Biosorption behaviour of Mangifera indica biowaste as function of Pretreatment

Biosorption behaviour of Mangifera indica biowaste as function of Pretreatment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Biosorption behaviour of Mangifera indica biowaste as function of Pretreatment Bhumika Kumari, jayshree ramkumar, Sankaran Chandramouleeswaran, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8729755/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Biosorption is a cost effective and clean process for the removal of toxic species. In this paper, an attempt has been made to make use of Mangifera indica biowaste for removal of cationic species. Different characterization techniques were used to characterize as well as understand the sorption process. The sorption behaviour was found to be dependent on the sorbent size with higher efficiency seen for samples with least size. The sorption of the dyes was found to be dependent on the nature of dye. The studies were carried out around pH ~ 6 as the pzc was obtained as 5.4. The differences in the sorption behaviour were further understood using molecular simulations. It was found that there was agreement between the experimental and simulation studies. Extensive equilibrium and kinetic modelling were carried out on the equilibraium and kinetic data obtained in this study. It was interesting to note that the mechanism followed in the sorption was dependent on the nature of solute as well as the size and pretreatment protocol. Mangifera indica biosorption dyes metal ions pretreatment molecular simulations Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1.0 Introduction The need for clean water is becoming a constant requirement, which encourages the use of various techniques to remove different species from water. The various species present, as contaminant may become pollutant based on the concentration in water. Hence, the treatment becomes important not only health reasons but also from aesthetic point of view. The presence of certain contaminants proves to be visually perturbing when present in water media. Irrespective of the reason, it becomes crucial to remove different species from water bodies and many techniques are available. At times, a combination of techniques proves to be very effective. Adsorption, better known, as sorption is very attractive as it is easy and allows the use of various materials as sorbents (Jayshree 2024 ). In general, synthetic materials with specific functional groups are normally used. Borosilicate glass was used as room temperature sorbents for the removal of dyes and metal ions (Chandramouleeswaran 2011). Nanomaterials with increased surface tend to show enhanced sorption efficiency. Our earlier works demonstrate the use of bare and modified nano sorbents for removal of toxic metal ions as well as dyes (Jayshree 2021). Due to the ongoing research for newer materials and also the concern towards the nature of sorbents itself, use of benign materials becomes very popular. The use of biowaste (Torres 2020 ) is attractive as it is aplenty thus making the processes cost effective. The process is facilitated by the presence of different functional groups (Yashikaa 2021). In this present study, the sorption efficiency of mango leaves studied with respect to various species. The Indian mango tree, known as Mangifera indica, is an evergreen plant which belongs to the family anacardiaceae. The annual output of mango in India is around 5 million tons which generates 2 million ton of mango leaves. These leaves are not consumed and are discarded or set on fire. Thus, the biowaste generated interest in various researchers who have used these for removal of various toxic species. Mango leaf powder particle (MLP) was used to remove Grey BL dye from aqueous solutions maintained at pH > 7 (Murugan 2010). However, the kinetics of the uptake was quite slow as it took 12h to reach equilibrium. Another attempt was made to use mango leaf as biosorbent for removal of Ni (II) from aqueous solutions (Banerjee 2017 ). The results showed that about 96.75% sorption could be achieved from Ni (II) solution of 10 mg/L concentration maintained at pH 5. The increase in concentration resulted in decreased uptake. The sorption reached equilibrium in about 1h. Mangifera indica can be effectively used as a biosorbent for uptake of Rhodamine B dye (Ibrahim 2019 ; Khan 2011). The sorption of various heavy metal ions by mango leaves (Mihretu 2019 ) showed that time needed to achieve equilibration was dependent on the initial metal ion concentrations. The optimum pH of the study was 6. The maximum sorption capacity for these ions of concentration of 100 mg/L was in the range of 210–230 mg/g using a sorbent dose of 0.4g/L. Mango leaf has also been used for removal of arsenic (III) (Kamsonlian 2012) at pH of 7 using a sorbent dosage of 7g/L. However, the studies were carried out for a very long time period (32h). The uptake of Cu and Zn ions by heat treated mango leaf samples were studied (Kaushal 2016 ; Ong 2013). In spite of the fact that there are many reports on the use of mango leaf or its derivatives as sorbents, it was of interest to carry out a systematic study to understand the various aspects of sorption. In this paper, the sorption of commonly available dyes and transition metal ions was carried using untreated and pretreated (chemical / thermal) mango leaves. Different aspects of sorption have been used with and without pretreatment for the removal of commonly available cationic dyes and also common cations. A detailed investigation of the possible mechanism has been evaluated. Simulation studies have been attempted to give an insight to the mechanism. To understand the selective binding of dye molecules and transition metal ions with the mango leaf, electronic structure calculations are carried out. The mango leaf has several active components such as quinone moiety, hydroxyl and carboxylic acid functional groups. Euxanthic acid (EA) is a small macromolecule which has all the essential active components of the mango leaf and thus we used EA as a model for mango leaf. 2.0 Experimental The mango leaves were collected from the western and southern parts of the country and named as Batch W and S respectively. The leaves obtained are washed well with water and dried. These dried samples are then treated differently prior to use. The untreated samples were used in different sizes ranging from 5cm in length to 100 microns. For treatment different processes were adopted. The acid treated samples mango leaves (ML-AW) was prepared by immersing in dilute acid (HCl) for two hours and then removed and washed several times with water. The water washed samples were then dried and stored in plastic bottles fit for further use. Fresh mango leaves were washed well and air dried. These samples were then heated at different temperatures ranging from 50 to 500°C. The heated samples were then labeled accordingly and used for sorption experiments immediately without storing. IR studies of the sorbents were carried out using Fourier Transform Infrared (FTIR) Spectroscopy (Bomem MB102) by using KBr pellets. The solutes chosen for the study were the cationic dyes namely Rhodamine B (RhB), Methylene blue (MB) and Malachite green (MG) and transition metal ions Cu (II) and Zn (II). The stock solutions of the dyes were prepared from the solid obtained from E.Merck without any further purification. The transition metal ions (Cu (II) and Zn (II)) were prepared by weighing their respective chloride salts (E.Merck) and diluted appropriately. The sorption studies were carried out in batch mode (Jayshree 2021). A fixed volume (10 mL) of solute solution (of known concentration and maintained at a particular pH) is equilibrated with accurately weighed amount of sorbent (pretreated differently) for a particular time period. Upon equilibration the amount of solute left behind in solution is determined using spectroscopic techniques. The concentration of dye left behind in solution was determined using a JASCO V 650 double beam spectrophotometer while ICP-AES was used for the determination of Cu(II) and Zn(II). The measurements are associated with error of 5%. This enables the calculation of the amount of solute taken up by the sorbent. The different experimental factors were varied and optimized to get maximum uptake. The studies were then extended to simulated sample solution containing a mixture of dye along with various cations and anions in tap water sample as described in earlier report (Khan 2019). The mixture solution maintained at pH 6, contained fixed concentrations of potassium di-hydrogen