
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

72885
10.1038/s41598-024-72885-z
Article
​Statistical-based optimization and mechanism assessments of Arsenic (III)​ adsorption by ZnO-Halloysite nanocomposite​
Khoddam Mohammad Ali 1
Norouzbeigi Reza norouzbeigi@iust.ac.ir

1
Velayi Elmira 2
Cavallaro Giuseppe 3
1 https://ror.org/01jw2p796 grid.411748.f 0000 0001 0387 0587 Nanomaterials and surface technology research Laboratory, School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, P.B. 16765–163, Narmak, Tehran, Iran
2 https://ror.org/05pg2cw06 grid.411468.e 0000 0004 0417 5692 Department of Chemical Engineering, Faculty of Engineering, Azarbaijan Shahid Madani University, P.O.Box: 537517–1379, Tabriz, Iran
3 https://ror.org/044k9ta02 grid.10776.37 0000 0004 1762 5517 Dipartimento di Fisica e Chimica, Università degli Studi di Palermo, Viale delle Scienze, pad. 17, Palermo, 90128 Italy
16 9 2024
16 9 2024
2024
14 2162913 6 2024
11 9 2024
© The Author(s) 2024
2024
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Arsenic contamination in aqueous media is a serious environmental problem, especially in developing countries. In this research, the Box-Behnken response surface methodology was used to optimize the most relevant variables affecting arsenic adsorption on the ZnO-halloysite surface, including temperature, adsorbent dosage, pH, contact time, and As (III) initial concentration. The regression analysis indicated that the experimental data were appropriately fitted to a quadratic model with the adjusted R-squared value (R2) of 0.982 for As(III) adsorption capacity and a linear model with R2 of 0.931 for As(III) removal. The p-values for both adsorption capacity and removal efficiency were below 0.05, with F-values of 116.91 and 115.58, respectively, supporting the model’s validity. The optimum conditions for maximum removal of As(III) were determined through numerical and graphical optimization using the desirability function. It was found that the optimum conditions for adsorption were pH = 7.99, contact time of 3.99 h, As(III) initial concentration of 49.96 mg/L, and adsorbent dosage of 0.135 g/40 ml. The accuracy of the optimization procedure was confirmed by a confirmatory experiment, which showed a maximum arsenic removal of 91.31% and an adsorption capacity of 12.63 mg/g under optimized conditions. Moreover, XPS analysis was performed at different pH levels to investigate the As (III) adsorption mechanism. The results demonstrated that As(III) adsorption occurs at acidic and neutral pH levels. On the other hand, when pH is increased to 8, As (III) oxidizes to As (V), and then adsorption occurs.

Keywords

ZnO-Halloysite surface
Box-Behnken design
Arsenic (III) removal
Adsorption mechanism
X-ray photoelectron spectroscopy (XPS) analysis
Numerical model
Subject terms

Environmental sciences
Chemistry
Nanoscience and technology
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Arsenic is widely distributed in groundwater despite its relative scarcity in the natural world, and most of their types are As (III) and As(V), with contrasting toxicity levels. As (III) has 60 times more toxicity than As(V). There are several causes, including mineral dissolution, geochemical reactions, and metal ores and wood preservatives that leach arsenic compounds1–3. The development of biocompatible and cost-effective technologies for removing arsenic is crucial. Different technologies such as adsorption, electrocoagulation, precipitation, filtration, reverse osmosis, ion exchange, membrane, and biological methods have been used to remove arsenic-contaminated water4–7. Surface adsorption was the most common method since it was simple, energy-efficient, and yielded few byproducts8. The low cost and worldwide availability of clays make them a valuable material for contaminant adsorption. Clays are at least 20 times cheaper than activated carbon, and they are widely available worldwide. The halloysite nanotube (Hal) as clay nanomaterial could be used as an economical and biocompatible adsorbent with particularity like different electrical charges in outer and lumen surfaces9–11. Arsenic oxyanions (arsenites As (III)) are the major arsenic species in groundwater in pH ranges of 2 to 9 12,13. The lower pHPZC of halloysite, which is around 2.75 to 4.8 14–16, makes it less effective at adsorbing As (III) since it exhibits cation-active behavior above pH 5 17. It is possible to significantly improve the sorption affinity of halloysite to oxyanions, including arsenites and arsenates, by modifying it with metals or metal oxides13,18. The use of nanocomposites for the treatment of dyes, inorganic compounds, and heavy metals from water and wastewater has been extensively investigated19,20. The Halloysite nanocomposites were typically used in the water treatment to remove organic and inorganic contaminants. Zinc oxide has antimicrobial properties, therefore it is considered one of the most important composite components in most research21. ZnO-Halloysite nanocomposite has been used as an economical, biocompatible, and low-toxic adsorbent for the removal of arsenic (As(III))22,23. Several factors affect arsenic adsorption capacity, such as temperature, solution pH, adsorbent dosage, and initial arsenic concentration24,25. In order to optimize adsorption processes, it is necessary to establish a logical relationship between these factors and their complex interactions. Response surface methodology (RSM) is the most potent experiment design that combines mathematical and statistical techniques for optimizing processes. One of the most reliable RSM methods is the Box–Behnken statistical experiment design method (BBD)26–30. By using this approach, a higher-order response surface is created than with a factorial approach, which is specially designed to fit second-order models. As a result, BBD can reveal the main effects of independent factors, the interaction effects, and the quadratic effects on the response variable25,31–33.

Inorganic forms of As (As(III) and As(V)) are dependent on the environment’s pH and redox potential. In pH less than 6, As(V) exists as H3AsO4 and H2AsO4−, whereas in pH greater than 7, As(V) exists as HAsO4− 2 and AsO4− 3.The predominant species of As(III) in pH less than 7 is H3AsO3 as an uncharged molecule and in pH greater than seven as H3AsO3 and H2AsO3−  34,35. In addition, the electrical charge of the surface is affected by pH and has a significant impact on arsenic adsorption.

