The discharge of industrial wastewater, particularly from chemical and mining industries, poses significant threats to the environment, public health, and safety due to high concentrations of pollutants leading to serious illnesses and the loss of aquatic life. It is therefore essential and urgent to devise measures for mitigating these threats. To advance the understanding of graphene membranes for arsenic removal from wastewater, we investigated the arsenic adsorption mechanism and relative selectivity on graphene-based materials using computational approaches. Our study employed hybrid quantum mechanical calculations for energy and geometry optimization to explore arsenic adsorption on pristine graphene membrane surfaces in vacuum and aqueous environments. We assessed the effect of different adsorption sites on the surface, including top (T), bridge (B), and hollow (H) across both edge (E) and center (C) regions, to identify the optimal site. Our results identified edge sites as the most effective for adsorption, with strong adsorption energies in both vacuum (-1.98 eV) and aqueous environments (-1.97 eV), which are generally stronger than those for water adsorption (-0.25 to -0.26 eV) on the surface. Geometrical analyses confirmed the bridge edge sites as the most preferred adsorption configuration. Our findings advance computational methodologies for designing efficient adsorbents and offer valuable insights for developing graphene-based materials. By elucidating adsorption mechanisms and optimizing membrane properties, this study contributes to the novel design of adsorbents for arsenic removal, addressing critical challenges in environmental remediation.
Research Article
Computational Insights into Graphene-Based Materials for Arsenic Removal from Wastewater: A Hybrid Quantum Mechanical Study
https://doi.org/10.21203/rs.3.rs-4935799/v1
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Adsorption
Arsenic
Carbon
DFT
Graphene
Heavy Metals
PM3
Wastewater
The global population growth has led to a significant increase in the demand for freshwater, straining traditional sources like rivers, lakes, and groundwater. Industrial development has further exacerbated the issue by polluting these sources with heavy metals like lead, copper, chromium, and arsenic, posing environmental and health risks (Jaishankar et al., 2014; Mitra et al., 2022). Therefore, it is essential to treat industrial and municipal wastewater to protect our conventional water sources. The severity of clean water scarcity especially in the deserts and rural areas of some developing countries cannot be overemphasized.
Arsenic is a naturally occurring heavy metal found in the environment, ranking 20th in natural abundance (Tchounwou et al., 2012). Arsenic is considered one of the most harmful trace elements in the environment and it exists in both inorganic (iAs) and organic (oAs) forms, with its chemical form and concentration affecting its solubility, mobility, reactivity, bioavailability, and toxicity (Mlangeni et al., 2023). Increases in arsenic levels in the soil over time are primarily caused by human activities such as uncontrolled industrial processes, using arsenic in the production of tools, cosmetics, ornaments, pigments, coal-fired furnaces, glass, tanneries, medicine, mirror manufacturing, and its use in pesticides during the 20th century (Patel et al., 2023). In water, arsenic presence is associated with natural processes including soil erosion, leaching from rocks, chemical reactions, biological activity, volcanic emissions, and industrial activities (Nordstrom, 2002; Smedley & Kinniburgh, 2002).
Arsenic contamination in water is a widespread issue globally. Groundwater and drinking water in many countries (Shaji et al., 2021; Smedley, 1996), including India, Bangladesh, Malaysia, Mexico, Hungary, New Zealand, the USA, Spain, Japan, Canada, Taiwan, and Mainland China, have been impacted by arsenic pollution. Long-term exposure to arsenic in drinking water can increase the incidence of cancer of the skin, lung, liver, bladder, arsenicosis, dermal lesions, peripheral neuropathy, kidney, and prostate (Islam et al., 2023; Smith et al., 1992). Given the global extent of arsenic contamination, it is essential to research and develop methods to understand arsenic chemistry and remove it from water. Common methods for removing arsenic include coagulation, flocculation, adsorption, ion exchange, membrane filtration, bioremediation, solar oxidation, and electrochemical treatments. Adsorption is particularly effective and cost-efficient due to its simplicity (Gao et al., 2020; Guisela B et al., 2022; Koomson et al., 2023; Pincus et al., 2021; Wei et al., 2019; Xing et al., 2022; Younas et al., 2024). Several adsorbents, such as clay, polymers, biological materials, metal oxides, and composite materials, have shown potential for removing arsenic. Despite these advancements, there are still limitations to their use. As a result, there is a pressing need to create new and effective adsorbents that can efficiently remove arsenic from water.
