and 0.11 it’s atomic %, and for treated brick particles sample Cd is present 0.21 its weight % and 0.04 atomic %. That’s mean the Cd metal are successfully adsorbed by brick sand nanoparticles. In the Figures 16(d), and 17(c), for untreated and treated brick particles adsorbent, it is clearly shown that Cr present in untreated brick particles sample is 0.94 its weight % and 0.37 atomic% and for the treated brick particles sample, the present of Cr it’s 1.00 weight % and 0.39 atomic %. The EDS results ensured the adsorption of Cr metal onto the surface of brick sand nanoparticles are quite successful. The above investigation proved that the adsorption quality of brick particle samples treated and untreated are relatively good for removal of heavy toxic metals Pb, Cd, and Cr from aqueous solution.

Figure 16. EDS spectrum of untreated brick sand nanoparticles(a), untreated brick sand nanoparticles adsorbed Pb (b), untreated brick sand nanoparticles adsorbed Cd (c), untreated brick sand nanoparticles adsorbed Cr (d).

Figure 17. EDS spectrum of treated brick sand nanoparticles adsorbed Pb (a), treated brick sand nanoparticles adsorbed Cd (b), treated brick sand nanoparticles adsorbed Cr (c).

According to physical properties, the major advance of the Third-class brick is -it can absorb about 25 % moisture or water of dry weight. The major drawback of this adsorbent is a) it is very soft, and b) it is not suitable for prolong time, c) it needs to change periodically.

Conclusion

BSNP is quite cheap, easily available, and effective adsorbent. From the XRD analysis, it was shown it crystallinity and nanoparticle properties. In this experiment both untreated and treated brick sand nanoparticles were used as an adsorbent for removal of heavy toxic metals from solution and shown the most significant removal capacities for Pb (97%), Cd (99%), and Cr (99%). The Langmuir, Freundlich, Temkin, Hill De Boer, Elovich, and Redlich-Peterson Isotherm Models are also almost agreed with the adsorption properties as well as capacities of brick sand nanoparticles. Moreover, the EDS analysis was also ensured the adsorption of heavy toxic metals onto the surface of brick sand nanoparticles. The thermodynamics properties of BSNP were studied are faviourable for this adsorbent. From the above discussion, we may conclude that brick sand nanoparticles have an excellent adsorption capacity and better removal properties for removal of Pb, Cd, and Cr from aqueous solution. Finally, we may suggest that the brick sand nanoparticles can be an effective, economical, and ecologically acceptable alternative adsorbent for removal of toxic metals from wastewater.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Rajshahi University [The grant no. ref. A1380/5/52/ University Grant Commission (Bimog) / Engineering/2/18-19].