phosphate (50 mg/ L), calcium chloride (58 mg/ L), ferrous sulphate (500 mg/L), nickel sulphate (20 mg/L), magnesium sulphate (220 mg/L), ammonium chloride (1300 mg/ L), ferric chloride (17 mg/L), zinc chloride (4 mg/L), manganese chloride (7 mg/L), cobalt chloride (4 mg/L), sodium bicarbonate (5000 mg/L) and EDTA (300 mg/ L), and varying concentration levels of dye. The characterization studies of mango leaf sorbents were carried out to assess their utility for sorption of cationic species. The most important aspect which decides the applicability of the biosorbents is the surface charge. The point of zero charge of the sample was determined by the well-known solid addition method adopted for biosorbents (Akpotu 2023; Al-Maliky 2021). In this, a series of 100 mL of Erlenmeyer flasks with 45 mL 0.1M KNO 3 maintained at different pH values in the range of 1–12 was taken. The pH adjustments were carried out by addition of appropriate volumes of 0.1M nitric acid/sodium hydroxide and then made to 50mL using 0.1 M KNO 3 . After measuring the initial pH of the solution, accurately weighed approximately 0.1 g of biosorbent is added flask is securely capped and shaken manually. After equilibration for a period of 48 h, the final pH of the solution is measured. The difference between the initial and final pH values (ΔpH) is plotted against the initial pH and the point of intersection (ΔpH = 0) gives the point of zero charge of the sorbent. In addition to point of zero charge, surface area is another parameter which is very essential to understand the sorption. The surface area of the bio-sorbents was determined methylene blue absorption method (Xia 2019) using the simple relationship Specific surface area (SSA)= (Xm*N*A)/M In this, the maximum capacity obtained by Langmuir fitting of equilibrium data is substituted in the following relationship, wherein Xm is the monolayer capacity in mg/g, (from Langmuir calculation), N is Avagadro number (6.022x 10 23 ) and A is area / one molecule of MB which is 130 Ǻ 2 (1.3E-20 m 2 ) due to flat orientation and M is the mass of MB. (319.85 g/mol). Simulation studies were carried out for EA molecule along with Rhodamine B (RhB), Methylene Blue (MB) and Malachite green (MG) dye molecules and hexa-aqua Cu (II) and Zn(II) ions. The structures are optimized at the density functional theory level. The species are optimized with BP86 functional in conjunction with def2-SV(P) basis set for all atoms (Schäfer 1994; Becke 1988 ; Perdew 1986 ). DOCKER program in ORCA 6.0 electronic structure package has been used to identify the binding of three dye molecules and two metal ions with EA molecule (Neese 2022 ; Neese 2012 ). The molecular electrostatic potentials (MESP) and optimized structures are plotted with AVOGADRO and Chimera softwares (Hanwell 2012; Goddard 2007; Pettersen 2004). 3.0 Results and Discussion The preliminary sorption studies using batches W and S showed that the variation in uptake between the two batches was less than 10%. This suggested that the origin of the leaves was not crucial in this particular study. The removal of cationic dyes by mango leaves could be attributed to the presence of functional groups which could bind or exchange these dyes. It is reported that mango leaf powder contains cellulose (45.2%) and lignin (26.6%) as the major constituents (Khan 2019) while extractives (12.2%) and hemicelluloses (13.3%) and ash (2.7%) form the rest of composition. All these constituents contain functional OH groups which could be responsible for the uptake of cationic species. It is also seen that the organic components containing the functional groups are present in percentage levels while the pollutant used for study is in the ppm levels of concentration. The functional groups present in the mango leaf sorbent has been identified using IR (Fig. 1 (A)). The band at 3299 cm − 1 corresponds to O–H stretching of bonded hydroxyl groups present in glucosides and lignin moiety (Kumar 2021). Similarly, the peak around 1618 cm − 1 is assigned to bending of O–H group present in different constituents. The characteristic peak located at 2914 and 2855 cm − 1 correspond to the C–H bond in –CH 2 and methyl groups respectively. The band at 1726 cm − 1 corresponds to the stretching of the C = O group present in the unionized carboxylate structure / carboxylic acid / pectin ester. The peak positions at 1458 and 1370 cm − 1 related to the symmetric bending of –CH 3 groups. Signal observed at 1240 cm − 1 can be accounted for −SO 3 stretching in ML. The peak at 1065 cm − 1 corresponds to the C–O stretching of ether groups (Khan 2019; Pandey 2022). Peaks region lower than 800 cm − 1 may be related to N-containing bioligands, alkene, halo group, C–H bending and C–O–H twist (Kuppusamy 2016). In this region, bands due to metal-oxygen bond appear. The presence of OH and COOH make it possible for the mango leaf to take up cationic species. The peaks below 1000 cm − 1 are attributed to the presence of M-O bonds (Chongrak 1998; Abdulwasiu 2022). In this region, peaks due to Ca-O, Si-O (Khan 2011) and Ca-Si bonds (Khan 2019) are present. The chemical composition of mango leaf has been earlier reported (Kumar 2021). It is seen that there are substantial levels of zinc (0.0024–0.014%), sodium (0.003–0.23%), boron (0.0016–0.0042%), copper (0.0021–0.0029%), iron (0.0062–0.034%), manganese (0.0028–0.003%) and cadmium (0.015%). The chemical composition of mango leaf used as sorbent in this study was assessed using EDS analysis. It was seen that sorbent contained C(72.98 wt %), O (24.08 wt%), Si (2.62 wt %) and Ca (0.32 wt%). Therefore, it becomes essential to use the same batch of mango leaf sorbent as well as tap water sample for the entire study. The dye solution was made up using tap water to understand the sorption in real scenario. Prior to making the solution, analysis of tap water was mandatory. The values of concentrations of different species in tap water is Ca (9.7 ± 0.08mg/mL) > TOC (7.9 ± 0.11mg/mL) > Mg (3.6 ± 0.40mg/mL) > K (0.86 ± 0.08mg/mL) > Zn (0.31 ± 0.05 mg/mL) > Fe (0.11 ± 0.05 mg/mL). It was essential to know the cationic composition of the water as the nature and concentration of these ions may interfere with the sorption studies. The concentration of most of the commonly present ions is in the range of our study. The TOC analysis was crucial as this may interfere with metal ion sorption. If a fresh tap water sample was taken, it was analyzed and the variation was found to be less than 10%. The effects of diverse investigational factors have been evaluated and the results are represented in Fig. 2 . For this, 10mL of MB solution (2 mg/L of MB) in tap water, was equilibrated under various conditions to achieve maximum uptake. The size and weight of the sorbent is an important parameter as decreased size leads to increased surface for sorption. For this, untreated mango leaf samples S1, S4, S10 and S50 having size of 100, 400, 1000 and 5000 microns respectively were used for equilibration of 120 mins. The amount of sorbent needed in each case was studied systematically and results are shown in Fig. 2 (a). It is seen that as the sample size decreases from 5000 to 100 microns, there is an increase in the amount of dye sorbed. This could be attributed to the enhanced surface area at lower sizes of sorbent. An interesting observation was that for larger sample size, the amount of dye taken up increased with increase in the weight of sorbent but for smaller sizes this change was quite less. Thus it was possible to use lower sample weights of 100 micron samples. Hence for the samples S1, S4, S10 and S50, the amount needed to achieve maximum sorption was 0.05, 0.2, 2 and 2 gm respectively. Despite the fact that larger sized sorbent required higher sorbent dosage, it was thought of carrying out the complete studies with each of the sample using the appropriate weight. In order to see the effect of equilibration time and pH of initial solution on sorption efficiency, 10mL of 2 mg/L MB was used for the study. The time variation studies (Fig. 2 (b)) shows that amount taken up is higher as well as faster for S1 while it is quite lethargic for large samples (S10 and S50). Thus, maximum uptake is achieved in 30 mins for S1, 60 mins for S4 and S10 and 90 mins for S50. However, for uniform comparison further studies were carried out at 90 mins. Despite the fact that the studies were carried out in tap water medium, it was of interest to evaluate the effect of solution pH on sorption characteristics. The solution pH will have profound effect on the uptake efficiency as it can lead to changes in the sorbate structure or the