Although arsenic adsorption mechanisms have been investigated on various adsorbents, no studies have been conducted on the mechanism of arsenic adsorption on ZnO-halloysite nanocomposite. In our previous study, the fabrication and optimization of synthesis conditions of the ZnO-Halloysite nanocomposite were studied in more detail. The major objective of this study is to investigate the arsenic adsorption mechanism onto the ZnO-halloysite nanocomposite, which is considered a nontoxic adsorbent. The RSM method was employed to determine the most appropriate combination for maximum arsenic removal and adsorption capacity and to examine the influence of key operating parameters on its adsorption.

In this work, X-ray photoelectron spectroscopy (XPS) was used to investigate the As (III) removal mechanism within the ZnO-Halloysite nanocomposite. Additionally, BBD experimental design was used to analyze and optimize the effects of adsorption conditions such as solution pH, adsorbent dosage, initial arsenic concentration, and temperature on ZnO-Halloysite nanocomposite surface adsorption capacity and As (III) removal efficiency.

Materials and methods

Chemical and material

Sodium arsenate solution (NaAsO2 > 98%) as As (III) standard stock and Halloysite purchased from Sigma Aldrich. The zinc acetate dihydrate [Zn(O2CCH3)2·2H2O] was purchased from Across Company. Hexamethylenetetramine (HMTA, C6H12N4), zinc nitrate hexahydrate (Zn (NO3)2.6H2O), and ethanol (C2H6O) were purchased by Merck Company. Sodium hydroxide (NaOH, 99%) and hydrochloric acid (HCl, 37%) were supplied by GATRAN SHIMI TAJHIZ, an Iranian company.

Preparation of ZnO-Halloysite nanocomposite

ZnO-Halloysite was prepared using the chemical bath deposition (CBD) method, as detailed in our previous work22,36. Firstly, ZnO seeds were formed on halloysite nanotubes by dip-coating halloysites in an aqueous zinc acetate solution and thermal decomposition at 500 °C (ZnO Seed-Halloysite). In the second step, zinc nitrate hexahydrate and HMTA were mixed with a constant molar ratio of 1:0.5. Following this, ZnO Seed-Halloysite was added to the prepared solution and heated at 90 °C for two hours. The solution was filtered and washed at least three times with deionized water. After collecting the powder, it was dried for one hour at 150 °C.

Characterization

The chemical composition of samples was investigated by Fourier transform spectroscopy, FTIR (Bruker, vector 22, Germany), and X-ray photoelectron spectroscopy (XPS-PHI 5000 Versa Probe III). The XPS data was analyzed using Spectral Data Processor, SDP, v8.0 software [https://xpslibrary.com/spectra-data-processor-sdp/]. The arsenic concentration was evaluated by Atomic Adsorption Spectrometry (GBC Avanta-PM). Morphological features of the samples were evaluated by field emission scanning electron microscopy coupled with an energy dispersive spectrometer (FE-SEM, TESCAN, MIRA III) and scanning transmission electron microscopy (STEM, THERMOSCIENTIFIC, QUATTRO S SEM). The nanocomposites’ Zeta potential values were measured using a Zeta potential instrument (Japan, Horiba, SZ100).

Experimental design and statistical analysis

Design of Experiments (DOE) was used to study the effects of various factors on arsenic adsorption. It is well-known that Box-Behnken design (BBD) is one of the most useful response surface methodologies, as it utilizes both statistical and mathematical methods to optimize response. In this study, the BBD design was used to optimize the adsorption process variables to maximize arsenic adsorption capacity. The optimization process comprises three steps: identifying the problem, determining the factors and levels affecting the response, designing experiments statically, and analyzing the results37,38. Five factors were studied in this study, including the initial As (III) concentration, solution pH, adsorbent dosage, temperature, and contact time, in batch adsorption experiments conducted in a 100 mL glass container. Table 1 displays the levels of selected variables, which are coded as − 1, 0, and 1. Box-Behnken statistical design comprised 43 experimental points, as shown in Table 2. The center point of the design was repeated three times, and all the experiments were repeated twice. A quadratic polynomial model can approximate the relationship between independent variables and responses, as shown in Eq. (1) 32,37,39.

1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:y={b}_{0}+\sum\:_{i=1}^{n}{b}_{i}{x}_{i}+\sum\:_{i=1}^{n}{b}_{ii}{x}_{i}^{2}+{\sum\:}_{i=1}^{n-1}{\sum\:}_{j=i+1}^{n}{b}_{ij}{x}_{i}{x}_{j}+\epsilon\:$$\end{document}

where, y is the predicted response (adsorption capacity of As(III)); b0, bi, bii and bij are the coefficients derived from polynomial regression. Additionally, xi and xj are independent variables. Lastly, ε represents the random error. Model validation was evaluated using analysis of variance (ANOVA) and lack of fit. In addition to determining the suitability and significance of a model, ANOVA can also confirm its compatibility. All the points are 3 times is repeated and average of all data is indicated. The data was analyzed using Design Expert software 13.05.0 [https://www.statease.com/trial/].

Table 1 Adsorption factors, their actual and coded levels for Box-Behnken design (BBD).

Variable	Unit	Coded value and limits	
−1	0	+ 1	
Initial concentration of As(III)	mg/l	10	30	50	
Adsorbent dosage (ZnO-Halloysite)	g/40 ml	0.05	0.1	0.15	
Temperature	°C	15	25	35	
pH	–	4	6	8	
Contact time	h	1	2.5	4	

Table 2 Box–Behnken design matrix and responses.

