Cu(PABA) is a Cu-based MOF material assembled from Cu2+ and the organic ligand p-aminobenzoic acid (PABA). Cu (PABA) was synthesized by a solvothermal method, characterized and applied to the adsorption of direct red 31 dye (DR-31). The effects of pH, DR-31 concentration and temperature on the adsorption performance of Cu(PABA) were investigated. The adsorption kinetics were analyzed by pseudo-first-order, pseudo-second-order and intra-particle diffusion models, and the adsorption equilibrium data was fitted by Langmuir and Freundlich isotherm models. The pseudo-first-order kinetics and Langmuir model satisfactorily described the adsorption kinetics and adsorption equilibrium, respectively. The maximum adsorption capacity of Cu(PABA) for DR-31 dye at room temperature was 1,244.8 mg/g, as calculated using the Langmuir adsorption isotherm model. By response surface methodology (RSM), the optimal adsorption was found at pH value of 10.9, DR-31 dye concentration of 216.6 mg/L, and temperature of 27 °C, and the removal rate was as high as 99.4%. Therefore, Cu(PABA) can be used as an efficient adsorbent for removing DR-31 dye from aqueous solution.

  • MOF material Cu(PABA) was synthesized by a solvothermal method.

  • The kinetics and isotherm models of DR-31dye adsorption on Cu(PABA) were studied.

  • According to the Langmuir isotherm model, the maximum adsorption capacity was calculated to be 1,244.8 mg/g.

  • Optimization of DR-31dye adsorption on Cu(PABA) by RSM.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Since modern times, dyes have been widely used in various places, such as papermaking, textiles, plastics, food and other industries (Faisal et al. 2022). This has resulted in the production of a large amount of wastewater contaminated with dyes. Because dyes are easily diffused, difficult to degrade, and toxic, they are a serious threat to the water environment. Large amounts of dyes are released into the water and accumulate over time, resulting in a decline in water quality and a large number of aquatic life deaths. In addition, some dyes can also endanger human health, cause various diseases, and even cause cancer. Therefore, people have to pay attention to the method of remediation of dye-contaminated water. Direct red 31 (DR-31) dye is a common synthetic dye and is easily soluble in water. It is mostly used for dyeing and printing silk, wool, paper, leather and other fibers, as well as for coloring paint, plastic, rubber, etc (Bhatia et al. 2016). DR-31 is an azo dye, which can secrete carcinogens and induce cancer under certain conditions. Therefore, it is necessary to remove it from the water environment.

Dye pollution in wastewater can be removed by physical, chemical or biological methods. Depending on the type of dye, the remediation technologies that can be used for dye wastewater treatment include adsorption, filtration, coagulation, photooxidation, chemical oxidation, ozonation and biodegradation, etc (Katheresan et al. 2018). Among these technologies, adsorption technology is one of the most commonly used sewage treatment methods due to its advantages of low cost, high efficiency, more environmental protection, convenient operation and easy recovery and reuse of adsorbents (Chowdhury et al. 2020). For adsorption, the removal effect of pollution mainly depends on the adsorption capacity of the adsorbent, so the selection of adsorbent is very important. At present, common adsorbents include zeolite, fly ash, silica gel, activated carbon and chitosan (Sadiq et al. 2021). Although these adsorbents are already available, sometimes they cannot achieve satisfactory results, so people hope to develop cheaper and more efficient adsorbents.

Metal organic frameworks (MOFs) are a class of porous hybrid materials, which are formed by the combination of metal ions and organic ligands through coordination bonds. MOFs are widely used in various fields such as gas storage and separation, chemical sensors, electrochemistry, catalysis, adsorption and so on. In the field of adsorption, MOFs are considered to be an ideal adsorption material due to their large surface area, high porosity and adjustable pore size (Oladoye et al. 2021). In fact, MOF materials used as adsorbents for heavy metals and organics in water have indeed received a lot of attention in recent decades (Oladoye et al. 2021). Cu(PABA) is a kind of Cu-MOF material. There are few reports about it at present, and the use of its pure carrier as an adsorbent to remove the DR-31 dye in water has not been reported.

The adsorption and removal of pollutants by adsorbents is affected by many factors, such as initial concentration, adsorbent dosage, solution pH and temperature. To more reasonably and simply analyze the simultaneous effects of these variables on adsorption and obtain the ideal restoration effect, we optimized the experimental parameters by using the response surface methodology (RSM). In terms of optimization research, RSM is a proven and useful method. Compared with the commonly used single variable analysis method, RSM comprehensively considers the influence of various parameters, and can significantly reduce the amount of experiments, with obvious advantages.