The literature surveys have revealed that numerous researchers consistently strive to develop effective and efficient materials for eliminating hea(Gao et al., 2020; Pincus et al., 2021; Wei et al., 2019)vy metals, including arsenic pollutants, from wastewater often discharged by industrial production processes. Some of these works explored the use of metal-organic frameworks (Dechdacho et al., 2023; Du et al., 2024), biochar (Din et al., 2024; Mon et al., 2024; Wei et al., 2019), and other carbon-based materials (Egbosiuba et al., 2020; Gao et al., 2020; Karić et al., 2022; Pincus et al., 2021; Tolkou & Kyzas, 2023; Wierońska-Wiśniewska et al., 2024). Among the works is Mojiri et al.(2024) that used mineral or clay-based adsorbents like bentonites and zeolites to eliminate arsenic from polluted water, which was similar to the work of Egbosiuba et al. (2024), which used kaolin clay to eliminate arsenic from wastewater with an adsorption capability of 337.22 mg/g. Another is Hua (2018) that confirms the effectiveness of modified bentonite using manganese oxide and poly-dimethyl diallyl ammonium-chloride for eliminating arsenic from wastewater. A biobased adsorbent was also synthesized by Guisela et al. (2022) from eucalyptus bark (a cheaply available material) using an acid treatment, which shows an adsorption capacity of 0.944 mg/g in their studies. The bulk of the works in the literature have consistently focused more on the deployment of experimental approaches in their studies for arsenic elimination from wastewater. Only a few of the works (Yan et al., 2024; S. Zhang et al., 2011; Y. Zhang & Liu, 2019) deployed either computational or hybrid approaches in their investigation. Some of these theoretical studies explored metallic oxides like calcium oxides, iron oxides, and other related sorbents. Wijaj et al. (2016) studied the interaction trends of doping elements on the periodic table with graphene sheets. However, a detailed report for the adsorption mechanism is not reported in the report.
Among the few computational studies that explored arsenic adsorption, there are insignificant reports accounting for the molecular-scale details of arsenic adsorption on graphene-based surfaces or membranes. In this study, we explore the different arsenic adsorption mechanisms on a pristine graphene sheet, which was investigated in a vacuum and aqueous (filled with water) systems using quantum mechanic calculation methods to unravel the effect of solvent on the arsenic adsorption capacity of the pristine graphene. In a nutshell, this study focuses on investigating graphene's adsorption capabilities and its potential for removing arsenic, a major pollutant in wastewater.
This section presents the overall approach, structural optimization, energy calculations, adsorption energy analysis, and other computational details employed in our study.
2.1 Study Strategy
Here, we explored the various adsorption mechanisms via the exploration of all possible adsorption sites, which include top edge and center (TE/TC), bridge edge and center (BE/BC), and hollow edge and center (HE/HC). Schematic diagrams illustrating the molecular structure of each mechanism before geometry (or structural) optimization are presented in Fig. 1.
All the possible site adsorptions were explored and the most stable site adsorption modes are confirmed for each case like hollow, bridge, and top, using their respective adsorption energies (or strengths) for arsenic (As) as shown in Fig. 1. Similar sites were explored for water adsorption on the graphene membrane sheet. In this study, we modeled our wastewater to be majorly composed of water and arsenic only, as our synthetic wastewater components, similar to the practice in a wet laboratory experiment.
2.2 Energy and Geometry Optimization Calculation
The molecules in this study were constructed using the Spartan Student v9.0.3 molecular modeling package on a Lenovo T495 laptop. Structural optimizations were conducted utilizing the PM3 method (Hehre, 2003; Wu et al., 2014) with a convergence criterion of 10− 9 atomic units (a.u.). Subsequently, employing the PM3 optimized geometry, single-point energy calculations were performed using a DFT-based approach on a Dell Precision Mobile Workstation 3250. These calculations utilized a 6-31G* basis set (Hehre, 2003) and were carried out in both vacuum and water environments, using the B3LYP-D3 method within the Spartan v24 (Wavefunction, 2023) molecular modeling package. The B3LYP-D3 method incorporates the Grimme D3 dispersion correction to enhance the accuracy of energy calculations (Goerigk, 2017; Oyegoke et al., 2024; Schröder et al., 2017).
Here, we present the impact of different adsorption modes on the graphene surface. We will report the geometry of the interacting species in Fig. 2 and the adsorption energies across the respective sites in vacuum and water systems in Tables 1 and 2.
Additionally, the corresponding adsorption energy of each mode will be detailed, offering a comprehensive understanding of the adsorption mechanisms, and their relative strengths are presented in Figs. 3 and 4.