Run	Initial concentration (mg/l) - A	Adsorbent dosage (g/40 ml) - B	pH - C	Contact time (hour) - D	Temperature - E	Arsenic removal
(%)	Adsorption capacity (mg/g)	
Actual	Predicted	%
Error	Actual	Predicted	%
Error	
1	50	0.1	4	2.5	25	58 ± 0.9	59.8	3.10	11.5 ± 0.1	12.02	4.52	
2	30	0.1	8	2.5	35	79 ± 0.8	79.55	0.70	9.48 ± 0.03	9.64	1.69	
3	30	0.15	6	2.5	35	83 ± 0.5	82.05	−1.14	6.6 ± 0. 2	6.64	0.61	
4	50	0.1	6	4	25	77 ± 0.1	75.87	−1.47	15.3 ± 0.11	15.35	0.33	
5	50	0.15	6	2.5	25	75 ± 0.9	78.3	4.40	10 ± 0.4	9.21	−7.90	
6	30	0.1	6	2.5	25	67 ± 0.3	71.55	6.79	8.04 ± 0.09	8.55	6.34	
7	10	0.15	6	2.5	25	85 ± 0.5	85.8	0.94	2.27 ± 0.03	3.27	44.05	
8	10	0.1	6	4	25	85 ± 1	83.37	−1.92	3.41 ± 0.06	2.88	−15.54	
9	30	0.05	8	2.5	25	75 ± 0.6	69.05	−7.93	18 ± 0.5	16.98	−5.67	
10	50	0.1	8	2.5	25	73 ± 0.4	75.8	3.84	14.5 ± 0.1	15.36	5.93	
11	30	0.1	6	4	15	81 ± 0.7	79.62	−1.70	9.72 ± 0.03	9.52	−2.06	
12	30	0.1	4	2.5	35	63 ± 0.3	63.55	0.87	7.56 ± 0.01	7.46	−1.32	
13	10	0.1	4	2.5	25	68 ± 0.8	67.3	−1.03	2.72 ± 0.05	2.11	−22.43	
14	30	0.1	6	2.5	25	68 ± 0.5	71.55	5.22	8.16 ± 0.03	8.55	4.78	
15	10	0.1	8	2.5	25	85 ± 1	83.3	−2.00	3.4 ± 0.11	3.13	−7.94	
16	50	0.1	6	2.5	15	70 ± 0.4	67.8	−3.14	14 ± 0.3	13.69	−2.21	
17	30	0.1	8	4	25	86 ± 0.7	87.62	1.88	10.32 ± 0.08	10.61	2.81	
18	30	0.1	4	2.5	15	65 ± 0.1	63.5	−2.31	7.8 ± 0.5	7.46	−4.36	
19	30	0.1	4	4	25	73 ± 0.3	71.62	−1.89	8.7 ± 0.3	8.43	−3.10	
20	30	0.15	6	4	25	93 ± 0.1	90.12	−3.10	7.4 ± 0.4	7.61	2.84	
21	30	0.05	6	4	25	65 ± 0.2	69.12	6.34	15.6 ± 0.1	15.76	1.03	
22	30	0.1	4	1	25	54 ± 0.3	55.49	2.76	6.48 ± 0.07	6.5	0.31	
23	30	0.05	6	2.5	35	63 ± 0.4	61.05	−3.10	15 ± 0.4	14.8	−1.33	
24	30	0.05	4	2.5	25	52 ± 0.5	53.05	2.02	12.5 ± 0.1	12.61	0.88	
25	30	0.15	8	2.5	25	89 ± 0.2	90.05	1.18	7.12 ± 0.02	6.64	−6.74	
26	50	0.05	6	2.5	25	58 ± 0.3	57.3	−1.21	23 ± 0.45	22.5	−2.17	
27	30	0.05	6	1	25	56 ± 0.3	52.99	−5.38	13.5 ± 0.3	13.83	2.44	
28	30	0.1	6	1	35	67 ± 0.6	63.49	−5.24	8 ± 0.5	7.59	−5.13	
29	30	0.15	6	2.5	15	88 ± 0.5	82.05	−6.76	7 ± 0.1	6.64	−5.14	
30	10	0.1	6	1	25	63 ± 0.5	67.24	6.73	2.5 ± 0.1	2.35	−6.00	
31	30	0.1	8	1	25	69 ± 0.2	71.49	3.61	8.28 ± 0.04	8.68	4.83	
32	30	0.15	4	2.5	25	75 ± 0.3	74.05	−1.27	6 ± 0.5	6.65	10.83	
33	30	0.1	8	2.5	15	80 ± 0.5	79.55	−0.56	9.6 ± 0.5	9.46	−1.46	
34	50	0.1	6	1	25	58 ± 0.3	59.74	3.00	11.6 ± 0.3	12.03	3.71	
35	30	0.1	6	2.5	25	71 ± 0.5	71.55	0.77	8.55 ± 0.08	8.55	0.00	
36	30	0.1	6	4	35	75 ± 0.5	79.62	6.16	9 ± 0.1	9.52	5.78	
37	30	0.1	6	1	15	64 ± 0.3	63.49	−0.80	7.68 ± 0.2	7.59	−1.17	
38	10	0.1	6	2.5	15	75 ± 0.6	75.3	0.40	3 ± 0.4	2.62	−12.67	
39	30	0.05	6	2.5	15	63 ± 0.5	61.05	−3.10	15 ± 0.5	14.8	−1.33	
40	50	0.1	6	2.5	35	70 ± 0.5	67.8	−3.14	14 ± 0.5	13.69	−2.21	
41	30	0.15	6	1	25	75 ± 0.2	73.99	−1.35	6 ± 0.6	5.68	−5.33	
42	10	0.05	6	2.5	25	63 ± 0.5	64.8	2.86	5 ± 0.2	6.29	25.80	
43	10	0.1	6	2.5	35	75 ± 0.1	75.3	0.40	3 ± 0.5	2.62	−12.67	