sorbent surface. The variation in pH (Fig. 2 (c)) shows that maximum uptake occurs at pH of 6. This corroborates with our earlier results on dye uptake studies using various sorbents (Jayshree 2021). From earlier studies, it is understood that the two extreme ends of pH will alter the chemical structure of cationic dye thus hindering the uptake. It is reported that pKa of MB is 3.8 and at higher pH the species is cationic in nature (Liany 2014). But with further increase into the alkaline range, the non-ionic form begins to dominate (Liany 2014). This tends to decrease the sorption efficiency. However, the effect of pH on the present sorbent surface was evaluated. From the plot of ΔpH vs pH (Fig. 1 (C)), it is seen that the curve crosses at pH value of 5.4 which is the point of zero charge. This value is in agreement with the reported value of 5.6 (Dey 2022). Thus, it is clear that at lower pH, the surface of sorbent is positive which reduces the sorption of cationic dye. At pH greater than 5.4, the surface becomes negative making it suitable for sorption of cationic dyes. Hence, pH of 6 was found to be optimum. In order to understand the effect of size on sorption, the specific surface area of the sorbent is to be calculated. In general, for calculation of specific surface area for biosorbents, MB sorption method is preferred. This could be due to the fact that BET being a dry method can produce erroneous results due to non-uniform sorption / desorption of N2 due to the non-homogeneous structure of the sorbent (seen from SEM images in Fig. 1 (B)). Due to irregular structure, the chances of N2 sorption and desorption need not necessarily be uniform during leading to variations in the measured values. Additionally, the methylene blue sorption is a liquid phase method, which allows the loading of the dye over the entire available sites. For biosorbent material, surface area values obtained using BET and Methylene blue sorption show variations (Chongrak 1998). In water, MB has easy access to the sites due to formation of linkages and the water medium helps in the sorbate reaching to most of the sites. The presence of water medium ensures the opening of the porous structures (seen from SEM) leading to uptake of MB. However, in N 2 sorption, preconditioning could alter the sample structure which can affect the measured values. Hence no attempt was made to use the BET method in this particular study. Thus, reports making use of MB sorption as a tool to measure surface area of biosorbents suggest that the method is very easy and results obtained have greater precision (Abdulwasiu 2022; Reddy 2012; Kipling 2007 ). In certain reports, the maximum experimental capacity value is taken but in others, the value from Langmuir fitting is taken. The basis of this calculation is the dimensions of methylene blue. Methylene molecule has parallel pipe shape with dimensions of 1.7 nm × 0.76 nm × 0.33 nm and surface area covered by one molecule can be 1.30 × 10 − 18 , 6.6 × 10 − 19 or 2.47 × 10 − 19 m 2 / molecule. In all the earlier reports, 1.30 × 10 − 18 m 2 / molecule is taken for further calculations. In this study attempt has been made to use both these values to calculate the specific surface area. It is seen that the values obtained using Qmax was higher than that with qe by a maximum of 2 times. Hence, the computed value using experimental qe and Qmax follow the same trend. Thus, the values of surface area obtained from qe and Qmax (given in parenthesis) for the samples are in the order S1 (79.40 (115) m 2 /g:) > S4 (48.12 (62.3) m 2 /g) > S10 (18.10 (32.3) m 2 /g) > S50 (6.82 (14.0) m 2 /g) attributing to the increased sorption efficiency in the same order. It was seen that the uptake did not lead to any changes in the pH, which would be expected if ion exchange mechanism operated. Since the solution pH did not alter much, it became essential to understand the composition of the mango leaf sorbent. From EDS it is seen that Ca is present in sorbent and this can be replaced with the dye and make the solution alkaline. However, the solution left behind does not show any significant variation nor contains proportional quantities of Ca which is expected when ion exchange occurs. To get a possible explanation for the same, XRD characterization of the sorbent was carried out (Fig. 1 (C)). It was observed that the sample overall is amorphous as characterized by broad noisy humps. In addition to that there are discrete sharp peaks at 2θ values of 14.95º (maximum), 24.45º, 30.13º and 46.25º. In earlier reports, these peaks have been attributed to the presence of low crystalline cellulosic material arranged in ordered lattice and bonded with O-H group through strong secondary forces (Reddy 2012; Kipling 2007 ). However, from EDS, we know that both Si and Ca are present in reasonable amounts. So, it becomes essential to understand whether these contribute to the peaks. It is reported that silica microspheres tend to show a peak at 22º (Liang 2011). For pure CaO nanoparticles, peaks are obtained at 2θ values of 32º (Jalu 2021) and this is slightly different to the values in our study. This is probably due to the fact that CaO is not isolated but as a part of the organic group present in the mango leaf. Thus, the peaks of mango leaf sorbent is due to the presence of O-H, C 2 H 6 , C 6 H 5 OH, NH 3 , MgO 2 , Na-O, and Ca-O. The crystallinity index gives a good understanding of the structure (Park 2010). The average crystallite grain size computed from XRD pattern is 84 nm while the crystalline index is around 50%. Further characterization is done using Nuclear Magnetic Resonance (Fig. 1 (E)). In the high field region (0–3 ppm), presence of organic acids is confirmed. It is reported earlier that in addition to citric, malic, and succinic acids, shikimic and quinic acids dominate (Duarte 2005). In addition, aliphatic acid like Alanine is also present (Lizada 1993). The spin values at 0.89, 1.29, 1.55, and 2.21 ppm indicate saturated fatty acids like palmitic acid (Wu 1993). The signals in the region 1.20–1.32 ppm indicate presence of fucose and rhamnose (Tucker 1993 ). The mid filed of 3-5.5 ppm indicate the presence of indicates the presence of various types of sugars (Tjan 1974). In the low field region of 5.5–10 ppm, the prominent peak indicates shikmic acid and other acids and aldehyde. Thus, the presence of phenolic and acidic groups tends to be responsible for the uptake of MB. The optimized pH which resulted in maximum MB sorption is 6 (Fig. 2 (C)). However, in order to understand the sorption mechanism and kinetics, it becomes essential to study the time variation of the other cationic species before studying the concentration effect. These studies indicate that nearly complete sorption is achieved for all the species in 120 mins (Fig. 2 (B)). To obtain the concentration isotherm, further studies with other sorbates were carried out at pH 6 with equilibration for 120 mins and the appropriate amounts of the sorbents. The results are given in Fig. 3 . The experimental capacity values are in the order MG > Rh B > MB ≅Cu(II) > Zn(II) for all sorbent sizes. Since the MB data was fitted to compute the specific surface area, it becomes essential to fit these data to different equilibrium models. Different models were used to understand the sorption behavior of all the sorbates on the four types of sorbents. The equilibrium data were fitted to Langmuir, Freundlich, DR and Temkin models (Jayshree 2024 ). The studies reveal that MB sorption shows better fit to Langmuir model while sorption of RhB and MG shower better fit for Freundlich model for all the sorbents S1, S4, S10 and S50 based on the R 2 value ≥ 0.95. This indicates that only one molecule of MB is sorbed on definite, localized site without interfering with the adjacent sites. Further this model indicates that the amount sorbed does not affect the rate and once a monolayer is formed, saturation occurs. The value of b (Langmuir constant) can be related to the variation of the area and porosity of the sorbent and for MB sorption it is found to increase in the order for the four sorbents S4 (0.10) ~S1(0.09) > S10 (0.05) > S50 (0.03). An analogous trend was observed for both RhB and MG. The capacity values computed using this model is useful for comparing the efficiency of various sorbents. S1 has the highest capacity value for all the sorbates studied. The dimensionless constant, RL, is the Langmuir separation factor (Eq. 3) which can predict the favourability of sorption. In this study, the values of RL is in the region of 0–1 indicating the sorption to be favourable. The value is closer to 1 for MB sorption