Response surface modelling and optimization for copper removal from acid mine drainage using oxidized Himalayan pine needle biochar

Elevated copper levels harm both plant and human health. In plants, it hinders growth, disrupts cellular function, and affects photosynthesis (Simate & Ndlovu 2014; Masindi et al. 2018). In humans, it leads to conditions like anaemia, liver and kidney damage, and stomach/intestinal irritation. Additionally, copper, along with HMs like cadmium, lead, and zinc, poses significant threats to aquatic life (Jiwan & Ajay 2011; Rodríguez-Galán et al. 2019). Acute exposure to these metals, with high short-term concentrations, can result in direct mortality, while chronic exposure, characterized by prolonged low concentrations, can lead to stunted growth, reduced reproduction, and deformities (Jiwan & Ajay 2011; Simate & Ndlovu 2014; Shane et al. 2021).

To date, various traditional methods, including chemical precipitation, ion exchange (Zhang et al. 2013), coagulation, membrane filtration, zero valent iron and adsorption have been employed to eliminate HMs from water (Al-Saydeh et al. 2017; Crane & Sapsford 2018). Among these approaches, adsorption distinguishes itself as a favoured option owing to its operational simplicity and notable treatment effectiveness. A range of adsorbents, including activated carbon, ion exchangers, cost-effective alternatives (such as biomass based sorbents), and biochar have been developed by various researchers for the effective HMs removal from contaminated water (Anastopoulos et al. 2019; Qiu et al. 2021).

Biochar, a carbon-rich organic material produced through biomass pyrolysis in low-oxygen conditions (EBC 2022), is an affordable and effective option for removing HM ions from polluted water (Qiu et al. 2021). Its negative surface charge, high specific surface area (SSA), porosity, and charge density make it compatible and selective for the adsorption of HMs (Deng et al. 2017; Qiu et al. 2021). Biochar application in water enhances the immobilization of HMs through a combination of electrostatic and non-electrostatic forces (Deng et al. 2017). A number of studies have been carried out by the various researchers on the removal of Cu(II) by biochar in aqueous solutions (Chen et al. 2011; Choudhary et al. 2020). Biochar derived from corn straw exhibited maximum Cu(II) adsorption capacity of 12.5 mg/g (Chen et al. 2011). The maximum sorption capacity for Cu(II) removal using pine needle biochar (PNB) was reported as 40.4 mg/g (Choudhary et al. 2020). On the other hand, researchers have reported exceptionally high adsorption capacity upto 373 mg/g for Cu(II) adsorption using activated carbon prepared from walnut shell (Shu et al. 2017).

In comparison to commercial activated carbon, biochar exhibits a relatively low adsorption capacity for HMs. Consequently, various modification methods were employed to enhance its HMs removal capabilities. Common methods involve the modification through H₃PO₄ (Peng et al. 2017), KOH (Trakal et al. 2014; Bashir et al. 2018), FeCl₃ for chemical modification, and physical activation techniques such as steam (Kwak et al. 2019) and carbon dioxide activation (Chang et al. 2000). However, these activation processes often require high temperatures, particularly for steam, and carbon dioxide activation (typically > 800 °C), leading to increased adsorbent costs (San Miguel et al. 2003).

In contrast, H₂O₂ is a relatively inexpensive and clean alternative, unlikely to cause secondary pollution as its decomposition products are H₂O and O₂ (Wang & Liu 2018). On the other hand, H₂O₂ oxidation enhances biochar physicochemical properties by introducing oxygen-containing functional groups (–COOH and –OH), expanding its SSA and pore size (Fan et al. 2018). Key factors in the adsorption process include π–π electron donor-receptor interactions, hydrogen bonding effects, hydrophobic distribution, and pore filling effects (Bhattacharyya 2016; Wang & Liu 2018). Additionally, HM adsorption on biochar primarily occurs through cation-π interactions, surface complexation, and ion exchange. The formations of stable complexes or competition for sites with multifunctional groups are crucial mechanisms for inorganic pollutant interactions with HMs on biochar (Fan et al. 2018). These relationships are vital for analyzing the impact of H₂O₂ concentration on metal removal.

Accordingly, in this study, Himalayan PNB was oxidized with H₂O₂ to enhance its performance and specificity in Cu(II) uptake. This study used the response surface methodology (RSM), employed through Design-Expert software (version 11.0), for designing experiments and statistical optimization. Additionally, the research considered four parameters (Cu(II) concentration, adsorbent dose, contact time and pH), various adsorption isotherms, and kinetic models for analyzing the equilibrium data.