Arsenic adsorption test

Batch adsorption experiments were performed according to our previous work22. Various amounts of adsorbent were in contact with As (III) solution and the solution was placed on a shaker. The shaker was operated at various temperatures (15–35 ºC) at 120 rpm. The adsorbents were separated from the solution using filter paper after exposing them to As (III) for a determined period of time (1–4 h). As(III) residual concentration in the solution was measured using an atomic adsorption spectrometer. The equilibrium adsorption capacity and removal efficiency were determined by the following equations:

2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{q}_{e}=\frac{\left({C}_{0}-{C}_{e}\right)\times\:V}{m}\:\:$$\end{document}

3 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:Removal\text{\%}=\frac{\left({C}_{0}-{C}_{e}\right)}{{C}_{0}}\times\:100$$\end{document}

Co and Ce are initial and equilibrium concentrations of As(III) (mg/L), respectively. V is solution volume (L), m is the adsorbent mass (g) and qe is equilibrium adsorption capacity (mg/g).

Result and discussions

Optimization of the as (III) removal using BBD

The BBD approach was applied to determine the optimum conditions for As(III) adsorption on ZnO-Halloysite nanocomposite surface. The As(III) adsorption capacity and removal efficiency as target responses (Table 2) depend on the individual or combination of selected variables. The empirical relationship between As(III) adsorption capacity and selected variables is shown in Eq. (4). Moreover, Eq. (5) provides a model for the removal efficiency of As(III). Accordingly, the experimental data on adsorption capacity was fitted on a quadratic model, while the data on arsenic removal was fitted on a linear model.4 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} {\text{y}} = & {\text{8}}.{\text{25}} + {\text{5}}.{\text{54A}} - {\text{4}}.0{\text{8B}} + {\text{1}}.0{\text{9}}~{\text{C}} + 0.{\text{96D}} - {\text{ }}0.0{\text{7E}} - {\text{2}}.{\text{57AB}} + 0.{\text{58AC}} + 0.{\text{69AD}} - {\text{1}}.{\text{1}}0{\text{BC}} \\ & - 0.{\text{175}}0{\text{BD}} - 0.{\text{1BE}} - 0.0{\text{45CD}} + 0.0{\text{3CE}} - 0.{\text{26DE}} - 0.{\text{26A}}^{{\text{2}}} + {\text{2}}.{\text{3}}0{\text{B}}^{{\text{2}}} + 0.{\text{13C}}^{{\text{2}}} + 0.0{\text{9D}}^{{\text{2}}} + 0.{\text{33E}}^{{\text{2}}} \\ \end{aligned}$$\end{document}

5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{y }} = {\text{ 71}}.{\text{56}}{-}{\text{3}}.{\text{75A}} + {\text{1}}0.{\text{5}}0{\text{B}} + {\text{8C}} + {\text{8}}.0{\text{6D}}{-}0.0{\text{6875E}}$$\end{document}

There is a general rule that both positive and negative signs have meaning in models. The positive sign describes the interactive effects of the variables on the model; in contrast, the negative sign before the model parameters represents the opposite impact or antagonistic effects on the model. The positive correlation coefficients indicate that both variables affect the response in a synergetic manner. Conversely, negative coefficients indicate an antagonistic influence on the response39,40.

Verification of each model should be conducted using ANOVA results. The model with a P-value less than 0.05 and a high F-value is generally considered highly significant. It is necessary to conduct the Lack-of-Fit test to determine whether the model accurately matches the data. When the “Lack-of-Fit F-values” are not significant (> 0.05), it indicates that the model fits the data well. Therefore, the statistical significance of a response function must be checked with the F-test41,42. According to Tables 3, 4, the adsorption capacity and arsenic removal models are well-verified and predicted, and the experimental data are highly compatible.

Table 3 ANOVA for the quadratic model of adsorption capacity.

Source	Sum of Squares	df	Mean square	F-value	p-value	
Model	878.10	20	43.91	116.91	< 0.0001	
A-As(III) Concentration	490.64	1	490.64	1306.51	< 0.0001	
B-Ads. Dosage	265.80	1	265.80	707.79	< 0.0001	
C-pH	19.01	1	19.01	50.62	< 0.0001	
D-Contact time	14.85	1	14.85	39.53	< 0.0001	
E-Temperature	0.0841	1	0.0841	0.2239	0.6407	
AB	26.35	1	26.35	70.17	< 0.0001	
AC	1.35	1	1.35	3.58	0.0716	
AD	1.94	1	1.94	5.17	0.0330	
AE	0.0000	1	0.0000	0.0000	1.0000	
BC	4.80	1	4.80	12.77	0.0017	
BD	0.1225	1	0.1225	0.3262	0.5737	
BE	0.0400	1	0.0400	0.1065	0.7472	
CD	0.0081	1	0.0081	0.0216	0.8846	
CE	0.0036	1	0.0036	0.0096	0.9229	
DE	0.2704	1	0.2704	0.7200	0.4053	
A²	0.4511	1	0.4511	1.20	0.2849	
B²	33.86	1	33.86	90.17	< 0.0001	
C²	0.1103	1	0.1103	0.2937	0.5933	
D²	0.0545	1	0.0545	0.1451	0.7069	
E²	0.7309	1	0.7309	1.95	0.1769	
Residual	8.26	22	0.3755			
Lack of fit	8.12	20	0.4060	5.71	0.1594	
Pure error	0.1422	2	0.0711			
Cor total	886.37	42				

Table 4 ANOVA for the linear model for Arsenic removal.