3.1 Analysis of Arsenic Adsorption in A Vacuum Environment
In this section, we delve into the analysis of adsorption strength across various adsorption modes on the graphene surface. Understanding these strengths is pivotal for assessing the efficiency and efficacy of graphene as an adsorbent material for arsenic removal from wastewater. In Table 1, we present the adsorption energies obtained across various modes of adsorption investigated in a vacuum. These modes encompass physisorption (Phys.), top edge (TE), top center (TC), double-bonded-bridge edge (BdE), double-bonded-bridge center (BdC), hollow edge (HE), hollow center (HC), single-bonded-bridge edge (BsE), and single-bonded-bridge center (BsC), following the related work presented in the literature (Wambu, 2024).
Table 1. Adsorption energies (Eads) in eV for different modes of As-graphene in a vacuum. (Note: TE – Top site at the edge, TC – Top site at the center, BdE – Bridge (double bond) at the edge, BdC – Bridge (double bond) at the center, HE – Hollow at the edge, HC – Hollow at the center, BsE – Bridge (Single Bond) at the edge, BsC – Bridge (single bond) at the center)
|
Mode |
Eads (eV) |
|
Phys |
-0.01 |
|
TE |
-1.97 |
|
TC |
-0.79 |
|
BdE |
-1.96 |
|
BdC |
-0.25 |
|
HE |
-1.97 |
|
HC |
-0.25 |
|
BsE |
-1.96 |
|
BsC |
-0.25 |
The data in Table 1 reveal that the most effective adsorption modes in a vacuum are TE, BdE, BsE, and HE, each exhibiting an adsorption energy of -1.97 eV. This indicates strong adherence of arsenic to these specific sites on the graphene surface. Conversely, physisorption shows a notably weak adsorption energy of -0.01 eV, signifying minimal interaction with the graphene surface. Center site adsorption modes (TC, BdC, HC, and BsC) demonstrate weaker adsorption than edge modes, especially the bridge sites, that suggest bridge edge sites on the graphene membrane surface offer superior adsorption capacity for arsenic. Our findings confirmed an adsorption site different from the study of Zhang and Liu (2019), whose work on arsenic adsorption using iron (III) oxide showed better adsorption via the surface top site. The findings suggest that arsenic adsorbs differently on different material surfaces.
3.2 Analysis of Arsenic Adsorption in Aqueous Environments
In Table 2, we present the adsorption energies of the same modes previously explored in the vacuum system, now in water. In the presence of water, the interaction between arsenic and graphene was slightly altered due to the competition of water with arsenic for adsorption sites on the graphene surface, unlike the case of vacuum adsorption. The findings made were found to have agreed with the literature (Anićijević et al., 2023; Israt Jahan et al., 2008; Tamaddoni Moghaddam et al., 2023) which established that the higher an adsorbate (arsenic) concentration the weaker the adsorption strength associated with the adsorbate competition for the limited surface sites present on an adsorbent.
Table 2. Adsorption energies (Eads) in eV for different modes of As-graphene in water. (Note: TE – Top site at the edge, TC – Top site at the center, BdE – Bridge (double bond) at the edge, BdC – Bridge (double bond) at the center, HE – Hollow at the edge, HC – Hollow at the center, BsE – Bridge (Single Bond) at the edge, BsC – Bridge (single bond) at the center)
|
Mode |
Eabs (eV) |
|
Phys |
-0.02 |
|
TE |
-1.79 |
|
TC |
-0.65 |
|
BdE |
-1.78 |
|
BdC |
-0.24 |
|
HE |
-1.79 |
|
HC |
-0.24 |
|
BsE |
-1.78 |
|
BsC |
-0.24 |
In water, the adsorption energies are slightly less negative than in a vacuum, indicating weaker adsorption overall. However, the trend remains similar, with TE, BdE, BsE, and HE modes still showing the strongest adsorption, with slightly reduced energies of -1.79 eV for TE and HE, and − 1.78 eV for BdE and BsE (Table 2). Physisorption remains weak, with an adsorption energy of -0.02 eV, reflecting minimal interaction with the graphene surface. The center adsorption modes (TC, BdC, HC, and BsC) again show weaker adsorption compared to edge modes. The stronger adsorptions show a shorter interaction distance with the arsenic, unlike the case of others that were wider, which followed the literature (Oyegoke et al., 2018; Zhao et al., 2022) that established that the longer a bond, the weaker its strength. However, according to other literature (Kraka et al., 2016), such relationships are not valid, contrary to the Zhao et al. (Zhao et al., 2022) report, which agreed with our findings.