on S1 indicating sorption to be both favoured and as well as linear in nature. It is interesting to note that only the sorption using S1 can be fitted to the Langmuir model for the uptake of both Cu(II) and Zn (II) and the values indicate a favourable sorption. Freundlich isotherm model gives an idea of heterogeneity of sorbent surface. The values computed Kf (Freundlich sorption capacity (L mg/ g)) and n (sorption intensity) could give an understanding of the nature of sorption. For all the systems, it is seen that the value of 1/n is in the range 0–1 indicating favourable sorption. The increased sorption capacity for Cu as compared to Zn (Table 1 ) could be due to the interaction between the carboxylate groups within the sorbent (Bunting 1970 ). Even though the system was studied at room temperature, the energy associated with the process was evaluated using Dubinin-Radushkevich (D-R) isotherm model. Despite its original use for vapour sorption onto microporous solids, DR can be used to study the energetics of the sorption process. It is seen that for all the systems in the present study, the sorption followed DR (R 2 value > 0.98 for all the sorbents) and the free energy calculated is in the range of 8–16 kJ/ mol indicating the process to be chemisorption at room temperature. To understand the effect of interactions between sorbent and sorbate on the overall process, Temkin isotherm model is used. The results showed that both binding energy and heat of sorption increases with decrease in particle size indicating better interaction with the sorbent sites for sample S1. Table 1 Computed Parameters from equilibrium modelling S50 S10 S4 S1 S50 S10 S4 S1 Langmuir Freundlich MB qe 5.33 2.31 3.8 3.9 K f 0.16 0.4 1.8 2.4 R L 0.75 0.69 0.92 0.96 1/n 0.88 0.89 0.75 0.82 R 2 0.985 0.964 0.969 0.975 R 2 0.976 0.981 0.956 0.951 RhB qe 17.45 4.8 2.52 7.5 K f 0.31 1.01 4.37 6.04 R L 0.84 0.69 0.90 0.92 1/n 0.90 0.86 0.73 0.79 R 2 0.981 0.994 0.994 .991 R 2 0.996 0.99 0.949 0.94 MG qe 20.69 0.1 38.2 32.3 K f 0.49 1.61 8.75 11.7 R L 0.70 0.76 0.95 0.91 1/n 0.88 0.85 0.76 0.80 R 2 0.98 0.98 0.99 .963 R 2 0.99 0.99 0.99 0.99 Cu qe Does not follow 3.3 Does not follow R L 0.84 R 2 0.97 Zn qe Does not follow 2.6 R L 0.78 R 2 0.943 The kinetics depict the advancement of uptake with time until saturation occurs due to equilibrium. The modelling of this data could give an understanding of the mechanism. The data has been subjected to both rate as well as mechanism equations. The best-fit model was selected based on the values of linear regression correlation coefficient, R 2 . The well known kinetic models of Lagergren pseudo first-order and pseudo second-order kinetic model developed by Ho and McKay have been used (Jayshree 2024 ). The parameters computed from the kinetic models are given in Table 2 . Pseudo first-order and pseudo second-order kinetic models are used to describe the rate of sorption and understand the mechanism. Lagergren pseudo first-order model assumes that sorption rate is proportional to the difference between maximum adsorption capacity and the amount of adsorbate adsorbed at any given time. It indicates a diffusion-controlled process. Pseudo-Second-Order Model assumes that chemisorption is the rate determining step suggesting the interactions between sorbate and sorbent. Table 2 Computed Parameters from kinetic modelling Psuedo First order Psuedo Second order S50 S10 S4 S1 S50 S10 S4 S1 MB q e 2.03 2.86 2.54 1.95 q e 2.59 3.46 3.85 4.76 k1 0.01 0.01 0.01 0.01 k2 1.14 2.73 9.48 16.55 R 2 0.991 0.99 0.92 0.975 R 2 0.99 1.00 1.00 0.99 RhB q e 0.67 2.38 8.01 12.21 q e 0.99 3.24 11.66 15.01 k1 0.00 0.01 0.00 0.01 k2 0.02 0.60 38.15 103.68 R 2 0.97 0.96 0.96 0.94 R 2 0.98 0.97 0.94 0.96 MG q e 1.11 3.46 31.1 20.36 q e 1.67 6.53 24.01 29.97 k1 0.01 0.01 0.01 0.002 k2 0.02 1.89 270.71 498.4 R 2 0.99 0.96 0.95 0.97 R 2 1.00 0.95 0.98 0.96 Cu q e 0.13 0.47 0.61 1.08 q e 0.17 0.65 0.89 1.99 k1 0.01 0.01 0.01 0.01 k2 0.01 0.01 0.02 0.07 R 2 0.98 0.98 0.99 0.99 R 2 0.99 0.96 0.95 0.92 Zn q e 0.03 0.15 0.30 0.81 q e 0.08 0.41 0.56 1.62 k1 0.01 0.01 0.01 0.01 k2 0.01 0.01 0.01 0.04 R 2 0.94 0.97 0.96 0.98 R 2 0.97 0.98 0.95 0.92 In the present study, it is interesting to note that the model followed depends on nature of sorbate. It is seen that the sorption of all the sorbate follow the pseudo first order reaction in the entire time period for all the sizes of the sorbent. However, only Methylene blue obeys pseudo second order model in the entire time period of study. In case of RhB and Cu sorption, the sorption data after 20 mins of start follows the pseudo second order model. It is further noted that for MG and Zn, the pseudo second order model is applicable after 50 mins of sorption. Therefore as no one particular model can be used to fit the sorption data it is very clear that the biosorption in this study is quite complex and there are many rate determining steps. The pseudo first order kinetics assumes that the adsorption rate is proportional to the number of vacant sites on the adsorbent surface, while the pseudo second order kinetics assumes that the adsorption rate is proportional to the square of the number of vacant sites (Jayshree 2024 ). Moreover, in this case the qe values predicted are also much greater than the experimentally obtained values. This indicates that sorption does not occur only on the vacant sites of the sorbent. When experimental results are lower than those predicted by kinetic models, it suggests that the model is not fit to the reaction due to the sorption being very complex. Moreover, the dyes sorbed on to the sorbent may tend to interact with its immediate neighbours leading to the formation of dimers and trimers which may lead to multilayer sorption. In order to validate the above results, Elovich model was used. It was originally used to assess the chemisorption behaviour of gases on solid surface. The model assumes that the rate of adsorption of solute decreases exponentially as the amount of adsorbed solute increase. In this model, constant 'a' represents the initial adsorption rate of a substance onto a surface, while 'b' represents the desorption constant. The higher value of b indicates high activation energy of chemisorption making it difficult to desorb. From Table 2 , it is seen that values of a and b are higher as size decreases for all sorbates. However, the values are maximum for Rhodamine B indicating less tendency to desorb. This could be due to the fact that the sorbed rhodamine B could form dimers and trimers making it difficult to desorb. The uptake involves different stages. The initial state involves the presence of active sites of sorbent. The sorbate molecules tend to diffuse through the liquid film surrounding the sorbate particle (Film diffusion). The sorbate then tends to get sorbed onto sorbent site. The sorbed sorbates tends to diffuse through the pores of sorbent (intraparticle diffusion) and then finally reaches equilibrium (rates of sorption and desorption are equal). To get a better understanding, the kinetic data were subjected to mechanism models namely Webber Morris Intraparticle diffusion and Boyd diffusion models (Jayshree 2024 ) are used to further assess whether sorption follows film or intraparticle diffusion. The Boyd model, assuming infinite bath conditions (near constant value of sorbate concentration) and a linear equilibrium relationship between the sorbate concentrations in the solution and sorbent phase, describes the rate of diffusion in liquid. According to this model, external film diffusion is the rate-limiting or determining step in sorption. The major assumptions of this model may not be applicable in many systems and in such cases, the model is not applicable. The parameters calculated from Boyd’s Model is shown in Table 3 for the data obtained in this study. It is seen that Boyd’s model was applicable to all the systems from 10 mins after start of equilibration till 60 mins. The linearity as given by R 2 > 0.95 indicate that external film diffusion operates within the time period. It is seen that for the same sorbate, value of I is higher for S1 which has the least particle size. When film diffusion is rate-controlling, a smaller sorbent particle with larger surface area and thinner diffusion path shows higher diffusion rate. This is indicated by higher I indicating faster kinetics. The value of B as obtained from the slop is used to calculate the diffusion coefficient (Table 3 ). It is seen that with decrease in particle size, the diffusion coefficient increases indicating higher diffusion rate. From Table 3 it is seen that the value of intercept in