In this paper, we synthesized the Cu-MOF material Cu(PABA) and used it as an adsorbent to remove DR-31 dye from solution. We investigated adsorption kinetics and adsorption isotherms, and evaluated the remediation ability of Cu(PABA) for DR-31 dye. We used single-factor and multi-factor methods to analyze the effects of the initial concentration of DR-31 dye, pH, and temperature on the adsorption. The experimental parameters were optimized by RSM. The results show that Cu(PABA) has high adsorption capacity and removal rate for DR-31 dye.

Materials

All reagents and drugs in this study are of analytical grade and can be used without further purification. Copper acetate monohydrate (Cu(CH3COO)2•H2O) and p-aminobenzoic acid (PABA) were purchased from Macklin. DR-31 dye was purchased from Shandong Yousu Chemical Technology Co., Ltd. Hydrochloric acid (HCl), sodium hydroxide (NaOH) and methanol (CH3OH) were all from Sinopharm Chemical Reagent Co., Ltd

Synthesis of Cu(PABA)

Cu(PABA) was synthesized by a solvothermal method, mixing copper acetate monohydrate (Cu(CH3COO)2•H2O) and p-aminobenzoic acid(PABA) at a ratio of 1.25:1. In detail, an electronic balance was first used to accurately weigh 0.218 g of Cu(CH3COO)2•H2O and 0.120 g of PABA. Cu(CH3COO)2•H2O was dissolved in 10 mL pure water, and PABA was dissolved in 6 mL pure water and 4 mL methanol mixed solution. Then, the cup containing Cu(CH3COO)2•H2O solution was placed on a magnetic stirrer and PABA solution was added drop by drop. After 6 hours of magnetic stirring, the mixture was centrifuged at 4,000 r. Finally, Cu(PABA) solid can be prepared by drying at 353 K.

Characterization

Microstructure and morphology of Cu(PABA) were characterized by scanning electron microscope (SEM, Zeiss Sigma300). The functional groups of Cu(PABA) were determined by Thermo Scientific Nicolet 6700 spectrometer, and the spectra were obtained in the scanning range of 400–4,000 cm−1. Thermogravimetric analysis (TGA) was measured using TGA 5500 thermo gravimetric analyzer. The powder samples were heated from 30 °C to 800 °C at a heating rate of 10 °C/min in N2 atmosphere. The specific surface area and pore size distribution were measured in N2 atmosphere using the Mike 2460 automatic specific surface and porosity analyzer, where the degassing time was 8 h and the degassing temperature was 200 °C. Surface properties were analyzed using the Thermo Scientific K-Alpha X-ray photoelectron spectrometer.

Adsorption experiments

Batch adsorption experiments were performed in a thermostatic shaker at 200 rpm with the temperature set to 25 °C. Before adding the adsorbent, a set of 100 mg/L DR-31 dye solutions (pH 3–12) by 0.1 M HCl and NaOH solutions were prepared to explore the effect of pH on adsorption. When studying the effect of initial concentration on adsorption, the concentration range of DR-31 was 50, 100, 150, 200, 250, 300 (mg/L), adding 10 mg Cu(PABA) sample to 50 ml solution. The removal rate(η) and adsorption capacity (qe) were calculated by formulas (1) and (2), respectively.
formula
(1)
formula
(2)
where C0 (mg/L) and Ce(mg/L) represent the initial concentration and equilibrium concentration of DR-31 solution, respectively; V(L) is the volume of the solution; m(g) Represents the quality of input Cu(PABA).

Kinetics were performed using an initial DR-31 concentration of 150 mg/L at a pH of 11. The isotherms were performed at three temperatures (25 °C, 35 °C, 45 °C) to observe the effect of temperature on adsorption. All absorbances were measured using a UV-2550 UV-Vis spectrometer. The maximum absorption wavelength of DR-31 is 525 nm, and the concentration of DR-31 was calculated by the change of absorbance at this wavelength.

