Chemical activation of phosphogypsum exhibits enhanced adsorption of malachite green from aqueous solution due to porosity refinement

doi:10.1007/s11705-024-2475-4

2024, Vol. 18

Issue (11) : 124 https://doi.org/10.1007/s11705-024-2475-4

Chemical activation of phosphogypsum exhibits enhanced adsorption of malachite green from aqueous solution due to porosity refinement

Anurag Panda¹, Anuradha Upadhyaya¹, Ramesh Kumar², Argha Acooli¹, Shirsendu Banerjee^1,³, Amrita Mishra³, Moonis Ali Khan⁴, Somnath Chowdhury⁵, Byong-Hun Jeon²(

), Sankha Chakrabortty^1,³(

), Suraj K. Tripathy^1,³(

)

¹. School of Chemical Technology, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India
². Department of Earth Resources & Environmental Engineering, Hanyang University, Seoul 04763, Republic of Korea
³. School of Biotechnology, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India
⁴. Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
⁵. Department of Chemical Engineering, National Institute of Technology, Durgapur 713209, West Bengal

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Abstract

Owing to its uncomplicated synthetic methodology and exorbitant market demand, malachite green is widely used in numerous industries, particularly as a fungicide in aquaculture. Considering its intrinsic toxicity and potential long-term health impacts, deployable and cost-effective strategies must be developed for eliminating water-soluble malachite green. In this study, chemically activated phosphogypsum, a byproduct of fertilizer production, was used to remove malachite green from an aqueous system. Due to its low cost and abundance, the use of phosphogypsum as a sorbent material may significantly reduce the cost of adsorption-based processes. Moreover, its structural durability allows efficient recycling without significant deformation during reactivation. However, untreated phosphogypsum exhibits minimal efficiency in adsorbing synthetic dyes due to its unfavorable surface chemistry. Our investigation revealed that Zn activation induced a noticeable increase in pore volume from 0.03 to 0.06 cm³·g^–1. A 60 mg·L^–1 sorbent dose, pH 7, 150 r·min^–1, and operational temperature of 30 °C produced 99% quantitative sorption efficiency. Response surface methodology and artificial neural network were used to optimize process parameters by validating experimental values. No detectable toxicity was observed in Escherichia coli when exposed to the treated water.

Keywords malachite green operating conditions optimization phosphogypsum sorption water treatment

Corresponding Author(s): Byong-Hun Jeon,Sankha Chakrabortty,Suraj K. Tripathy

About author:

Chunqi Yang contributed equally to this work.

Just Accepted Date: 21 May 2024 Issue Date: 31 July 2024

Cite this article:

Anurag Panda,Anuradha Upadhyaya,Ramesh Kumar, et al. Chemical activation of phosphogypsum exhibits enhanced adsorption of malachite green from aqueous solution due to porosity refinement[J]. Front. Chem. Sci. Eng., 2024, 18(11): 124.

URL:

https://academic.hep.com.cn/fcse/EN/10.1007/s11705-024-2475-4
https://academic.hep.com.cn/fcse/EN/Y2024/V18/I11/124

Tab.1 Coded data of the selected operating conditions used for the design of experiments

Std	Run	Factor A	Factor B	Factor C	Factor D	Factor E	Response
Std	Run	Adsorbent dose/(g·L^–1)	MG concentration/(mg·L^–1)	Stirring time/(r·min^–1)	Temperature/°C	Time/min	Observed adsorption efficiency/%	RSM predicted adsorption efficiency/%	ANN predicted adsorption efficiency/%
35	1	0.6	30	150	25	45	90	90.4	88.40
48	2	0.6	50	150	25	45	100	99.6	99.88
5	3	0.5	40	200	20	30	70	70.4	69.99
18	4	0.7	40	100	20	60	85	84.1	84.99
12	5	0.7	60	100	30	30	72	70.4	72.00
20	6	0.7	60	100	20	60	78	77.1	70.42
38	7	0.6	50	250	25	45	84	86.1	83.99
1	8	0.5	40	100	20	30	80	78.5	79.99
33	9	0.4	50	150	25	45	60	60.5	60.00
32	10	0.7	60	200	30	60	84	83.2	84.00
42	11	0.6	50	150	25	75	86	84.8	81.90
28	12	0.7	60	100	30	60	86	84.2	91.85
2	13	0.7	40	100	20	30	86	84.5	84.35
47	14	0.6	50	150	25	45	99.8	99.2	99.88
11	15	0.5	60	100	30	30	72	72.6	75.86
10	16	0.7	40	100	30	30	78	77.4	78.00
16	17	0.7	60	200	30	30	76	75.1	76.00
37	18	0.6	50	50	25	45	89	88	89.00
34	19	0.8	50	150	25	45	70	68.4	70.00
30	20	0.7	40	200	30	60	74	76.1	74.00
29	21	0.5	40	200	30	60	66	67.4	66.00
43	22	0.6	50	150	25	45	100	99.4	99.88
46	23	0.6	50	150	25	45	100	99.5	99.88
14	24	0.7	40	200	30	30	75	75.2	74.99
36	25	0.6	70	150	25	45	85	83.7	84.99
19	26	0.5	60	100	20	60	55	55.6	55.00
31	27	0.5	60	200	30	60	76	77.4	76.00
6	28	0.7	40	200	20	30	70	68.2	70.00
25	29	0.5	40	100	30	60	68	68.4	61.63
22	30	0.7	40	200	20	60	55	54.8	55.00
21	31	0.5	40	200	20	60	42	43.5	41.14
27	32	0.5	60	100	30	60	70	68.4	69.99
41	33	0.6	50	150	25	15	88	87.7	87.99
26	34	0.7	40	100	30	60	88	86.8	87.99
15	35	0.5	60	200	30	30	88	88.5	87.99
3	36	0.5	60	100	20	30	60	63.1	60.00
24	37	0.7	60	200	20	60	64	62.8	57.49
4	38	0.7	60	100	20	30	62	62.7	62.00
23	39	0.5	60	200	20	60	50	50.8	50.00
50	40	0.6	50	150	25	45	100	99.7	99.88
40	41	0.6	50	150	35	45	80	80.7	80.00
7	42	0.5	60	200	20	30	60	62.7	60.00
17	43	0.5	40	100	20	60	50	51.4	50.00
39	44	0.6	50	150	15	45	42	42.7	41.99
8	45	0.7	60	200	20	30	62	60.7	61.99
44	46	0.6	50	150	25	45	99.5	99.4	99.88
45	47	0.6	50	150	25	45	99.9	99.8	99.88
49	48	0.6	50	150	25	45	100	99.8	99.88
9	49	0.5	40	100	30	30	82	83.4	82.00
13	50	0.5	40	200	30	30	86	85.7	86.00