Boyd’s model is different for the different ions. It is seen that the values for transition metal ions is higher than that of cationic dyes. Boyd’s model can help to understand whether the diffusion through the liquid film surrounding the sorbent or the pores is rate determining step. The value of intercept can help in understanding the mechanism. If the linear plot passes through the origin (intercept value is zero), then mechanism is internal pore diffusion. However, if the plot does not pass through intercept, then this value becomes an important parameter to understand the process. Higher value of intercept indicates higher resistance to mass transfer due to film diffusion. Thus, it is seen that Cu and Zn ions show the least resistance to mass transfer due to film diffusion as compared to the dyes studied. The mechanism can be further evaluated. The plot of Bt vs t being linear if crosses the origin indicates mechanism to be internal pore diffusion. In the present study, it is seen that only for Cu and Zn, internal pore diffusion is visible for all the sizes of the samples. The pore size of sorbent is about 20–50 nm. Therefore all the sorbates should be possible to enter the pores. However, it is seen that pore diffusion is not an important step for the sorption of the dyes. This indicates that the dye cations may tend to form dimers and trimers which lead to bulky structure thus restricting the internal pore diffusion mechanism. The migration of ions from bulk to the liquid film surrounding the sorbent particle is the first step involved. A diffusion barrier created, can play a major role in the rate determining step. The external diffusion regulates the transfer of solute from bulk solution to the liquid film around the sorbent particle while internal diffusion controls the transfer to pores. The movement of sorbates to external surface of sorbent (external or film diffusion) or to the pores (internal diffusion) can decide the rate of reaction. The values of diffusion coefficient give further understanding of the process. It is reported that diffusion coefficient for film diffusion is in the range of 1E-6 to 1E-8 cm 2 /s and that for pore diffusion is within the range of 1E-11 to 1E-13 cm 2 /s (Jayshree 2021). From the table, it is seen that for all the systems in this study, the value is in the range of 1E-6 to 1E-8 cm 2 /s indicating film diffusion-controlled process. Webber Morris model can further assess the nature of rate determining step. In this the plot of qt vs t gives the value of intraparticle diffusion rate constant from slope and intercept I gives an idea of thickness of boundary layer. Theoretically a single plot is expected. However, due to the different mechanisms, it may not be a single plot. Different linear portions are attributed to different mechanisms. The first linear portion defines the mechanism for film diffusion or external mass transfer process. The second portion defines the pore diffusion mechanism while the third linear portion indicates adsorption-desorption equilibrium. Generally, the last stage is not the rate determining step as it is fast. The time durations of each of these processes can give further clue about the mechanism. For S50, there is a single plot which indicates that sorption is controlled by film diffusion mechanism. For S10, plots corresponding to film diffusion and adsorption-desorption are seen for all the sorbates. For S4, MB and RhB sorption show three distinct linear regions while MG, Cu and Zn show two regions confirming film diffusion mechanism. For MB, the first two linear regions are in the time period of 2–40 min and 40–60 min respectively. Thus the ratio of film diffusion to pore diffusion is 2:1 indicating film diffusion to be dominant but present along with pore diffusion. also contributes. A similar result is obtained for RhB sorption onto S4. For S1, MB sorption shows first two distinct portions in time period of 0–40 and 40–60 (ratio 2:1) indicating film diffusion to be dominant. A similar mechanism is evident for RhB and Zn. For MG and Cu, there are only two peaks indicating the presence of film diffusion mechanism being dominant. These results are in agreement with the results of Boyd’s model. It is to be mentioned that the decrease in particle size leads to complexity in the sorption mechanism. Table 3 Parameters computed from Boyd and Webber Morris models Boyd’s Model Webber Morris S50 S10 S4 S1 S50 S10 S4 S1 MB I 0.29 0.36 0.38 0.44 k 1 0.27 0.42 0.98 1.98 D(E-8) 4 4 8 20 C 1 0.05 0.11 0.46 0.52 R 2 0.99 0.98 0.99 0.96 k 2 - 0.02 0.38 0.09 C 2 - 2.9 2.35 3.53 k 3 - - 0.5 0.02 C 3 - - 1.9 4.1 RhB I 0.30 0.38 0.42 0.51 k 1 0.06 0.31 1.3 2.9 D(E-8) 6 9 20 41 C 1 0.04 0.29 0.5 0.3 R 2 0.96 0.95 0.98 0.97 k 2 - 0.01 3.4 1.1 C 2 - 2.1 2 3.1 k 3 - 0.01 0.01 C 3 - 7.6 9.4 MG I 0.33 0.40 0.46 0.52 k 1 0.11 0.48 1.88 2.3 D(E-8) 80 100 200 250 C 1 0.02 0.94 1.01 1.22 R 2 0.97 0.98 0.96 0.98 k 2 - 0.01 0.02 0.02 C 2 - 3.3 2.58 1.88 k 3 - - - - C 3 - - - - Cu I 0.15 0.21 0.25 0.28 k 1 0.01 0.07 0.1 0.11 D(E-8) 100 150 198 215 C 1 0.02 0.05 0.05 0.1 R 2 0.96 0.98 0.97 0.96 k 2 - 0.01 0.2 0.59 C 2 - 0.46 0.48 0.02 k 3 - 0.57 C 3 - 0.93 Zn I 0.22 0.28 0.30 0.32 k 1 0.01 0.02 0.03 0.1 D(E-8) 100 180 225 275 C 1 0.02 0.02 0.01 0.1 R 2 0.99 0.98 0.97 0.97 k 2 - 0.01 0.01 0.22 C 2 - 0.2 0.24 0.87 k 3 - - - 0.01 C 3 - 0.79 The kinetic data was subjected to diffusion chemisorption models (Jayshree 2024 ). It was seen that the model is not applicable throughout the first 60 mins of sorption process. Thus, it confirms that the sorption process obtained in the present study is dominated by film diffusion as given by both Boyd and Webber Morris model. It is also clear that chemisorption does not control the overall sorption rate. Thus, it is clear that pore diffusion is very rapid as can be seen from the porous structure of the sorbent (SEM image in Fig. 1 E). Furthermore, Cu tends to diffuse to the pores and bind with groups present and therefore monolayer sorption is not followed (Langmuir model not followed). Regeneration is an important aspect of sorption studies. Even though the sorbent used in the present study was a biowaste, it was of interest to evaluate the possibility of regeneration and reuse of the sorbent. The regenerations studies were carried out with MB sorption on sample S1. The first approach was to use dilute acid as the regenerant. It was seen that the removal was more than 90% for all the sorbates. However subsequent reuse showed a decreased uptake efficiency. Since the sorbent is a biowaste, thermal treatment was thought of as an attractive alternative. The idea behind this was that even if the sorbent is destroyed along with the dye, the waste is reduced. Thus, the samples were heated at 50 o C and tested. It was seen that regenerated sorbent showed an increase in uptake efficiency In order to understand the changes, a systematic study was carried out by pretreating sample S1 by acid as well as heat treatment in temperature range of 50 o C to 500 o C. The uptake efficiency with respect to methylene blue was studied to understand the changes. The results are shown in Fig. 4 (A). It is seen that uptake efficiency increased with temperature increase up to 100 o C and there after decrease was observed. To get an insight of these changes, it was thought pertinent to characterize the pretreated samples. The pretreated samples were analysed using various characterization techniques. The scanned images of samples Pure, AW (Acid washed), ML-50 (heated at 50 o C), ML-100 (heated at 100 o C) and ML-200 (heated at 200 o C). The preheating beyond 250 made the samples very dark and image was black. Also beyond preheating at 200 o C, the samples when equilibrated with dyes imparted additional blackish brown colour to the dye solution making them unsuitable for use as sorbents. The images of these samples without and with MB are shown in Fig. 4 (B). It is seen that with increase in temperature, ML tend to become brown and finally becomes black. Heating at 500 o C gives black powder. The XRD of these samples are given in Fig. 4 (C). Pure ML sample shows XRD pattern with peaks at 2θ values of 14.95 o 24.45, 30.13 and 46.25 corresponding to cellulosic material with regular lattices network due to bonding of O–H groups (Khan 2019). In acid treated samples, XRD pattern shows sharpest peak at 15.07 indicating that the O-H peak of Pure ML is altered. This is expected as the acid attacks the O-H group converting it into a different moiety. The peak at 30.13 is also shifted to 31.12 indicating some bond cleavage due to acid treatment and changes in the cellulose network. However new