RSM optimization

In this study, the combined interference of various parameters (pH, DR-31 dye concentration and temperature) on adsorption was evaluated using the RSM approach. BOX-Behnken design (BBD) was used to determine the optimum process parameters. The design model of three levels and three factors was adopted. Three codes of −1, 0 and +1 represented three levels (high, medium and low). A, B and C corresponded to three factors of pH, DR-31 dye concentration and temperature, respectively. Table 1 shows the experimental design levels for the DR-31 dye adsorption factor on Cu(PABA).

Table 1

Experimental design levels for the DR-31 dye adsorption factor on Cu(PABA)

FactorsUnitCodeLevels
−10+1
pH — 10 11 12 
Initial concentration mg/L 150 200 250 
Temperature 25°C 35°C 45°C 
FactorsUnitCodeLevels
−10+1
pH — 10 11 12 
Initial concentration mg/L 150 200 250 
Temperature 25°C 35°C 45°C 
Based on the interaction between parameters, the mathematical expression of adsorption can be listed
formula
(3)
where Y is the dependent variable representing the adsorption capacity of DR-31; Xi and Xj are independent variables; β0, βi, βii and βij are migration coefficient, linear coefficient, second-order coefficient and interaction coefficient, respectively.

Characterization of Cu(PABA)

SEM and TEM analysis

SEM is a means of directly observing the morphology of materials, and the SEM image of Cu(PABA) is shown in Figure 1. As shown in Figure 1(a), Cu(PABA) presented as a spherical cluster as a whole, which is the same as the morphology reported by Yuan et al. (Yuan et al. 2021), but the surface thorn-like protrusions were obvious, which may be caused by different solvents. The thorn-like protrusions are more conducive to the increase of the surface area. In addition, there are many loose crystals that do not form spherical shapes next to the clusters, which may be due to the high rate of magnetic stirring during the synthesis process, which causes the crystals to be broken up after clustering, and their thorn-like characteristics are still obvious. Figure 1(b) shows the appearance of a single ‘thorn’ by transmission electron microscopy.

Figure 1

The SEM (a) and TEM (b) images of Cu(PABA).

Figure 1

The SEM (a) and TEM (b) images of Cu(PABA).

Close modal

FT-IR analysis

The functional groups of Cu(PABA) were verified by FT-IR. The spectra are shown in Figure 2, and the peaks at 3,425 cm−1, 3,253 cm−1 and 3,139 cm−1 can be attributed to the stretching of -OH,-NH and C-H (Jiang et al. 2022). The peaks at 1,610 cm−1 and 1,392 cm−1 may be caused by the vibration of carboxyl or benzene ring skeleton (Wang et al. 2019). The peak at 1,124 cm−1 is the in-plane bending vibration of C-H; the spectra at 825−638 cm−1 represent the out-of-plane bending vibration of C-H.

Figure 2

FTIR spectrum of Cu(PABA).

Figure 2

FTIR spectrum of Cu(PABA).

Close modal

TGA analysis

Figure 3 shows the TGA results of Cu(PABA). The weight loss process can be divided into two stages. The first stage occurs between 30 °C and 230 °C, and the weight loss is about 7.5%, which is the loss process of non-coordinated water molecules and coordinated water molecules. 230–600 °C is the second stage of weight loss, the structure of Cu(PABA) is gradually destroyed and the skeleton collapses, and the mass loss is close to 70%. After 600 °C, there is no significant change in weight, which is a residue that exists stably in the form of CuO (Ramohlola et al. 2017). TGA shows that Cu(PABA) is stable when the temperature is lower than 230 °C.

Figure 3

TGA and DTG curve.

Figure 3

TGA and DTG curve.

Close modal

BET analysis

The specific surface area and pore size distribution are important factors affecting the adsorption capacity of the adsorbent. Therefore, We used BET characterization to measure the specific surface area and pore size distribution of Cu(PABA). The N2 adsorption-desorption isotherms of Cu(PABA) are shown in Figure 4. We can clearly see that the isotherm has an obvious hysteresis loop, which indicates that Cu(PABA) may have a mesoporous structure. The Characterization results show that the BET surface area of Cu(PABA) was obtained to 84 m2/g. The Barrett-Joyner-Halenda (BJH) method was used to calculate the aperture, and the average aperture was obtained to 14.6 nm. The cumulative pore size adsorption and desorption volume were 0.076 m3/g and 0.052 m3/g, respectively. The inset in Figure 4. shows the pore distribution, and the pore size of Cu(PABA) mainly ranges from 1 to 40 nm.

Figure 4

N2 adsorption−desorption curves and pore width distributions (inset) of Cu(PABA).