Source	Sum of squares	df	Mean square	F-value	p-value		
Model	4060.63	5	812.13	115.58	< 0.0001	Significant	
A-As(III) Concentration	225.00	1	225.00	32.02	< 0.0001		
B-Ads. Dosage	1764.00	1	1764.00	251.05	< 0.0001		
C-pH	1024.00	1	1024.00	145.73	< 0.0001		
D-Contact time	1040.06	1	1040.06	148.02	< 0.0001		
E-Temperature	7.56	1	7.56	1.08	0.3063		
Residual	259.98	37	7.03				
Lack of fit	251.31	35	7.18	1.66	0.4475	Not significant	
Pure error	8.67	2	4.33				
Cor total	4320.60	42					

The value of adjusted R2 for the quadratic model in adsorption capacity is 0.982. This value agrees reasonably with the predicted R2 of 0.963 because the difference is less than 0.2. In addition, the adjusted coefficient can be used to predict As(III) adsorption most accurately. Similarly, the adjusted R2 for the arsenic removal model was 0.931, which aligns with the predicted R2 value (0.919). A comparison of predicted and actual data for adsorption capacity and arsenic removal of As(III) is shown in Fig. 1 to confirm the model’s validity. There should be a linear distribution of data points along the 45º line of the predicted versus actual graph. According to the results, the model can nearly approximate the original experimental data. Furthermore, adequate precision, as measured by dividing the effective signal by noise, can be achieved 47.62 and 37.49 for quadratic and linear model, respectively(> 4), indicating accurate result43,44.

Fig. 1 The predicted values versus experimental or actual values of As(III) adsorption capacity (a) and AS(III) removal (b) over ZnO-Halloysite.

Effect of operation variables on adsorption performance of ZnO-Halloysite nanocomposite

According to the ANOVA results for both responses, adsorption capacity and removal efficiency of As (III) presented in Tables 3, 4, the corresponding models are highly significant with P-values less than 0.01. For both models, the terms of arsenic concentration (A), adsorbent dosage (B), solution pH (C), and contact time (D) are highly significant, but temperature (E) is insignificant with a P-value greater than 0.1 45,46. Consequently, the insignificant model term was eliminated using the backward elimination method with an alpha value greater than 0.05 and based on the P-value as a criterion. This improved the model predictability for As (III) adsorption capacity. The lack of fit for the reduced quadratic model (Eq. (6)) was recalculated, resulting in a non-significant value of 0.2014. The ANOVA result is mentioned in Table 5. Moreover, after using the backward elimination method, adequate precision is reached to 73.77.

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Table 5 ANOVA for the reduced quadratic model for adsorption capacity.

Source	Sum of squares	df	Mean square	F-value	p-value	
Model	876.81	10	87.68	293.59	< 0.0001	
A-As (III) Concentration	490.64	1	490.64	1642.87	< 0.0001	
B-Ads. Dosage	265.80	1	265.80	890.01	< 0.0001	
C-pH	19.01	1	19.01	63.65	< 0.0001	
D-Contact time	14.85	1	14.85	49.71	< 0.0001	
AB	26.35	1	26.35	88.24	< 0.0001	
AC	1.35	1	1.35	4.51	0.0416	
AD	1.94	1	1.94	6.51	0.0157	
BC	4.80	1	4.80	16.06	0.0003	
A²	1.55	1	1.55	5.21	0.0293	
B²	45.29	1	45.29	151.67	< 0.0001	
Residual	9.56	32	0.2986			
Lack of fit	9.41	30	0.3138	4.41	0.2014	
Pure error	0.1422	2	0.0711			
Cor total	886.37	42				

Moreover, significant model in linear for Arsenic removal as response after model modification by backward elimination process based on P-value as a criterion is shown in (Eq. (7)). The ANOVA result is mentioned in Table 6. Moreover, after using the backward elimination method, adequate precision is reach to 41.03 and predicted R2 is reach to 0.92. 7 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{y }} = {\text{ 71}}.{\text{56 }} - {\text{ 3}}.{\text{75A }} + {\text{ 1}}0.{\text{5}}0{\text{B }} + {\text{ 8C }} + {\text{ 8}}.0{\text{6D}}$$\end{document}

Table 6 ANOVA for the reduced linear model for arsenic removal.

Source	Sum of squares	df	Mean square	F-value	p-value		
Model	4053.06	4	1013.27	143.92	< 0.0001	Significant	
A-As(III) Concentration	225.00	1	225.00	31.96	< 0.0001		
B-Ads. Dosage	1764.00	1	1764.00	250.55	< 0.0001		
C-pH	1024.00	1	1024.00	145.44	< 0.0001		
D-Contact time	1040.06	1	1040.06	147.72	< 0.0001		
Residual	267.54	38	7.04				
Lack of fit	258.88	36	7.19	1.66	0.4472	Not significant	
Pure error	8.67	2	4.33				
Cor total	4320.60	42					

Response surface studies and contour plots

Contour plots and 3D response surface graphs are shown in Fig. 2 to assess the influence of five variables on adsorption capacity. The graphs illustrate how two main variables affect the response at a constant level of the other variables. According to Eq. 6, which appears in Table 5, the effectiveness of the model’s factors determines the consequences of interactions in 3D graphs (AB > BC > AD > AC). As stated, AB and BC have antagonistic effects on adsorption capacity since they have negative signs in the model, while AD and AC have positive signs, resulting in synergetic effects47,48. According to Fig. 2, following the reduced quadratic model (Eq. (6)), the effect of temperature is eliminated by the high P-value. As shown in Fig. 2b, f, the adsorption capacity increases as the As (III) concentration (A) and contact time (D) simultaneously increase. The relation between initial concentration (A) and solution pH (C) at a constant medium level of other variables is presented in Fig. 2c, g. At low As (III) initial concentrations, pH variations from acidic to alkaline lead to a slight increase in adsorption capacity. However, at higher As(III) initial concentrations, pH effects become more pronounced. Based on the pHPZC value, the surface charge of As(III) species was negative above pH 7, and the predominant species of As(III) are H3AsO3 and H2AsO3−. The increase in adsorption capacity may be due to oxidation of As(III) and conversion to As(V), as well as surface complex formation between As(V) and ZnO-Halloysite surface functional groups22,49. As already mentioned, As(III) initial concentration (A) and adsorbent dosage (B) have antagonistic effects on adsorption capacity, i.e., an increase in As(III) initial concentration and a decline in adsorbent dosage leads to an increase in adsorption capacity (Fig. 2a, e). A similar relationship is observed between adsorbent dosage (B) and solution pH (C) (Fig. 2d, h). By increasing the pH of the solution and reducing the adsorbent dosage, the adsorption capacity is increased.