peaks are formed at 22.13 and 55.53. Due to acid treatment the peaks due to MgO, NaO and CaO are clearly visible (Dey 2022). In the temperature variation study, it is seen that at 150 o C, the structure becomes more crystalline in nature while it tends to amorphous nature when heated at 200 o C. ML-50 gave three sharp peaks at 15.05, 27.24 and 30.15 corresponding to O-H and C2H6. The peak at 42 degrees corresponds to NH2. For ML-100 gave sharp peaks at 15.10, 24.47, 30.129, 38.55 correspond to cellulose network. The peak at 38.55 corresponds to C2H6 group. ML-150 gave peaks at 10.153, 13.225,15.21, 15.47, 17.68, 25.89 indicating the loss of phenolic groups present. Proton NMR Spectroscopy was also used to understand the changes in structure. The results are given in Fig. 4 (D). It is seen that in the high field region (0–3.0 ppm) shows signals due to presence of various acids like citric, malic, succinic acids in predominance along with lower amounts of lactic, propionic, and 3-hydroxybutyric acids. The presence of lipids is confirmed by the peaks at 0.89, 1.29, 1.55, and 2.21 ppm, which may arise from one or more saturated fatty acids (e.g. palmitic acid. The mid field region (3.0–5.5 ppm) is dominated by intense and overlapped signals of the major sugars. In low field region, the prominent signal is due to shikimic acid. It is seen that NMR spectra changes with pretreatment indicating the possible structural changes of Mangiferin into isomanguiferolic acid, Ambolic acid etc (Reddy 2012; Escobedo 2012; Neiss 1999; Belton 1997). Thus, it is evident that the pretreatment by heating tends to change the original dominant species in mango leaf. FTIR spectroscopy was used to further understand the changes with pretreatment. The results are given in Fig. 4 (E). FTIR is useful to identify the functional groups based on their vibrations (Adelaja 2019; Kuppuswamy 2016; Peydayesh 2015 ; Gupta 2012; Reddy 2012; Uddin 2009). The IR spectra of pristine Mango leaf showed characteristic peaks at 3299, 2913.9, 1618 and 1065 cm − 1 with small peaks at 2855, 1726 cm-1 and multiple small peaks between 1458 − 1240 cm-1 and 800 − 640 cm-1. The peak at 3299 cm-1 was due to the O-H group stretching due to intra and intermolecular hydrogen bonding of alcohols or phenols present in lignin, cellulose, pectin, and hemicelluloses (Adelaja 2019). The peak at 2855 cm − 1 corresponds to C–H stretching of methyl and methylene groups while the peak at 1618 cm-1 is attributed to the carbonyl group (C = O) stretching in the carboxylate structure or carboxylic acid. The stretching of C = O of unionized carboxylic acid is reflected in the peak at 1726 cm-1. The peak at 1065 cm-1 corresponds to the C-N stretching of aliphatic amines while peak around 1300 cm-1 corresponds to C-N stretching of aromatic amines or symmetric bending of CH3 group. The peak near 800 cm-1 indicated the presence of = C—H bending of alkenes. In ML-AW, the major peaks corresponding to the O-H group, C–H stretching of methyl and methylene groups and carbonyl group (C = O) stretching are not altered much. The acid treatment is found to enhance the peaks at 2855 cm-1 (C–H stretching of methyl and methylene groups) and 1726 cm-1 (stretching of C = O of unionized carboxylic acid). Thus, the C = O is affected by acid treatment. Heat treatment shows marked changes in the spectra. The peak at 3299 cm-1 is found to decrease in intensity with increase in temperature of heating. This indicates that heating removes the water molecules adsorbed on the surface and also within the structure. Similar trend is observed for the peak at 2855 cm − 1 (corresponds to C–H stretching of methyl and methylene groups) indicating the cleavage of C-H bonds. The peak at 1618 cm-1 (attributed to the carbonyl group (C = O) stretching in the carboxylate structure or carboxylic acid) is also found to show decreased intensity indicating the possible thermal cleavage of this group. It is interesting to note that the peaks at 1726 cm-1 (stretching of C = O of unionized carboxylic acid) and 1065 cm-1 (corresponds to the C-N stretching of aliphatic amines) increases in intensity with temperature indicating the thermal ionization of these groups. In order to understand the changes, SEM and EDS analysis were carried out. The SEM images (Fig. 4 (E)) showed a change in the microstructure with the pretreatment. Acid treatment resulted in the loss of the porous structure while heating till 200 o C resulted in a more homogeneous structure with well-defined pores making it more suited for uptake studies. The results of EDS given in Table 3 give a further understanding of the chemical changes upon pretreatment. From EDS results (table along with SEM images) it is seen that S1 has got reasonable amounts of C (72.98 wt. %), O (24.08 wt. %), followed by lower amounts of Si (2.62 wt. %) and small amount of Ca (0.32 wt.%). It is seen that upon acid treatment and heating at 100 and 200 o C, the calcium is removed. Furthermore, heating at higher temperatures tends to increase the C and O content while Si content is reduced. Thus, it is clear that there are structural changes upon heating. Moreover, the decreased Ca levels result in increased uptake as the sites are available and the structure is more open for uptake 4.0 Simulation Studies In order to understand the mechanism involved in the biosorption, simulation studies were carried out. This has been achieved by density functional theory (state of the complexes within DFT) calculations. The hydrated metal ions, orthophenanthroline and their complexes are scrutinized using DFT calculations. All structures are optimized with BP86 functional in conjunction with def2-TZVP basis set (Niesse 2022, Niesse 2012; Hanwell 2012; Goddard 2007; Schäfer 1994; Schäfer 1992; Becke 1988 ; Perdew 1986 ). The importance of weak interactions is recognized using Grimme’s D3-BJ corrections to the functional. The results are shown in Fig. 5 . The computed MESP of EA, RB, MB and MG is shown in Fig. 5 (a). The blue and red color codes indicate moieties with positively and negatively charged lobes. Among the three dye molecules, MG has the largest negative potential and can bind strongly with EA which is largely having positive potential. The same can be confirmed with distances among the three pairs, EA:RB, EA:MB, EA:MG, where MG has the shortest distance with EA as shown in Fig. 5 (b). Even though the dyes studied are cationic in nature, it is interesting to note that MG exhibits a negative potential due to the presence of electron-donating groups around its three conjugated phenyl rings, as well as in the central ring. These groups enhance electron density and promote delocalization of negative charge, making the molecule more electronegative. Conversely, EA contains electron-withdrawing hydroxyl (-OH) groups, which reduce electron density and create an electropositive character. Due to the large flexible nature of EA and the dye molecules, several possible stacked structures are possible. To identify the most probable structure, we have used the DOCKER algorithm. The minimum energy structures of the three dye molecules along with Cu2 + and Zn2 + ions are shown in Fig. 5 (b). The binding energies (kcal/mol) computed for the five systems follows the order of EA:MG (-312.26) > EA:MB (-311.39) > EA:RhB (-307.56) > EA: Cu(H2O) 6 2+ (-98.80) > EA: Zn(H2O) 6 2+ (-80.90). This order amongst the dyes can be attributed to the presence of several hydrogen bonding and π-π interactions. However, for the two transition metal ions, it is observed that Cu 2+ ion binds stronger than Zn 2+ ion. Mulliken charge analysis reveals net charges of 0.509 and 0.796 on Cu 2+ and Zn 2+ respectively, indicating a greater charge transfer from Cu 2+ to the EA ligand compared to Zn 2+ . Due to its higher charge-to-radius ratio, Cu²⁺ behaves as a stronger hard Lewis acid compared to Zn²⁺. This results in a stronger interaction with EA, driven by the principle of hard-hard acid-base interactions. Additionally, due to the Jahn-teller nature of Cu 2+ ion, there is a redistribution of electronic density within the molecule. This distortion influences molecular polarizability, further reinforcing the strong interaction between Cu 2+ and the EA whereas in the case of Zn 2+ ion, this interaction is rather weak leading to the least binding energy. The same can be confirmed with the DFT optimized distances between EA and M 2+ (H 2 O) 6 in Fig. 5 (b). The binding energies are significantly lower for ions compared to dye molecules. This difference arises from the presence of hexa-coordinated water molecules surrounding the ions, which mediate the interaction with