Figure 4

N2 adsorption−desorption curves and pore width distributions (inset) of Cu(PABA).

Close modal

Effect of solution pH

pH is a non-negligible influencing factor, which can affect the surface charge of Cu(PABA) adsorbent and the ionization degree of DR-31 dye. We experimented the adsorption under the conditions of pH 3∼12 at room temperature to analyze the effect of pH on the adsorption of DR-31 dye. Figure 5(a) shows the adsorption capacity of Cu(PABA) DR-31 under different pH conditions. We can see that the adsorption capacity is the lowest when pH=3. This may be attributed to the destruction of the structure of Cu(PABA) in a peracid environment. When the pH increased from 4 to 11, the adsorption capacity increased gradually with the increase of pH value. These trends can be explained by the Zeta potential of the material. As shown in Figure 5(b), the surface charge of Cu(PABA) maintains positive charge at pH lower than 11, and the Zeta potential approaches zero at pH=11, which is consistent with the experimental results of maximum adsorption capacity at pH=11. At this time, the adsorption capacity of Cu(PABA) for DR-31 was 237.7 mg/g, and the removal rate reached 95.6%. When pH>11, the adsorption capacity of Cu(PABA) began to decrease, which was due to the increase of the ionization degree of DR-31 at higher pH, the Zeta potential became negative charge and the electrostatic repulsion increased.

Figure 5

(a) Effect of pH on the adsorption of Cu(PABA) toward DR-31; (b) The zeta potential of Cu(PABA) at various pHs.

Figure 5

(a) Effect of pH on the adsorption of Cu(PABA) toward DR-31; (b) The zeta potential of Cu(PABA) at various pHs.

Close modal

Effect of initial dye concentration

Figure 6(a) and 6(b) show the relationship between the initial concentration of DR-31 dye and the adsorption capacity and removal rate, respectively. The relationship between initial concentration and dye removal has been widely studied. Generally speaking, when the concentration is low, there are more adsorption sites and it is easier to adsorb. Thus there is a higher removal rate. As the initial concentration increases, it takes more time to reach equilibrium, while the adsorption sites are occupied and the adsorption rate decreases. Figure 6(b) shows that with the increase of the initial concentration of DR-31 dye, the removal rate shows a decreasing trend. During the process of increasing the initial concentration from 50 mg/L to 300 mg/L, the removal rate decreases from 95% to 77%, which can be attributed to the saturation of adsorbent adsorption sites. Figure 6(a) shows that with the increasing initial concentration, the adsorption capacity of the adsorbent at equilibrium also increases, which may be due to the increasing driving force due to the increasing concentration (Habibi et al. 2022).

Figure 6

(a) Effect of initial DR-31 concentration on Cu(PABA) adsorption capacity. (b) Variation of removal rate with increasing initial concentration.

Figure 6

(a) Effect of initial DR-31 concentration on Cu(PABA) adsorption capacity. (b) Variation of removal rate with increasing initial concentration.

Close modal

Adsorption kinetics

Adsorption kinetics is an important experiment to explore the adsorption process, which can explain the adsorption mechanism and adsorption type. The kinetics were investigated using pseudo-first-order, pseudo-second-order and intraparticle diffusion models. They are described by Equations (4)–(6) respectively (Makrygianni et al. 2019; Saxena et al. 2020; Yang et al. 2021).

Pseudo-first-order kinetic model:
formula
(4)
Pseudo-second-order kinetic model:
formula
(5)
Intraparticle diffusion equations:
formula
(6)

In the above formula, qt (mg/g) is the adsorption capacity at time t, and qe (mg/g) represents the adsorption capacity at equilibrium; k (min−1) and kp (mg/g min0.5) represent pseudo-first-order rate constants and rate constants of internal diffusion, respectively; v0 (mmol/(g·min) represents the initial adsorption rate for pseudo-second-order kinetics; C is a constant representing the thickness of the boundary layer in the intraparticle diffusion model.

Figure 7 and Table 2 show the simulation results and related parameter values of the kinetics, respectively. By comparing the values of the correlation coefficients in the table, it can be found that the R2 (0.988) of the pseudo-first-order kinetic model is very close to the R2 (0.980) of the pseudo-second-order kinetic model. Furthermore, the qe (816.9 mg/g) calculated by the pseudo-first-order kinetic model is closer to the actual qe value (744 mg/g). Therefore, it can be concluded that the pseudo-first-order model is more suitable to describe the adsorption of DR-31 by Cu(PABA).