Tab.2 CCD-based data matrix corresponding to different operating conditions

Fig.1 (a) XRD pattern and (b) FTIR spectra of PG and PG-Zn.

Fig.2 (a–e) XPS of zinc-treated PG; (a) XPS plot for zinc-treated PG; (b) plot for Ca; (c) plot for Zn; (d) plot for Si; (e) plot for C.

Fig.3 Morphological images for PG and zinc-treated PG. (a, c) Field emission scanning electron microscopic image of PG; (b, d) field emission scanning electron microscopic image of zinc-treated PG (PG-Zn); (e) energy-dispersive X-ray analysis for PG; (f) energy-dispersive X-ray analysis for PG-Zn; (g) SAED pattern for PG-Zn; (h) d-spacing derived from HRTEM for PG-Zn; (i) HRTEM image of PG-Zn.

Fig.4 (a) N₂ adsorption and desorption curve of PG; (b) pore size distribution of PG; (c) N₂ adsorption and desorption curve of PG-Zn; (d) pore size distribution of PG-Zn.

Fig.5 (a) Zeta potential of PG; (b) PZC of PG; (c) zeta potential of PG-Zn; (d) PZC of PG-Zn.

Fig.6 Effect of different operational conditions on the adsorption efficiency. (a) Stirring speed vs. adsorption efficiency (other conditions: pH = 7, initial MG concentration = 50 ppm, sorbent dose = 0.6 g·L^–1, temperature = 30 °C); (b) sorbent dose vs. adsorption efficiency (other conditions: pH = 7, initial MG concentration = 50 ppm, stirring speed = 150 r·min^–1, temperature = 30 °C); (c) dye concentration vs. adsorption efficiency (other conditions: pH = 7, stirring speed = 150 r·min^–1, sorbent dose = 0.6 g·L^–1, temperature = 30 °C); (d) solution pH vs. adsorption efficiency (other conditions: initial MG concentration = 50 ppm, stirring speed = 150 r·min^–1, sorbent dose = 0.6 g·L^–1, temperature = 30 °C); (e) temperature vs. adsorption efficiency (other conditions: pH = 7, stirring speed = 150 r·min^–1, sorbent dose = 0.6 g·L^–1, initial MG concentration = 50 ppm).

Fig.7 Mutual interactions of the different operational parameters (extracted from RSM-CCD) on MG dye adsorption efficiency over PG-Zn; mutual interactions of (a) adsorbent dose and temperature; (b) adsorbent dose and stirring speed; (c) MG concentration and adsorbent dose; (d) time and stirring speed; (e) time and temperature; (f) time and adsorbent dose; (g) MG concentration and temperature; (h) MG concentration and temperature; (i) time and concentration; (j) temperature and stirring speed.

Tab.3 Summary of statistical results of RSM and ANN^a)

Fig.8 (a) Neural network of the operating parameters and (b) the regression curve with a final R² value of > 0.99.

Tab.4 Optimized operational conditions to achieve the maximum removal efficiency

Tab.5 Equilibrium parameters of different isotherm models used in this study

Fig.9 Adsorption isotherm models. (a) Langmuir; (b) Freundlich; (c) Temkin; (d) Dubinin-Radushkevich.

Tab.6 Kinetic and thermodynamic results of the present study

Fig.10 Kinetic graphs. (a) Pseudo first-order model; (b) pseudo second-order model; (c) intra-particle diffusion model; (d) thermodynamic graph.

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