PNB was prepared from the Himalayan pine needles (PNs) according to the method adopted elsewhere (Bashir et al. 2022). The oxidized biochar was prepared at ambient temperature (25 ± 2 °C), by mixing 3 g of PNB sample with 20 mL of 3, 10, 20, and 30% H₂O₂ solutions (Wang & Liu 2018). The resulting oxidized biochar was washed with deionized water (DI) until a neutral pH was achieved, followed by drying at 105 °C for 24 h to remove moisture. The H₂O₂-oxidized biochar was labelled as OPNB_3, OPNB_10, OPNB_20, and OPNB_30 after treatment with 3, 10, 20, and 30% H₂O₂ solution, respectively. Biochar and the oxidized biochar samples were kept in a sealed container for further experiments.

Copper (Cu(II)) stock solution of 1,000 mg/L was prepared by dissolving 3.804 g of Cu(NO₃)₂·3H₂O in 1 L deionized (DI) water. Solutions with varying concentrations of Cu(II) determined through central composite design (CCD) as outlined in Table 1 were subsequently prepared using the stock solution.

During the study, Cu(II) batch adsorption experiments were conducted with synthetic wastewater (SWW) that mimicked real AMD. Table 1 provides a summary of the process parameters used in the batch tests. The copper concentrations were varied within the range of 12.5–162.5 mg/L (12.5, 50, 87.5, 125, and 162.5 mg/L), which is consistent with Cu(II) concentrations reported in AMD in various studies (ranging from 3 to 138 mg/L) (Edraki et al. 2005; Ayora et al. 2016; Rodríguez-Galán et al. 2019). The pH values were adjusted to 1.5, 3, 4.5, 6.0, and 7.5 to cover a broad spectrum of experimental conditions. Additionally, different contact times ranging from 60 to 1,260 min were examined (Table 1). The adsorbent dosages used in the study ranged from 0.2 to 1 g/L for both the adsorbents (PNB and OPNB).

The adsorption experiments were conducted in triplicate using 50-mL Erlenmeyer flasks, each containing 20 mL of Cu(II) ion solutions with varying initial concentrations and employing PNB and OPNB adsorbents. pH of the solutions at initial stage of experiments was maintained using either 0.01 M HCl or 0.01 M NaOH. To facilitate effective contact between the adsorbent and adsorbate, the samples were mixed at 200 (revolutions per minute) RPM using an orbital shaker (Tarson, 3050, Spinix, MC01) at an ambient temperature of 25 ± 2 °C. All the experiments were conducted in triplicate to minimize the errors. Subsequently, the supernatant was subjected to filtration using a 0.2-μm syringe filter and subsequently diluted with 2% HNO₃ for analysis.

Here, N indicates the total number of experiments, k indicates the number of factors or parameters, n₀ indicates the number of central point replicates.

Here, C_i indicates the initial concentration of Cu(II) (mg/L), C_e indicates the final concentration of Cu(II) (mg/L), V indicates the volume of the aqueous solution (L), M indicates the absorbent mass (g).

To understand the Cu(II) adsorption mechanism using PNB and OPNB as an adsorbent, three models, namely, Langmuir, Freundlich, and Temkin adsorption isotherms were investigated (Bashir et al. 2022). Isotherm models assist in designing optimal adsorption conditions for various applications.

Here, n indicates the dimensionless constant (signifying the adsorption intensity).

In general adsorption kinetics is a graphical representation, which depicts the rate at which a solute is either retained or released from an aqueous solution to the solid-phase interface, considering factors such as adsorbate concentration, adsorbent dosage, temperature, and pH (Fakhri 2017). The adsorption process involves two primary mechanisms: physical adsorption, or physisorption, which arises from weak forces like van der Waals interactions, and chemical adsorption, or chemisorption, where a strong bond forms between the adsorbate and the adsorbent, involving electron transfer.