Table 2

Kinetic model parameters for the adsorption of DR-31 dye on Cu(PABA)

ModelParametersValue of parameters
Pseudo-first-order kinetic  (min−10.0056 
qe (mg g−1816.9 
R2 0.988 
Pseudo-second-order kinetic v0 (mmol/(g min−15.139 
qe (mg g−11,117.8 
R2 0.980 
Intraparticle diffusion  (mg g−1 min−0.529.881 
R2 0.987 
  (mg g−1 min−0.565.083 
 R2 0.994 
  (mg g −1 min−0.50.695 
 R2 0.531 
ModelParametersValue of parameters
Pseudo-first-order kinetic  (min−10.0056 
qe (mg g−1816.9 
R2 0.988 
Pseudo-second-order kinetic v0 (mmol/(g min−15.139 
qe (mg g−11,117.8 
R2 0.980 
Intraparticle diffusion  (mg g−1 min−0.529.881 
R2 0.987 
  (mg g−1 min−0.565.083 
 R2 0.994 
  (mg g −1 min−0.50.695 
 R2 0.531 
Figure 7

The kinetic model of DR-31 dye adsorbed by Cu(PABA). (a) Pseudo-first-order; (b) Pseudo-second-order; (c) intraparticle diffusion.

Figure 7

The kinetic model of DR-31 dye adsorbed by Cu(PABA). (a) Pseudo-first-order; (b) Pseudo-second-order; (c) intraparticle diffusion.

Close modal

In general, the adsorption process can include three steps: (1) the process of diffusion of the adsorbate from the bulk solution to the outer surface of the adsorbent, called out-diffusion; (2) The process in which the adsorbate enters the adsorbent micropores from the outer surface of the adsorbent and diffuses to the inner surface is called intraparticle diffusion; (3) The adsorbate is adsorbed by the adsorbent on the inner surface of the adsorbent solid, which is called the surface adsorption process. The intraparticle diffusion model was used to analyze the rate-limiting step of adsorption. If the graph shows a straight line through the origin, then the adsorption process is controlled only by intraparticle diffusion. If the graph does not show the origin or has multiple lines, it means that the adsorption process is not only controlled by intraparticle diffusion, but also affected by membrane diffusion and other diffusion mechanisms (Lin & Chang 2015; Zheng et al. 2021). It can be seen from Figure 7(c) that the adsorption process is divided into three stages and does not cross the origin, which indicates that the adsorption mechanism is affected by various diffusion mechanisms such as internal diffusion, membrane diffusion and surface adsorption.

Adsorption isotherms

In this study, two isotherm models of Langmuir (Xia et al. 2021) and Freundlich (Gao et al. 2018) were used for the analysis of the adsorption isotherm data.

Langmuir isotherm:
formula
(7)
Freundlich isotherm:
formula
(8)

Above, Ce (mg/L) represents the equilibrium concentration of DR-31; qe (mg/g) represents the adsorption capacity at equilibrium; qm (mg/g) represents the maximum adsorption capacity; b (L/mg) and K (mmol1−1/n/g1−1/n) represent the Langmuir constant and Freundlich constant, respectively; n represents the heterogeneous factor related to the surface heterogeneity of adsorbent.

The Langmuir adsorption isotherm assumes that the adsorbent is a homogeneous surface, single molecular layer, and the adsorption site is limited, so the adsorption will reach saturation. The Freundlich model is an empirical model, assuming that the adsorption is on the heterogeneous solid surface, with multilayer adsorption. Figure 8(a) and 8(b) shows the Langmuir and Freundlich isotherms of Cu(PABA) at 298, 308 and 318 K, respectively. Table 3 lists the relevant parameters of each model and the parameters obtained at three temperatures. According to the correlation coefficient, the Langmuir model is more suitable for describing the adsorption of DR-31 dye on Cu(PABA). This indicates that the homogeneous monolayer adsorption assumed by the Langmuir model can better describe the adsorption of DR-31 dye on Cu(PABA).