The evaluation of geometries obtained for adsorption modes reveals significant deviations from their initial positions and modes in Fig. 1 to new positions in Fig. 3. This confirms the shift of the arsenic atom towards the bridge position across various adsorption sites, transitioning from Top (T), Bridge (B), and Hollow (H) to the Bridge (B) position at both edge (E) and center (C) sites.
3.3 Analysis of Geometrical Structures and Solvation Effects
The evaluation of geometries reveals a significant deviation from the initial positions and modes modeled in Fig. 1. This confirms that the arsenic atom shifts towards the bridge position across various adsorption sites, transitioning from Top (T), Bridge (B), and Hollow (H) to assuming the Bridge (B) position at both edge and center sites. This indicates that the bridge mode is the most preferred adsorption mode.
Additionally, coherency analysis using a plot in Fig. 4a gave an R-square value of 0.999, indicating a high correlation between the adsorption energies computed in vacuum and water, with a difference of about 9% in their adsorption strength. This suggests that the arsenic adsorption energies in water are generally about 9% weaker than those reported for vacuum (gas) systems, which agreed with Humpola et al. (2013) report that shows weaker adsorption of phenol in water than one adsorbs in void space (that is, vacuum). Furthermore, the results in Fig. 4a show that all the adsorption modes or sites evaluated in our study can be categorized into four groups, following the trend of adsorption energies and modes observed after optimizing their systems in both water and vacuum, as evidenced by the four distinct points in Fig. 5a. The findings were found to agree with the literature reports (Húmpola et al., 2013) that have shown a different adsorption strength for the sorption of a species across different mediums like vacuum and water.
The comparison of adsorption energies in vacuum and water reveals that the adsorption of arsenic on graphene is relatively stronger in vacuum than in water, identifying the edge modes (TE, BdE, BsE, and HE) as the most effective adsorption sites, with strong adsorption energies in both vacuum (-1.97 eV) and water (-1.98 eV) (Fig. 4b). This suggests that the absence of competing species in a vacuum system, different from the aqueous system (where water is present), must have strengthened the graphene adsorption capacity for arsenic in a vacuum (void) system. This was similar to the report of the literature (Anićijević et al., 2023; Israt Jahan et al., 2008; Tamaddoni Moghaddam et al., 2023) that shows that less or reduced competition (that is, reduced, lower, or lesser pollutant concentration) does yield a stronger adsorption strength.
In our analysis, we further identified the most stable As-graphene geometries obtained for adsorption in water in Table 2, including TE, BdE, BsE, and HE as the most stable modes of adsorption. Figure 4 shows the various bond lengths and bond angles of the three structures. No significant difference was seen in the interaction bond distance for the adsorption processes explored across the various sites presented in Fig. 3, except for the result obtained for physisorption, which was significantly different. This similar trend is also visible in Fig. 5b. A good correlation was seen for the distances obtained in Fig. 3 for the physisorption and chemisorption modes, with their corresponding strengths reported as approximately − 0.02 eV (-0.01 eV) and − 1.8 eV (-2.0 eV), respectively, for water (vacuum) in Tables 1 and 2, which agreed with the literature (Húmpola et al., 2013) that shows similar different adsorption strengths across different sorption media.
3.4 Graphene Adsorption Strength for Water in A Vacuum
To further understand and validate the earlier data presented in this study, which supports the surface of the adsorbent as a potential material for the adsorption of arsenic in wastewater (known for being an aqueous environment), we investigated the affinity of the membrane sheet for water molecules. This, in turn, provides an understanding to some extent of the surface hydrophilicity or hydrophobicity (He et al., 2016; Hong et al., 2020) regarding the preference of surface adsorption strength for the pollutant. Here, we explored the different modes of water adsorption on the graphene membrane surface as presented in Table 3 to facilitate an understanding of the extent of water interaction with the surface.
Table 3 presents a comparative analysis of the strengths of various water adsorption modes, relative to their corresponding geometries indicating their proximity to the membrane surface (as shown in Fig. 5). These analyses suggest that water molecules generally prefer to be adsorbed around the edge sites of the membrane surface. This preference is evident in the bent position of the water molecule, with its hydrogen atom facing the membrane surface. The optimum adsorption position identified via our studies was found to have agreed with the literature (Liang et al., 2021), which shows that water adsorbs via the hollow site of the graphene surface with its hydrogen atom of the water molecule pointed vertically downward to the surface.