The kinetic study of the data obtained from copper sorption batch test was analyzed by using the following kinetic models:

The determination of the physical properties of PNB and OPNB_3, OPNB_10, OPNB_20, and OPNB_30 such as pore size, SSA and pore volume were meticulously examined using BET analyzer (Quanta chrome, Autosorb iQ3) in inert atmosphere using N₂ adsorption at 77 K. The samples were vacuum degassed for 24 hours at 150 °C before BET analysis. The elemental composition of PNB and OPNB was assessed through a CHNSO analyzer (UNICUBE plus) to determine their C, H, N, and O content. The surface morphology images of the PNB, as well as those of the OPNB, were examined using the scanning electron microscope (SEM) system, referred to as EDX-SEM. Fourier transformation infra-red (FTIR) spectroscopy analysis (FTIR L1600312, Agilent Technologies) was conducted to identify the surface functional groups present on the adsorbents (PNB and OPNB) surface. Inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7850) was employed to determine the final concentration of Cu(II) ions present in the SWW.

Table 2 outlines the surface characteristics and elemental properties of PNB, OPNB_3, OPNB_10, OPNB_20, and OPNB_30 after treatment with 3, 10, 20, and 30% H₂O₂ solutions, respectively. During the pyrolysis, lignocellulosic biomass underwent decarboxylation and decarbonization, resulted in the production of biochar enriched with surface functional groups, minerals, and ash content. Previous studies reported that H₂O₂ modification is more effective in removing inorganic material, potentially due to carbon oxidation by H₂O₂, and making ash removal easier (Bhattacharyya 2016; Wang & Liu 2018).

In comparison to the initial biomass (PN) with carbon content, PNB and OPNB exhibit increased carbon and nitrogen content, while hydrogen content and the H/C ratio decrease. The oxygen content of the raw material drops after pyrolysis but increases when treated with H₂O₂. After H₂O₂ modification, the O content in the biochar rises, attributed to surface carbon oxidation and the proliferation of oxygen-containing functional groups, especially carboxyl groups. The SSA of PNB initially measures around 36.4 m²/g. However, the SSA of OPNB increases when treated with different concentrations of H₂O₂ (Table 2) reflecting the removal of ash content from the pristine biochar through a DI water washing process following the modification.

The produced PNB underwent treatment with varying H₂O₂ concentrations (Table 2) for optimized characteristics like a higher SSA and increased oxygen content. Notably, the SSA of PNB significantly increased to 145.7 m²/g when treated with 10% H₂O₂, compared to its untreated value of 36.4 m²/g . This substantial increase in SSA was primarily attributed to the removal of organic matter and ash content, subsequently enhancing the porosity of PNB after H₂O₂ treatment (Wang & Liu 2018; Wongrod et al. 2018, 2019). However, it's noteworthy that the SSA decreased when subjected to higher H₂O₂ concentrations, such as 20 and 30% (Table 2). This decline in SSA can be attributed to the intensified oxidation process, which disrupted the porous structure and channels within the biochar (Nie et al. 2019). As the optimized properties were obtained at the 10% H₂O₂ concentration, thereafter all the experiments were conducted with the OPNB_10 and named as OPNB for the further experiments.

Table 3 presents the results of 30 adsorption experiments, which were designed using CCD under RSM. These results include 8 axial points, 16 cube/factorial points, and 6 duplicates, obtained under the respective experimental conditions. The actual Cu(II) adsorption capacity (Q_a) and the predicted adsorption capacity (Q_p) for the adsorbents PNB and OPNB presented in Table 3 showed good proximity between the Q_a and Q_p values.

Values with positive sign in Equations (23) and (24) signify an incremental effect of the variable on the adsorption capacity, while values with negative sign indicate a decremental effect of the variable on the Cu(II) adsorption capacity for PNB and OPNB, respectively.

The predicted adsorption capacity values from Equations (23) and (24) exhibit a close agreement with the experimental values, as presented in Table 3. Assessment of the values presented in Table 3 reveals minimal residuals between the Q_a and Q_p. Model suitability was assessed through correlation coefficients R² and adj. R². Referring to the analysis of variance (ANOVA) results (Tables 4 and 5), the R² and adj. R² values were determined to be 0.96 and 0.92, respectively, in case of PNB. Whereas in case of OPNB these values were found to be 0.99 and 0.98. The high R² values underscore a strong correlation between the predicted and actual response values. Additionally, adj. R² value indicates that models describe the 92 and 98% variation in Cu(II) adsorption is accounted for the independent variables in case of PNB and OPNB, respectively. While only 8% for PNB and 2% for OPNB, indicating the portion of variability not captured by these models.