Table 3

Adsorption isotherm parameters for DR-31 adsorption on Cu(PABA)

IsothermParametersValue of parameters
298 K308 K318 K
Langmuir qm(mg g−11,244.8 1,054.1 1,493.6 
b 0.490 0.249 0.050 
R2 0.960 0.926 0.980 
Freundlich  460.891 295.099 145.505 
n 3.041 2.917 1.892 
R2 0.882 0.992 0.980 
IsothermParametersValue of parameters
298 K308 K318 K
Langmuir qm(mg g−11,244.8 1,054.1 1,493.6 
b 0.490 0.249 0.050 
R2 0.960 0.926 0.980 
Freundlich  460.891 295.099 145.505 
n 3.041 2.917 1.892 
R2 0.882 0.992 0.980 
Figure 8

Adsorption isotherms of DR-31 on Cu(PABA) at three temperatures (298 K, 308 K, 318 K): (a) Langmuir; (b) Freundlich.

Figure 8

Adsorption isotherms of DR-31 on Cu(PABA) at three temperatures (298 K, 308 K, 318 K): (a) Langmuir; (b) Freundlich.

Close modal

Table 4 lists the maximum adsorption capacity of some reported dye adsorbents. It can be found that the maximum adsorption capacity of Cu(PABA) on DR-31 is significantly higher than that of many reported dye adsorbents. This indicates that Cu (PABA) is a promising adsorbent for DR-31 dye.

Figure 9

XPS spectra of Cu(PABA) before and after adsorption of DR-31. (a) full view; (b) C1 s; (c) N 1 s; (d) O1 s; (e) S2p; (f) Cu2p.

Figure 9

XPS spectra of Cu(PABA) before and after adsorption of DR-31. (a) full view; (b) C1 s; (c) N 1 s; (d) O1 s; (e) S2p; (f) Cu2p.

Close modal
Table 4

The reported adsorption capacity of DR-31 dye adsorbent

AdsorbentsTempenture(°C)Adsorption capacity (mg/g)References
Cu(PABA) 25 1,244.8 This study 
Biochar-Cp 25 136.7 Behl et al. (2019)  
Rice husk 30 129.8 Safa & Bhatti (2011)  
NiFe2O4/AC 25 299.7 Livani & Ghorbani (2018)  
Activated carbon 25 111.0 Mahmoodi et al. (2011)  
Surfactant-modified coconut coir pith 32 76.3 Sureshkumar & Namasivayam (2008)  
Core–shell magnetic adsorbent nanoparticle 25 323 Mahmoodi (2014)  
Garlic peel 55 38.0 Asfaram et al. (2014)  
Fe (III)/Cr (III) hydroxide 25 5.1 Namasivayam & Sumithra (2005)  
AdsorbentsTempenture(°C)Adsorption capacity (mg/g)References
Cu(PABA) 25 1,244.8 This study 
Biochar-Cp 25 136.7 Behl et al. (2019)  
Rice husk 30 129.8 Safa & Bhatti (2011)  
NiFe2O4/AC 25 299.7 Livani & Ghorbani (2018)  
Activated carbon 25 111.0 Mahmoodi et al. (2011)  
Surfactant-modified coconut coir pith 32 76.3 Sureshkumar & Namasivayam (2008)  
Core–shell magnetic adsorbent nanoparticle 25 323 Mahmoodi (2014)  
Garlic peel 55 38.0 Asfaram et al. (2014)  
Fe (III)/Cr (III) hydroxide 25 5.1 Namasivayam & Sumithra (2005)  

Adsorption mechanism

DR-31 is a direct dye soluble in water in an anionic state. We measured the zeta potential of Cu(PABA), which is greater than zero at pH values less than 11, electrostatically attracting the negatively charged DR-31 dye. Electrostatic interaction is one of the main mechanisms affecting the adsorption of dyes on MOFs (Li et al. 2018). BET characterization showed that Cu(PABA) has mesopores with an average pore size as high as 14.6 nm, which facilitates the entry of DR-31 molecules into the pores of Cu(PABA) and is an important factor leading to the high adsorption capacity. To further explore the mechanism of adsorption, we performed XSP scans of Cu(PABA) before and after adsorption. Broad-scan XPS spectra showed peaks of C1 s, N1 s, O1 s and Cu2p, which are the main constituent elements of Cu(PABA). As shown in Figure 9(c), a new peak appeared at 400.6 eV in the high-resolution spectrum of N1 s after adsorption. This could be NH3+, which illustrates the protonation process of the amino group, making the material positively charged at low pH, which favors electrostatic attraction (Valadi et al. 2022). A new peak appeared in the high-resolution spectrum of S2p, which was related to -SO3 of the direct dye, indicating that DR-31 was successfully adsorbed onto Cu(PABA). In addition, hydrogen bonding and π-π interactions have also been reported to be important mechanisms affecting the adsorption of dye molecules (Tchinsa et al. 2021).