EA ligand. In contrast, dye molecules engage in direct interactions with EA, leading to stronger binding affinities. The computed binding affinities are corroborated with the experimentally obtained uptake capacity values. Conclusions The present study utilizes waste mango leaf as possible sorbent for cationic organic dyes as well as cations in tap water medium. The sorbents were characterized with different techniques to understand the sorption process. The equilibrium and kinetic data obtained were subjected to modelling which gives an deeper insight of the mechanism involved. Pretreatment of the sorbents resulted in a marked changes in the uptake efficiency. The probable reason for this change was understood using characterization techniques. To get a better understanding of the overall process, simulation studies were carried out. The binding energies of the solute with a particular component of the sorbet was calculated. The order was found to match the order of capacity values for solutes. Declarations Acknowledgements: The authors thank Head ACD and colleagues in BARC for the support during this work. Funding: No special funding was obtained for this work Authors and Affiliations Analytical Chemistry Division, Bhabha Atomic Research Centre, Mumbai – 400 085. Jayshree Ramkumar, S.Chandramouleeswaran, Naman K. Bharti Chemistry Division, Bhabha Atomic Research Centre, Mumbai – 400 085 Mahesh Sundararajan Homi Bhabha National Institute, Anushaktinagar, Mumbai – 400 094. Jayshree Ramkumar, Mahesh Sundararajan, Naman K. Bharti Isotope and Rad. Application Division, Bhabha Atomic Research Centre, Mumbai – 400 085 Bhumika Kumari K.J. Somaiya College of Science and Commerce VidyaVihar, Mumbai-400077 Alan John Mathew Authors’ Contributions : All authors whose names appear on the submission 1) made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work; 2) drafted the work or revised it critically for important intellectual content; 3) approved the version to be published; and 4) agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Jayshree Ramkumar - Conceptualization, Data Analysis, Writing the original draft Bhumika Kumari – Data Acquisition S.Chandramouleeswaran - Data Acquisition Alan John Mathew - Data Acquisition Naman K. Bharti and Mahesh Sundararajan– Theoretical simulations and writing of this part Corresponding author – Jayshree Ramkumar and Mahesh Sundararajan (for theoretical simulations) Ethical Approval: The submitted work should be original. The manuscript is not submitted to more any other journal for simultaneous consideration. Consent to Participate: NA Consent to Publish: NA Competing Interests : The authors have no competing interests to declare that are relevant to the content of this article. Data Availability Statement : All data generated or analysed during this study are included in this published article and its supplementary information files. References Abdulwasiu OS, Emmanuel IA, Hajara Y (2022) Application of Methylene Blue Adsorption Technique in the Determination of Specific Surface Area of Termite Feathers. 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J Chem Phys 100:5829–5835 Tjan SB, Voragen AGJ, Pilnik W (1974) Analysis of some partly and fully esterified oligogalactopyranuronic acids by p.m.r. spectrometry at 220 MHz. Carbohydr Res 34:15–23 Torres E (2020) Biosorption: A Review of the Latest Advances. Processes 8:1584 Tucker GA (1993) Introduction. Biochemistry of Fruit Ripening Eds Seymour GB, Taylor JE and Tucker GA. Chapman & Hall, London, pp 1–51 Uddin MT, Rukanuzzaman M, Khan MMR, Islam MA (2009) Adsorption of methylene blue from aqueous solution by jackfruit (Artocarpus heteropyllus) leaf powder: a fixed-bed column study. J Environ Manag 90:3443–3450 Wu JS, Chen H, Fang T, Chen S C.S. and, Shaw PE (1993) 620–655. Auburnadale: Agscience Xia Y, Yao Q, Zhang W, Zhang Y, Zhao M (2019) Comparative adsorption of methylene blue by magnetic baker’s yeast and EDTAD-modified magnetic baker’s yeast: Equilibrium and kinetic study. Arab J Chem 12:2448–2456 Yaashikaa PR, Senthil Kumar P, Saravanan A, Dai-Viet V N (2021) Advances in biosorbents for removal of environmental pollutants: A review on pretreatment, removal mechanism and future outlook. J Haz Mater 420:126596 Supplementary Files DataAvailibility20260209.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8729755","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593320192,"identity":"466a9465-d2bd-4998-859b-7fb5296ea8df","order_by":0,"name":"Bhumika Kumari","email":"","orcid":"","institution":"Homi Bhabha National Institute","correspondingAuthor":false,"prefix":"","firstName":"Bhumika","middleName":"","lastName":"Kumari","suffix":""},{"id":593320193,"identity":"1c302b58-4cc2-4ad0-9a63-7e7186a94d35","order_by":1,"name":"jayshree ramkumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYBACxgY2xscIrgFxWpiNSdPCwMDGJk2aw5hnH0urLqi5J8/Av/iYBEPBHSIc1pd27PaMY8WGDRLP0iQYDJ4RoaWHve02D1tCAoPEGWMDBoPDxGkp5vlHmha2Y8y8bUAt/D2GD4jVkiw9sy/BsE2CLfFBAjFaDHvYDD8XfEuQ5+c/fODAhz/EaGmAMtgkEhgYEghrYGCQh7P4DxCjfhSMglEwCkYiAAD28DKenfXQngAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3187-7759","institution":"Bhabha Atomic Research Centre","correspondingAuthor":true,"prefix":"","firstName":"jayshree","middleName":"","lastName":"ramkumar","suffix":""},{"id":593320194,"identity":"2759422c-9fa8-4a6d-b0bc-a08aed65f2c2","order_by":2,"name":"Sankaran Chandramouleeswaran","email":"","orcid":"","institution":"Bhabha Atomic Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Sankaran","middleName":"","lastName":"Chandramouleeswaran","suffix":""},{"id":593320195,"identity":"641413bf-5af0-44b8-a2bc-64aae97588b7","order_by":3,"name":"Alan John Mathew","email":"","orcid":"","institution":"Somaiya Vidyavihar","correspondingAuthor":false,"prefix":"","firstName":"Alan","middleName":"John","lastName":"Mathew","suffix":""},{"id":593320196,"identity":"87f2388d-2d69-4cde-819a-b61b2377a237","order_by":4,"name":"Naman Kumar Bharti","email":"","orcid":"","institution":"Homi Bhabha National Institute","correspondingAuthor":false,"prefix":"","firstName":"Naman","middleName":"Kumar","lastName":"Bharti","suffix":""},{"id":593320197,"identity":"32497ad4-ee70-4d87-88eb-ce115c333617","order_by":5,"name":"Mahesh Sundararajan","email":"","orcid":"","institution":"Bhabha Atomic Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Mahesh","middleName":"","lastName":"Sundararajan","suffix":""}],"badges":[],"createdAt":"2026-01-29 09:52:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8729755/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8729755/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103504081,"identity":"767c2cec-38e4-475b-8f38-b886bbc9f679","added_by":"auto","created_at":"2026-02-26 13:16:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":191189,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCharacterization of ML\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8729755/v1/acb0e1b27f269e6bafc4e9e3.png"},{"id":103075014,"identity":"8ee46cf6-511c-45f9-b464-40d4113aca37","added_by":"auto","created_at":"2026-02-20 13:22:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153425,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEffect of experimental parameters on MB sorption using sorbents of varying sizes (\u003c/em\u003eO\u003cem\u003e S1, \u003c/em\u003e▲\u003cem\u003eS4\u003c/em\u003e5\u003cem\u003eS10 and \u003c/em\u003e•\u003cem\u003e S50)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8729755/v1/beeab29e33fec64fd155ee2d.png"},{"id":103075015,"identity":"04dd55d4-ba69-44da-b338-28e7c78c9f8b","added_by":"auto","created_at":"2026-02-20 13:22:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84975,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEffect of initial sorbate concentration on the uptake \u003c/em\u003e(O S1, ▲ S4,5S10 and • S50\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8729755/v1/9ab9d98165998e23d13affc8.png"},{"id":103504197,"identity":"795c8499-796f-4475-968f-f1b08ae78c13","added_by":"auto","created_at":"2026-02-26 13:18:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":322677,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eResults of studies using pretreated sorbents (a) Effect of pretreatment protocols, (b) Scanned images, (c) XRD Spectra (d) NMR Spectra (e) IR (f) SEM\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8729755/v1/be5b6b9da6e6e45438ccbf42.png"},{"id":103075018,"identity":"9151b562-2d3e-46c8-9f16-8a1495ebb16e","added_by":"auto","created_at":"2026-02-20 13:22:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":302888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eResults