Moreover, the ANOVA for Cu(II) adsorption was used during the study for determining the adequacy of the model. Tables 4 and 5 presented ANOVA for PNB and OPNB adsorbents, respectively, where the significance of each variable was determined through the Fischer's test coefficients (F) and probability (P) values. Assessing a regression models significance involves examining the F-value for overall effectiveness and the P value for individual coefficients significance. Higher F values indicate stronger relationships, while lower P values signal greater confidence in specific variable contributions (Lan et al. 2011). This dual evaluation enhances our understanding of model validity and variable impact. In Tables 4 and 5, it is evident that the RSM-proposed model is highly significant, as indicated by a high F-value of 66.61 and a minimal P value of less than 0.0001 in the case of PNB. Meanwhile these values were found to be 335.54 and <0.0001 in the case of OPNB. The individual coefficients, including linear, square, and quadratic terms, also exhibit high significance, with large F values and correspondingly low P values (Tables 4 and 5). In the case of PNB, terms like AB, AC, and AD are deemed non-significant, as indicated by P > 0.05. Similarly, for OPNB, terms such as AC, BC, CD, and C² are considered non-significant with P > 0.05. Furthermore, P-value for PNB 0.3616 and 0.7079 for OPNB suggests that the lack-of-fit has no significance, affirming the validity of the regression analysis for Cu(II) adsorption.

Figure 4(b), 4(d) and 4(f) illustrate the effect of adsorbent (PNB) dosage on the adsorption of Cu(II). Likewise, Figures 5(b), 5(d), and 5(f) depict the impact of OPNB dosage on Cu(II) adsorption of Cu(II). Figure 4(f) for PNB and Figure 4(f) for OPNB show the adsorption capacity as pH and adsorbent dosages vary. Also, the Figures 4(b), 4(d), 5(b) and 5(d) illustrate a decrease in Cu(II) uptake as the adsorbent dosage increases from 0.4 to 1.0 g/L for both the adsorbents. This paradox can be ascribed to the fact that, despite the increase in adsorption sites at higher adsorbent doses, the adsorption reaction does not saturate, leading to a decline in Cu(II) uptake. Moreover, the cluster of the adsorbents at higher doses may contribute to the reduction in Cu(II) removal capacity. Interestingly, optimal Cu(II) adsorption occurs at lower adsorbent dosages and higher pH levels. This occurrence is likely elucidated by the heightened accessibility of adsorption sites at higher pH values, enhancing Cu(II) interaction with the adsorbents. This phenomenon underscores the characteristic of the chelation mechanism, which can be ascribed to the reduced competitive adsorption of H⁺ at higher pH (Huang & Chen 2009; Tan et al. 2012). Nevertheless, with an increasing quantity of adsorbent, there is a rise in effective surface area and the ratio of adsorbent to adsorbate, leading to a decrease in the uptake of Cu(II) (Ahmad & Hasan 2016).

In Figure 4(a)–4(c), the results distinctly demonstrate an initial increase in adsorption capacity with contact time for PNB, reaching a peak, and then experiencing a slight decrease. Likewise, for the adsorbent OPNB, the adsorption capacity exhibits an increase over time, followed by a decrease, as depicted in Figure 5(a)–5(c). The increase in adsorption capacity at initial stage with time can be due to availability of more vacant sites for the adsorption. This can relate with the enhanced SSA and increased oxygen content (Table 2) resulting from the oxidation of biochar. Consequently, the elevated concentration of O-containing functional groups contributed to the increment in Cu(II) uptake. Nevertheless, a subsequent increase in contact time resulted in a reduction in adsorption capacity, attributed to the desorption of Cu(II) into the solution after attaining the equilibrium.

In the complicated realm of HM removal, the pH of the solution contributes as an important factor (Ahmad & Hasan 2016). Influence of pH is illustrated in the Figure 4(c), 4(e) and 4(f) for the adsorption capacity of PNB. Similarly Figure 5(c), 5(e) and 5(f) demonstrate the influence of pH on Cu(II) adsorption for the adsorbent OPNB. A meticulous examination of the data in Figures 4(c) and 5(c) elucidates a nuanced relationship between the adsorption rate of Cu(II) and the pH variation, revealing a notable augmentation as pH values ascend from 3 to 6. The pivotal role of pH becomes distinct below the pH threshold of 3.0, where a competitive interaction occurs between H⁺ ions and Cu(II) ions for the adsorbent surface. This competition introduces a barrier to the approach of Cu(II), dictated by repulsion forces, resulting in a diminished metal removal rate (Luef et al. 1991). This interaction implies that, at pH levels below 3.0, protons compete with Cu(II) for ligand binding sites and complex formation, which contributes to the observed decrease in metal removal. As pH ascends, H⁺ ions decrease, increases Cu(II) adsorption. This observed increase in adsorption capacity with an increasing pH can be elucidated by the heightened negative charge characterizing the surface of the adsorbent (Kalavathy et al. 2009).