RSM analysis

The ANOVA results for DR-31 dye adsorption by Cu(PABA) are shown in Table 5. Through Design Expert program, the experimental results were fitted to the following equation.
formula
(9)
where Y is defined as the removal rate of DR-31, A is pH, B is the initial concentration of DR-31dye (mg/g), and C is temperature (°C). In the variance analysis, the significance of model was determined using the P-values and F-values and the higher the value of F and the lower value of P, the more the significance of the model would be (Nguyen et al. 2022). Here, F value=137.8, p (<0.0001), indicating that the model is significant and can well explain the removal of DR-31. As can be seen in Table 5, there are A, B, A2 and C2 with p-value <0.05, which shows that these terms are important model terms. In addition, the value of the determination coefficient R2 is 0.9980, the adjusted R2 value is equal to 0.9954, and the predicted R2 value is equal to 0.9877. High R2 values indicate the reliability of the OR model in predicting response. High R2 values indicate the reliability of the OR model in predicting response. Figure 10(a)–10(c) shows the interaction of pH-initial concentration, pH-temperature and initial concentration-temperature in the selected variable range. It can be found that there is a weak interaction between the independent variable and its optimal response.
Table 5

ANOVA data for the DR-31 adsorption model

SourceSum of squaresdfMean squareF-valuep-value
Model 22,728.20 2,525.36 383.05 <0.0001 significant 
660.66 660.66 100.21 <0.0001  
8.20 8.20 1.24 0.3015  
146.21 146.21 22.18 0.0022  
AB 0.5625 0.5625 0.0853 0.7787  
AC 1.0000 1.0000 0.1517 0.7085  
BC 9.61 9.61 1.46 0.2665  
A2 21,611.15 21,611.15 3,278.00 <0.0001  
B2 2.02 2.02 0.3063 0.5972  
C2 37.08 37.08 5.62 0.0495  
Residual 46.15 6.59    
Lack of fit 14.42 4.81 0.6058 0.6453 not significant 
Pure error 31.73 7.93    
Cor total 22,774.35 16     
SourceSum of squaresdfMean squareF-valuep-value
Model 22,728.20 2,525.36 383.05 <0.0001 significant 
660.66 660.66 100.21 <0.0001  
8.20 8.20 1.24 0.3015  
146.21 146.21 22.18 0.0022  
AB 0.5625 0.5625 0.0853 0.7787  
AC 1.0000 1.0000 0.1517 0.7085  
BC 9.61 9.61 1.46 0.2665  
A2 21,611.15 21,611.15 3,278.00 <0.0001  
B2 2.02 2.02 0.3063 0.5972  
C2 37.08 37.08 5.62 0.0495  
Residual 46.15 6.59    
Lack of fit 14.42 4.81 0.6058 0.6453 not significant 
Pure error 31.73 7.93    
Cor total 22,774.35 16     
Figure 10

The 3D response surface plot for the interaction of the pH, initial DR-31 dye concentration and temperature.

Figure 10

The 3D response surface plot for the interaction of the pH, initial DR-31 dye concentration and temperature.

Close modal

Based on the fitted model,the optimal DR-31 adsorption conditions are: pH=10.9, Initial concentration=216.6 mg/g, Temperature=27 °C, and removal efficiency reached 99.4%. In order to verify the prediction results, we conducted experimental tests, and the results showed that the direct deviation between the prediction results and the experimental results was very small (<3%), which verified the validity of the model.

In this study, Cu(PABA) was synthesized by a solvothermal method and used to remove DR-31 dye from aqueous solution. The characterization results show that Cu(PABA) has mesoporous structure with high surface area (84 m2/g) and large average pore size (14.6 nm). Therefore, Cu(PABA) has strong adsorption capacity and can effectively remove DR-31 dye in water. According to the langmuir model, the maximum adsorption capacity reached 1,244.8 mg/g. Adsorption experiments showed that the adsorption of Cu(PABA) on DR-31 dye was more in line with langmuir isotherm model and quasi-first-order kinetic model. Based on the optimization results of response surface, the removal rate was 99.4% under the optimal conditions of pH(10.9), DR-31 dye concentration (216.6 mg/L) and temperature (27 °C). In summary, Cu(PABA) was a promising adsorbent for dyes. Further study on the adsorption mechanism and more experiments on different dyes are necessary.