Table 3. Adsorption energies (Eads) in eV for different modes of H2O on graphene. (Note: TE – Top site at the edge, TC – Top site at the center, BdE – Bridge (double bond) at the edge, BdC – Bridge (double bond) at the center, HE – Hollow at the edge, HC – Hollow at the center, BsE – Bridge (Single Bond) at the edge, BsC – Bridge (single bond) at the center)
|
Mode |
Eads (eV) |
|
Phy |
-0.2592 |
|
TE |
-0.1971 |
|
TC |
-0.2368 |
|
BSE |
-0.1961 |
|
BSC |
-0.0207 |
|
BDE |
-0.1844 |
|
BDC |
-0.0086 |
|
HC |
-0.2545 |
|
HE |
-0.2321 |
The bond distance in Fig. 5, between the lowest hydrogen atom of the water molecule and the nearest carbon atom of the absorbent surface, is smaller for edge sites (especially the hollow sites (HE) at the edges of the sheet) compared to their center (C) counterparts. This observation indicates a stronger interaction between water molecules and the graphene membrane at the edges, highlighting the significance of edge sites in water adsorption. The hydrogen distance of the water molecule with the graphene surface reported for our study as 2.76 to 2.78 Å was found to be longer compared to one reported by Liang et al. (2021) as 2.16 Å with a stronger adsorption strength (-0.53 eV) on a single-vacancy-graphene surface sheet, unlike our study’s sheet that lacks vacancies.
3.5 Assessment of Graphene Adsorption Selectivity
Here, we evaluated the adsorption of arsenic on graphene (Tables 1 and 2) and compared it to the water adsorption process presented in Table 3. This comparative analysis examines the relationship between water and arsenic adsorption on the graphene surface, predicting the graphene membrane's preference in terms of selectivity.
The comparative analysis of the results presented in Tables 1 to 3 and Figs. 3 and 5 for arsenic and water competition for adsorption on the graphene membrane surface indicates that the adsorption strength of arsenic is much stronger than that reported for water adsorption. Further analysis of their adsorption geometrical structures reveals corresponding deductions reported in Tables 1 to 3, with large interaction or bonding distances reported for water and shorter distances reported for arsenic. The strongest arsenic adsorption mode shows a distance of 2.11 Å at the bridge edge (HE) site, while the strongest water adsorption shows a distance of 2.76 to 2.78 Å at the hollow center (HC) site, which agreed with the literature(Zhao et al., 2022) that established the relationship between bond length and their strength. These findings suggest that the graphene membrane surface would selectively attract the arsenic pollutant more effectively than the water molecules present in the model wastewater
In this study, we conducted a comprehensive evaluation of the effectiveness of a graphene membrane as an adsorbent for removing arsenic components from industrial wastewater. Our approach involved detailed modeling and analysis of various adsorption mechanisms. We explored different adsorption sites, including Top edge and center (TE/TC), Bridge edge and center (BdE/BdC), and Hollow edge and center (HE/HC), under both vacuum and aqueous environments. We identified the most viable and stable adsorption modes for optimal arsenic removal in wastewater and demonstrated the membrane's potential for purifying both gas (using a vacuum environment) and water (using an aqueous environment) streams contaminated with arsenic.
Our analysis of adsorption mechanisms confirmed that arsenic tends to adsorb preferentially via the bridge site of the graphene membrane's edge sites across various adsorption sites. This indicates that the bridge edge site mode is the most preferred adsorption configuration for arsenic. In contrast, water preferentially adsorbs via the hollow center sites of the membrane sheet. A comparative analysis of water and arsenic adsorption competition revealed that the graphene membrane surface would adsorb arsenic more readily than water. This suggests that graphene and other carbon-based materials with similar geometrical structures and properties could effectively remove arsenic from wastewater.
Future studies could investigate methods to enhance the adsorption capacity of graphene-based materials, such as functionalization or modification techniques, and conduct competitive adsorption studies to evaluate their performance in the presence of other contaminants commonly found in wastewater. These studies could improve the efficiency of graphene-based adsorbents in removing contaminants from water and provide insights into their potential applications in water treatment.
Conflict of Interest
The author declares no conflict of interest in this study.
Acknowledgement
The authors wish to acknowledge the support of Wavefunction Inc US for providing a discounted license for Student Spartan v9 and a free license for Spartan 24, which were respectively used for semi-empirical calculation and dispersion corrected B3LYP calculations in the energy calculation.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data is provided within the manuscript.
CRediT Authorship Contribution Statement
Olusola Ibraheem Ayeni: Writing – original draft, Visualization, Validation, Software, Investigation, Formal analysis, Computation, Simulation, Funding Acquisition.
Toyese Oyegoke: Project administration, Supervision, Writing – review & editing, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Computation, Simulation, Funding Acquisition, Conceptualization.
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