The final step in the RSM involves determining the optimal conditions for achieving maximum Cu(II) uptake yields through the adsorption process using PNB and OPNB as adsorbents. For optimization purposes, the software integrates global information from the model, considering response surfaces, and the interactions among all the process variables to identify the optimum conditions for the overall process. The evaluation of optimal conditions is performed using the desirability function (d). To ensure comprehensive coverage of parameter variations, the lower limit (d = 0, representing the response at its undesirable limit) and upper limit (d = 1.0, signifying a highly desirable limit of the response) were set.

The Freundlich isotherm (Figure 8(b)) model exhibited R² values were determined to be 0.88 and 0.95 for PNB and OPNB, respectively, indicating a suitable-fitted model for Cu(II) adsorption (Figure 8(b)). The Freundlich constants (K_F), reflecting the energy of adsorption, were 8.35 mg/g for PNB and 24.04 mg/g for OPNB. The intensity of adsorption (n) suggested cooperative adsorption for PNB (4.46) and normal adsorption for OPNB (3.44).

Also, the Temkin model displayed a R² of 0.81 for PNB and 0.91 for OPNB (Figure 8(c)), along with corresponding adsorption constants B_T and K_T presented in Table 6. It was indicated by the lower R² values for PNB and OPNB that the Temkin model did not adequately fit the data (Table 6). These findings indicated a lower degree of surface heterogeneity and compatibility with both the Langmuir and Freundlich isotherms.

As here in the present study of value of R² obtained for Temkin model and Freundlich model was found to be lower than those obtained in the Langmuir model. High R² (0.99) value and closeness among the experimental and predicted values from models of adsorption capacity suggested that the monolayer adsorption phenomenon for the uptake of Cu(II) for using the absorbents PNB and OPNB.

Also, in most AMD the discharged effluent typically encompasses Cu(II) concentrations within the interval of 3–138 mg/L (Edraki et al. 2005; Ayora et al. 2016). The present study showed that the OPNB has an adsorption capacity of up to 102.04 mg/g and it can efficiently remove Cu(II) up to 125 mg/L. Thus, the selected concentration holds practical significance for the treatment of AMD. During this study, it was found that the OPNB can efficiently remove Cu(II) up to 125 mg/L with an adsorption capacity of up to 102.04 mg/g. Thus, the chosen concentration holds practical significance for AMD treatment.

In Table 7, constants k₁, k₂ and k₃ represent the rate constants for the adsorption kinetics of PFO, PSO and IPD, respectively. R² serves as the correlation coefficient, gauging the degree of agreement between model-predicted values and experimental data. To assess the appropriateness of each model, all of them were subjected to linear regression analysis with the results of experimental data. The R² and the agreement between Q_exp. and predicted Q_p values serve as criteria for model applicability.

The results in Table 7 demonstrate that the R² values of the PSO model were notably high, exceeding 0.98 for both the adsorbents PNB and OPNB. Furthermore, the predicted values closely corresponded to the experimental values obtained under identical conditions. The strong correlation between the adsorption process of Cu(II) and thus the PSO principles suggests that the adsorption rate is directly proportional to the square of the number of available adsorption sites (Duan et al. 2022). These findings imply that the adsorption of Cu(II) onto both PNB and OPNB involved a combination of physisorption and chemisorption, with chemisorption dominating the overall adsorption process. The PFO and IPD models, on the other hand, showed lower correlation coefficients than PSO and were found to be insufficient in explaining Cu(II) adsorption onto biochar as well as oxidized biochar.

The Cu(II) adsorption capacity in the fixed-bed column (31.7 mg/g) exhibited a lower value compared to the batch sorption capacity (90.88 mg/g). This disparity can be attributed to the existence of a broad mass transfer zone, ultimately leading to a reduction in the effective bed capacity (Choudhary et al. 2020).