Fig. 2 Response surface plots (a-d) and contour line of interaction effects between each factor (e-h) on total arsenic adsorption capacity.

Optimization based on the desirability function

Using numerical optimization, a desirable value for each input factor and response can be selected. For a given set of conditions, possible input optimizations include range, maximum, minimum, target, none (for response), and set to determine the optimal output value. In this study, input variables were given specific ranges of values, while As (III) removal efficiency was aimed at a maximum, and As (III) adsorption capacity was considered a target. Minimum and maximum target values were set between 5 and 25 mg/g. Figure 3 illustrates ramp function graphs with blue and red dots for five key factors and their two responses. Factors and responses are represented by red and blue dots, respectively. The height of the dot corresponds to the level of desirability upon optimization. According to Fig. 3, the values of optimal conditions for independent variables were obtained as follows: pH = 7.99, contact time of 3.9 h, As (III) initial concentration of 49.99 mg/L, and adsorbent dosage of 0.135 g/40 ml. Using these conditions, the degree of desirability of the model was 0.955, the maximum achieved removal percentage of As (III) was 91.31%, and the adsorption capacity as the target was 12.63 mg/g. The confirmatory experiment demonstrated that AS(III) removal was 89.2% and As (III) adsorption capacity was 12.1 mg/g under optimal conditions, compared to the model’s removal of 91.31% and adsorption capacity of 12.63 mg/g. This indicates that the model is suitable and accurate.

Fig. 3 The desirable ramp for optimizing the adsorption capacity as the target.

Moreover, the graphical optimization as contour graphs of As (III) removal, adsorption capacity, and desirability are shown in Fig. 4. Gradient colours are used in the graph, with blue representing a low level of desirability and warm yellow representing a high level. In the contour plot, the flags show the optimal point for achieving 95.5% desirability, 91.31% As (III) removal efficiency, and 12.63 mg/g adsorption. As all the information has been summarized, the factors are considered AB.

Fig. 4 (a) Contour plot for desirability achievement based on the specified criteria, contour plots for (b) adsorption capacity, (c) arsenic removal, and (d) overlay plot to As (III) concentration and adsorbent dosage.

FTIR analysis and effect of pH

FTIR analysis was conducted to study the chemical stability of ZnO-Halloysite nanocomposites surface under various pH conditions. ZnO-Halloysite nanocomposite was exposed to acidic (pH = 2), slightly acidic and basic (pH = 4 and 8), neutral, and pH of 10 and 12 media for 24 h. Figure 5 shows the FTIR spectra of ZnO-Halloysite after exposure to different pH media. In the ZnO-Halloysite spectra, bands at 440, 470, 516, 640, and 782 cm− 1 can be attributed to Zn-O stretching vibrations. The band at 3428 cm corresponds to the stretching vibration of hydroxyl groups. There are peaks at 1106, 804, and 928 cm− 1, which correspond to asymmetric vibrations of Si-O-Si and bending vibrations of Si-OH. The bands observed at 1338 and 1341 cm− 1 are assigned to the bending and wagging vibrations of C-H groups, respectively. The band at 1635 cm− 1 is related to the asymmetric vibration of C = O. There is a peak at 1565 cm− 1 associated with the C-C stretching vibration.

Fig. 5 FTIR spectra curves between (a) 400 cm− 1 to 4000 cm− 1 and (b) 400 cm− 1 to 2000 cm− 1 of ZnO-Halloysite after exposure to different pH media.

Comparing ZnO-Halloysite spectra at different pH confirms that the intensity of peaks relating to the ZnO particles was reduced on ZnO-Halloysite nanocomposite (peaks 440, 473, 516, and 782 cm− 1) in highly acidic and alkaline solutions. These findings indicate the structure is inherently unstable in acidic and alkaline environments. The destruction of ZnO nanoparticles in acidic conditions is more significant than in alkaline environments. However, zinc oxide particles do not necessarily dissolve in acidic and alkaline environments due to their varying solubilities in different pH50–53. As a result, the nanocomposites are stable at neutral pH, but zinc ions are dissolved significantly in highly acidic (pH = 2) and highly alkaline (pH = 12) environments. Similar results have been reported by other researchers54,55. The other dramatic changes may result from the degradation of the silica phase in the nanocomposite (804, 914, 1106 cm− 1). Moreover, there is an absorption peak between 1710 and 1760 cm−11 for the C = O bond. Table 7 summarizes the characteristics of the stretching vibration bands associated with the bonds.

Table 7 Positions and assignments of the IR vibration bands.

Range or points	Wavenumber (cm−1)	Band assignments	
A1	2500–3000	O-H bonds	
A2	3300–3400	O-H stretch	
15	1709	C = O stretch	
14	1635	C = O stretch	
13	1565	C-C stretch	
12	1412	O-H bend	
10, 11, 8	1338–1350, 1020	C-H group	
9, 6, 7	1106, 928, 804	Si-O-Si and bending vibrations of Si-OH	
1, 2, 3, 4, 5	440, 470, 516, 640, 782	Zn-O stretch	

XPS analysis

Figure 6 shows the XPS survey spectra of Halloysite, ZnO seed-Halloysite, and ZnO-Halloysite surfcae. According to Fig. 6, carbon, oxygen, aluminum, and silicon are present in Halloysite. Furthermore, zinc is observed in ZnO seed-Halloysite and ZnO-Halloysite on nanotube and nanocomposite surfaces. Additionally, Table 8 shows a quantitative comparison of the XPS results of the above compounds.