This work was supported by Science and Technology Innovation Team Project of Hubei Provincial Department of Education (Grant No. T2020002), Wuhan Science and Technology Planning Project (Grant No. 2020020601012274), National Natural Science Foundation of China (Grant No. 41571306) and Hubei Technological Innovation Special Fund (Grant No. 2020ZYYD019). The authors would like to thank Jiang Lang from Shiyanjia Lab (www.shiyanjia.com) for the characterization support.

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

The authors declare there is no conflict.

Asfaram
A.
,
Fathi
M. R.
,
Khodadoust
S.
&
Naraki
M.
2014
Removal of direct Red 12B by garlic peel as a cheap adsorbent: kinetics, thermodynamic and equilibrium isotherms study of removal
.
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
127
,
415
421
.
Behl
K.
,
Sinha
S.
,
Sharma
M.
,
Singh
R.
,
Joshi
M.
,
Bhatnagar
A.
&
Nigam
S.
2019
One-time cultivation of Chlorella pyrenoidosa in aqueous dye solution supplemented with biochar for microalgal growth, dye decolorization and lipid production
.
Chemical Engineering Journal
364
,
552
561
.
Chowdhury
M. F.
,
Khandaker
S.
,
Sarker
F.
,
Islam
A.
,
Rahman
M. T.
&
Awual
M. R.
2020
Current treatment technologies and mechanisms for removal of indigo carmine dyes from wastewater: a review
.
Journal of Molecular Liquids
318
,
114061
.
Katheresan
V.
,
Kansedo
J.
&
Lau
S. Y.
2018
Efficiency of various recent wastewater dye removal methods: a review
.
Journal of Environmental Chemical Engineering
6
(
4
),
4676
4697
.
Nguyen
V.
,
Vo
T.
,
Nguyen
T.
,
Dat
N. D.
,
Huu
B. T.
,
Nguyen
X.
,
Tran
T.
,
Le
T.
,
Duong
T.
,
Bui
M.
,
Dong
C.
&
Bui
X.
2022
Adsorption of norfloxacin from aqueous solution on biochar derived from spent coffee ground: master variables and response surface method optimized adsorption process
.
Chemosphere (Oxford)
288
(
2
),
132577
.
Oladoye
P. O.
,
Adegboyega
S. A.
&
Giwa
A. A.
2021
Remediation potentials of composite metal-organic frameworks (MOFs) for dyes as water contaminants: a comprehensive review of recent literatures
.
Environmental Nanotechnology, Monitoring & Management
16
,
100568
.
Ramohlola
K. E.
,
Masikini
M.
,
Mdluli
S. B.
,
Monama
G. R.
,
Hato
M. J.
,
Molapo
K. M.
,
Iwuoha
E. I.
&
Modibane
K. D.
2017
Electrocatalytic hydrogen evolution reaction of metal organic frameworks decorated with poly (3-aminobenzoic acid)
.
Electrochimica Acta
246
,
1174
1182
.
Sadiq
A. C.
,
Olasupo
A.
,
Ngah
W. S. W.
,
Rahim
N. Y.
&
Suah
F. B. M.
2021
A decade development in the application of chitosan-based materials for dye adsorption: a short review
.
International Journal of Biological Macromolecules
191
,
1151
1163
.
Sureshkumar
M. V.
&
Namasivayam
C.
2008
Adsorption behavior of Direct Red 12B and Rhodamine B from water onto surfactant-modified coconut coir pith
.
Colloids and Surfaces A: Physicochemical and Engineering Aspects
317
(
1–3
),
277
283
.
Wang
L.
,
Zhi
W.
,
Wan
J.
,
Han
J.
,
Li
C.
&
Wang
Y.
2019
Recyclable β-Glucosidase by one-pot encapsulation with Cu-MOFs for enhanced hydrolysis of cellulose to glucose
.
ACS Sustainable Chemistry & Engineering
7
(
3
),
3339
3348
.
Yuan
Y.
,
Cai
W.
,
Xu
J.
,
Cheng
J.
&
Du
K.
2021
Recyclable laccase by coprecipitation with aciduric Cu-based MOFs for bisphenol A degradation in an aqueous environment
.
Colloids and Surfaces B: Biointerfaces
204
,
111792
.
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