The expected mechanism for copper removal can include ion exchange, physical adsorption, complexation, electrostatic attraction and precipitation. Out of these mechanisms precipitation can be ruled out since copper precipitation takes place beyond pH 9 while for the experiments the pH range maintained was 6–7. The adsorption capacity of the H₂O₂-oxidized biochar (OPNB) increased to about 3.5 times compared to the parent biochar. This can be attributed to the physical adsorption, electrostatic attraction, ion exchange and complexation.

The SSA of OPNB is significantly higher at 145.7 m²/g compared to 36.4 m²/g for PNB (Table 2). The increased SSA of OPNB offers more adsorption sites for Cu(II), surpassing PNB and hence demonstrating higher adsorption capacity. Also, the micro-and mesopores within the biochar trapped the Cu(II). Moreover, within the pH range (6–7) under consideration, the intricate process of Cu(II) adsorption onto the surfaces of PNB and OPNB was predominantly governed by physical adsorption through the confluence of fundamental forces. These encompass the van der Waals forces, which involve the attractive forces between molecules, the London forces, arising from temporary dipoles, and electrostatic forces such as dipole–dipole attractions (Bashir et al. 2022; Osman et al. 2023). The interplay of these forces contributes to the meticulous and dynamic nature of the adsorption phenomenon, highlighting the complex interactions that dictate the affinity of Cu(II) ions for the biochar surfaces. Hence, these weak forces signify physisorption as the predominant adsorption mechanism, characterized by its reversible nature.

In this process (Equation (25)), the O–H bond is disrupted, and new bonds are formed between O and M²⁺ once the metal ions migrate to the biochars (PNB and OPNB) surface (Wang & Liu 2018). The H₂O₂ modification increased the concentration of carboxyl groups in PNB, contributing substantially to the enhanced Cu(II) adsorption capacity of the biochar (Wang & Liu 2018). Furthermore, the pH_PZC of OPNB is 5.1 which is lower than the pH_PZC of PNB 5.7. Hence, the surface of OPNB is expected to be more negatively charged as that of PNB, allowing higher Cu(II) adsorption on OPNB surface.

Furthermore, the biochar surfaces contain exchangeable cations such as calcium, sodium etc. as reported elsewhere (Bashir et al. 2022). These cations replaced by Cu(II) ions through an ion exchange process which facilitated the swapping of metal ions for the native cations on the PNB and OPNB surface. Thus, this swapping/interchange of ions helps in removing copper metal from the aqueous solution.

The comparative analysis of Cu(II) adsorption capacities employing various adsorbents is outlined in Table 9. The findings indicate that the adsorption capacity achieved with OPNB was 102.4 mg/g. Notably, Table 9 illustrates a significantly higher adsorption capacity for OPNB compared to its precursor biochar (PNB). Additionally, the table reveals that the modification of PNB through H₂O₂ treatment resulted in a comparatively higher adsorption capacity than that of the other adsorbents (Table 9). Thus, it can be interpretated that the oxidation of the biochar increases the adsorption capacity much higher than the original biochar.

Oxidation of PNB significantly enhances its physicochemical properties, including porous structure, functional groups, and textural characteristics. RSM was utilized to design and optimize the sorption of Cu(II) ions for PNB and OPNB. The optimal values obtained were 125 mg/L initial Cu(II) concentration, 0.4 g/L adsorbent dosage, 360 min contact time and a pH of 6 with the desirability of 1 for OPNB. These optimal conditions resulted in Cu(II) adsorption of 102.04 mg/g in case of OPNB about 3.46 times more than that of PNB. Langmuir isotherm and the PSO model accurately fit the experimental results. Desorption study revealed that the OPNB has better reusability up to four cycles without losing its adsorption capacity due to its regeneration using the 0.1 M HCl. On the other hand, results of the column study demonstrated the superior performance of biochar after the oxidation using the H₂O₂. The superior performance of OPNB was attributed to its higher surface area, well-developed porous structure, and prevalence of active O-containing surface functional groups, as validated by BET, SEM, and FTIR analysis.

C.M. contributed to conceptualization, methodology, investigation, data curation, writing – original draft, review, and editing. M.B. contributed to methodology, investigation, data curation. A.A. and A.K.D. contributed to conceptualization and supervision.

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

The authors declare there is no conflict.