Table 8 Surface atomic ratios of the samples from XPS analysis.

Sample description	Atomic ratio %	
Al 2p	Si 2p	C 1s	O 1s	Zn 2p1	Zn 2p3	
Halloysite pure	14.30	19.60	14.80	51.30			
ZnO seed-Halloysite	16.30	18.00	12.90	51.30	1.40		
ZnO-Halloysite	1.60	40.60	21.00	23.10	7.80	5.90	

Table 8 summarizes the atomic ratios and contents of the samples. According to Fig. 6c, ZnO-Halloysite shows two strong peaks at 1046.1 and 1023.1 eV related to the binding energies of Zn 2p1/2 and Zn 2p3/2. It was estimated that the energy difference between the two peaks was 23 eV, similar to that of typical ZnO nanoparticles, thus providing support for the + 2 valence state of Zn in ZnO56. Compared with ZnO seed-Halloysite and ZnO-Halloysite, ZnO particles are more abundant in ZnO-Halloysite surface. A higher amount of C1s is associated with the presence of HMTA. The O1s spectra of Halloysite and ZnO-Halloysite samples are shown in Fig. 7; the deconvoluted O1s spectra in Halloysite (Fig. 7a) showed three peaks centered 530.4, 531.6, 532.5 eV, which were assigned to surface hydroxyl groups, Al2O3 and SiO257,58. The deconvolution of O1s peaks was performed using the Gaussian function.

Fig. 6 XPS Survey spectra of Halloysite pure (a), ZnO Seed-Halloysite (b), ZnO-Halloysite (c), and overlay of all survey spectra (d).

Moreover, the deconvoluted O1s spectrum for ZnO-Halloysite (Fig. 7b) shows four peaks centered in 528.8, 531.3,533 and 534.2. The band at 528.8 is attributable to the O2− ions bond with Zn + 2 ions in the wurtzite structure of ZnO57,59–63. Furthermore, the bands at 531.3 might be related to OH groups derived from OH− radical adsorption or Zn(OH)2 on the surface64,65 or oxygen vacancies in ZnO66–68. Furthermore, binding energy centered at 531.3 is described as C = O in some literature69,70. The peak at a binding energy of 533 eV can be attributed to surface hydroxyl or Zn–O–Si groups in halloysite and to chemically attached oxygen species like H2O and O2 on ZnO surfaces64,71–73. Additionally, the band at 534.2 eV could probably be attributed to O-C = O surface bonding on ZnO or Zn2SiO4 surfaces73,74.

Fig. 7 O1s XPS spectrums for Halloysite pure (a) and ZnO-Halloysite (b).

Effect of pH on the as (III) adsorption

Based on our previous work, pHPZC for ZnO-Halloysite was reported to be nearly 6.9 as a function of zeta potential22. Accordingly, the surface charge of ZnO-Halloysite is negative above pHpzc. Since As (III) species have neural or negative surface charge up to pH 8, arsenic adsorption is expected to decline above pHpzc. However, the results, as mentioned above, showed that the adsorption capacity increased with an increase in pH from 6 to 8. In order to better understand the effect of solution pH on the adsorption of As(III) on ZnO-Halloysite composites, XPS analysis was performed. Numerous studies have reported the effectiveness of nanomaterials in removing arsenic ions from water. Reddy et al.75, reported that CuO nanoparticles can remove As(III) and As(V) under a wide pH range due to the high pHpzc (pHpzc=9.4(. Moreover, this study indicated that the As(III) adsorption rate is slower than that of As(V). Three characteristic peaks at 45.15, 45.28 and 45.2 eV were identified as As(V) in XPS analysis, which was approximately 1 eV more than the position associated with As(III)76. According to the research published by sofer et al., the bands at 41.5 and 42.2 eV were correlated with As3d5/2 and As3d3/2 while binding energies at 44.9 eV were related to As(III) as As(III) trioxide77. It has been reported that the binding energies of As3d in arsenic oxides are in the range of 44.3–44.5, and 45.2–45.6 eV78. Summary of some of the binding energies stated in the previous studies is presented in Table 9. Figure 8 illustrates high-resolution As3d XPS spectra after As (III) adsorption onto ZnO-Halloysite at different solution pH. Using Gaussian fitting analysis, the As3d spectrum of ZnO-Halloysite-As was deconvoluted into three characteristic peaks between 38 and 49 eV. The surface atomic content and ratios of arsenic species are presented in Table 10.

Table 9 As3d binding energy and arsenic species.

Adsorbent	Binding energy (eV)	Arsenic species	References	
nZVI	45.2

45

43.5

	As(V)

As(V)

As(III)

	79	
Ferromanganese slag	43.75

44.90

	As(III)

As(V)

	80	
(Co–Al–Fe) nano adsorbent	41.94

42.25

43.04

43.81

44.62

44.58

	As(III)

As(III)

As(III)

As(V)

As(V)

As(V)

	81	
MnO2@La(OH)3 Nanocomposite	45.11

45.13

45.1

44.1

	As(V)

As(V)

As(V)

As(III)

	82	

Based on the results, the adsorbed arsenic on ZnO-Halloysite nanocomposite surface is predominately present in As(III) at all pH levels. As (V) was also observed in the As3d spectra of all ZnO-Halloysite-As nanocomposite with atomic ratios of 27, 34.6, and 38.4% respectively as mentioned in Table 10, for pH ranges 4, 6, and 8. It can be concluded from this observation that As(III) was partially oxidized to As(V) during the adsorption reaction. The dominant species, H3AsO3 generally dissociates into H2AsO3−, HAsO32−, and AsO33−83. pH can affect the oxidation rate of the As(III) in these three forms49. According to the results, As(III) oxidized faster at pH 8 compared to pH 6 or 4.

Table 10 Binding energy of As3d after adsorption as (III) upon ZnO-Halloysite.

pH range	ZnO-Halloysite after As (III) adsorption	
4	6	8	
Binding energy	41.51	43.34	45.01	42.25	43.63	44.89	41.79	44.09	45.61	
Atomic ratio %	25.6	47.4	27	22.8	42.6	34.6	15.9	45.7	38.4	
Arsenic species	As(III)	As(III)	As(V)	As(III)	As(III)	As(V)	As(III)	As(III)	As(V)	

Moreover, as shown in Fig. 8d, intensity of binding energy in pH equal to 8 is more than 4 and 6, this point is confirmed the optimum operational condition which is derived from adsorption capacity model.

Fig. 8 As 3d spectrum in different pH: 4 (a), 6 (b), 8 (c) and Overlayer of As3d spectrum in different pH (d).

EDX analysis

To further investigate the chemical composition of ZnO-Halloysite surface after arsenic adsorption, the EDX analysis was performed. Figure 9 shows the EDX spectrum and elemental mapping of ZnO-Halloysite nanocomposite surface after As (III) adsorption at pH 8. The results showed that ZnO-Halloysite nanocomposite adsorbent adsorbed arsenic. The elemental mapping graphs clearly show that elemental arsenic is abundant and uniformly distributed in the adsorption products, demonstrating ZnO-Halloysite nanocomposites’ excellent adsorption abilities.

Fig. 9 Elemental mapping (a-f) and EDX spectrum (g-h) of ZnO-Halloysite nanocomposite after As(III) adsorption at pH = 8.

Arsenic removal mechanism

The adsorption of arsenic on ZnO-Halloysite nanocomposite includes a hard acid-hard base interaction as well as surface complexation between arsenic and ZnO-Halloysite nanocomposite. Hard acid and hard base reactions might occur in the pH range of 7 to 8 due to hydroxyl groups. According to the results, the maximum adsorption of arsenic was found in the 7.0–8.0 pH range. Arsenic (III) has a neutral form (H3AsO3) or negative surface charge in this pH range. The hydroxyl groups on the ZnO-Halloysite surface can deprotonate under these conditions and attach to arsenic (III). A similar mechanism is reported for arsenic removal by zinc peroxide functionalized synthetic graphite84.

It is possible for As(III) and ZnO-Halloysite to form complexes due to As(III) doping caused by native defects in ZnO nanoparticles, such as O vacancies and Zn interstitials. Generally, Arsenic in ZnO may occupy the Zn site (Aszn), O site (AsO), or the interstitial position (Asi)85. According to some studies, As(III) does not necessarily occupy the O site; however, Aszn-2Vzn and Aszn-3Vzn may form86,87.

According to the literature, binding energy of 47–48 eV is attributed to As in Aso (As occupying an O site), which can serve as a deep acceptor (Aso). Binding energy in the range of 41 to 42 may relate to AsZn, which acts as a donor bond. In addition, AsZn–2VZn is another possible acceptor defect related to arsenic occupying zinc sites inducing two vacancies. The AsZn–2VZn has a very similar binding energy to AsZn. In the AsZn– 2VZn complex, the As (III) atom donates all three electrons simultaneously to each of the two VZn that can accept two electrons. As3+ will sit in the geometry of a wurtzite structure in a highly electronegative environment with negative oxygen ions. The band at a binding energy of 42 eV is assigned to Asi, which acts amphoteric. As-O and As-Zn bindings should not appear if arsenic occupies the oxygen or zinc sites85,88–93. According to the XPS results, the As–O bonding is not visible, but the As–Zn bonding is visible, showing that the defect closely resembles AsO.

Conclusion

Box-Behnken Response Surface Design was used in this study to determine the optimal conditions for arsenic adsorption onto ZnO-Halloysite nanocomposite surface. According to the results, the adsorption conditions significantly affected the removal of arsenic. The interactive effect of five independent variables (initial arsenic concentration, pH, temperature, contact time, and adsorbent dose) on two responses (arsenic adsorption capacity and removal) was evaluated using response surface plots. A quadratic and linear mathematical model was developed based on statistical regression analysis of experimental data obtained from 43 batch runs for arsenic adsorption capacity and removal efficiency. At a 95% confidence level, the ANOVA results further established the significance (P < 0.05) of the quadratic model for arsenic adsorption capacity and the linear model for arsenic removal. By using the desirability function method, optimization of the adsorbent dosage (0.135 g), initial concentration (49.99 mg/L), temperature (25 °C), pH (3.99), and contact time (3.99 h) led to a maximum removal efficiency of 91.31% of arsenic with an adsorption capacity of 12.63 mg/g with 0.995 desirability. The arsenic adsorption mechanism onto ZnO-Halloysite was studied using FTIR and XPS analyses. Results indicated that maximum arsenic adsorption occurs between pH 7 and 8 due to As(III) adsorption during hard acid-hard base reactions or due to complexes formed between AS(III) and ZnO-Halloysite surface during AS(III) doping. Furthermore, arsenic adsorption in the form of As (VI) on ZnO-Halloysite was pronounced at high pH, which may be explained by the rapid oxidation of As(III) into As(VI) at high pHs.

Author contributions

M.A.K.: Writing—Original Draft, Methodology, Investigation, software, Formal analysis. R.N.: Supervision, Conceptualization, Resources. E.V.: Writing—Review & Editing, Supervision, Conceptualization, Methodology. G.C.: Supervision, software, Methodology.

Data availability

The data for this study will be available upon reasonable request by contacting the corresponding author (norouzbeigi@iust.ac.ir).

Declarations

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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