of simulation studies\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8729755/v1/5f05d7f9e5f9498cf449ad01.png"},{"id":104781805,"identity":"7ff6b859-18a2-4ade-ab6c-6efecc88915d","added_by":"auto","created_at":"2026-03-17 07:56:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2083218,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8729755/v1/f8abd548-190b-4e18-9f94-306c7f0471ec.pdf"},{"id":103075019,"identity":"d62c28f9-44d3-43f6-9b1b-60e7d04b3e41","added_by":"auto","created_at":"2026-02-20 13:22:59","extension":"doc","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":96256,"visible":true,"origin":"","legend":"","description":"","filename":"DataAvailibility20260209.doc","url":"https://assets-eu.researchsquare.com/files/rs-8729755/v1/dbad88f93999c0389e64e65b.doc"}],"financialInterests":"","formattedTitle":"Biosorption behaviour of Mangifera indica biowaste as function of Pretreatment","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe need for clean water is becoming a constant requirement, which encourages the use of various techniques to remove different species from water. The various species present, as contaminant may become pollutant based on the concentration in water. Hence, the treatment becomes important not only health reasons but also from aesthetic point of view. The presence of certain contaminants proves to be visually perturbing when present in water media. Irrespective of the reason, it becomes crucial to remove different species from water bodies and many techniques are available. At times, a combination of techniques proves to be very effective. Adsorption, better known, as sorption is very attractive as it is easy and allows the use of various materials as sorbents (Jayshree \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In general, synthetic materials with specific functional groups are normally used. Borosilicate glass was used as room temperature sorbents for the removal of dyes and metal ions (Chandramouleeswaran 2011). Nanomaterials with increased surface tend to show enhanced sorption efficiency. Our earlier works demonstrate the use of bare and modified nano sorbents for removal of toxic metal ions as well as dyes (Jayshree 2021). Due to the ongoing research for newer materials and also the concern towards the nature of sorbents itself, use of benign materials becomes very popular. The use of biowaste (Torres \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) is attractive as it is aplenty thus making the processes cost effective. The process is facilitated by the presence of different functional groups (Yashikaa 2021).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn this present study, the sorption efficiency of mango leaves studied with respect to various species. The Indian mango tree, known as Mangifera indica, is an evergreen plant which belongs to the family anacardiaceae. The annual output of mango in India is around 5\u0026nbsp;million tons which generates 2\u0026nbsp;million ton of mango leaves. These leaves are not consumed and are discarded or set on fire. Thus, the biowaste generated interest in various researchers who have used these for removal of various toxic species. Mango leaf powder particle (MLP) was used to remove Grey BL dye from aqueous solutions maintained at pH\u0026thinsp;\u0026gt;\u0026thinsp;7 (Murugan 2010). However, the kinetics of the uptake was quite slow as it took 12h to reach equilibrium. Another attempt was made to use mango leaf as biosorbent for removal of Ni (II) from aqueous solutions (Banerjee \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The results showed that about 96.75% sorption could be achieved from Ni (II) solution of 10 mg/L concentration maintained at pH 5. The increase in concentration resulted in decreased uptake. The sorption reached equilibrium in about 1h. Mangifera indica can be effectively used as a biosorbent for uptake of Rhodamine B dye (Ibrahim \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Khan 2011). The sorption of various heavy metal ions by mango leaves (Mihretu \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) showed that time needed to achieve equilibration was dependent on the initial metal ion concentrations. The optimum pH of the study was 6. The maximum sorption capacity for these ions of concentration of 100 mg/L was in the range of 210\u0026ndash;230 mg/g using a sorbent dose of 0.4g/L. Mango leaf has also been used for removal of arsenic (III) (Kamsonlian 2012) at pH of 7 using a sorbent dosage of 7g/L. However, the studies were carried out for a very long time period (32h). The uptake of Cu and Zn ions by heat treated mango leaf samples were studied (Kaushal \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ong 2013).\u003c/p\u003e \u003cp\u003eIn spite of the fact that there are many reports on the use of mango leaf or its derivatives as sorbents, it was of interest to carry out a systematic study to understand the various aspects of sorption. In this paper, the sorption of commonly available dyes and transition metal ions was carried using untreated and pretreated (chemical / thermal) mango leaves. Different aspects of sorption have been used with and without pretreatment for the removal of commonly available cationic dyes and also common cations. A detailed investigation of the possible mechanism has been evaluated.\u003c/p\u003e \u003cp\u003eSimulation studies have been attempted to give an insight to the mechanism. To understand the selective binding of dye molecules and transition metal ions with the mango leaf, electronic structure calculations are carried out. The mango leaf has several active components such as quinone moiety, hydroxyl and carboxylic acid functional groups. Euxanthic acid (EA) is a small macromolecule which has all the essential active components of the mango leaf and thus we used EA as a model for mango leaf.\u003c/p\u003e"},{"header":"2.0 Experimental","content":"\u003cp\u003eThe mango leaves were collected from the western and southern parts of the country and named as Batch W and S respectively. The leaves obtained are washed well with water and dried. These dried samples are then treated differently prior to use. The untreated samples were used in different sizes ranging from 5cm in length to 100 microns. For treatment different processes were adopted. The acid treated samples mango leaves (ML-AW) was prepared by immersing in dilute acid (HCl) for two hours and then removed and washed several times with water. The water washed samples were then dried and stored in plastic bottles fit for further use. Fresh mango leaves were washed well and air dried. These samples were then heated at different temperatures ranging from 50 to 500\u0026deg;C. The heated samples were then labeled accordingly and used for sorption experiments immediately without storing. IR studies of the sorbents were carried out using Fourier Transform Infrared (FTIR) Spectroscopy (Bomem MB102) by using KBr pellets. The solutes chosen for the study were the cationic dyes namely Rhodamine B (RhB), Methylene blue (MB) and Malachite green (MG) and transition metal ions Cu (II) and Zn (II). The stock solutions of the dyes were prepared from the solid obtained from E.Merck without any further purification. The transition metal ions (Cu (II) and Zn (II)) were prepared by weighing their respective chloride salts (E.Merck) and diluted appropriately. The sorption studies were carried out in batch mode (Jayshree 2021). A fixed volume (10 mL) of solute solution (of known concentration and maintained at a particular pH) is equilibrated with accurately weighed amount of sorbent (pretreated differently) for a particular time period. Upon equilibration the amount of solute left behind in solution is determined using spectroscopic techniques. The concentration of dye left behind in solution was determined using a JASCO V 650 double beam spectrophotometer while ICP-AES was used for the determination of Cu(II) and Zn(II). The measurements are associated with error of 5%. This enables the calculation of the amount of solute taken up by the sorbent. The different experimental factors were varied and optimized to get maximum uptake. The studies were then extended to simulated sample solution containing a mixture of dye along with various cations and anions in tap water sample as described in earlier report (Khan 2019). The mixture solution maintained at pH 6, contained fixed concentrations of potassium di-hydrogen phosphate (50 mg/ L), calcium chloride (58 mg/ L), ferrous sulphate (500 mg/L), nickel sulphate (20 mg/L), magnesium sulphate (220 mg/L), ammonium chloride (1300 mg/ L), ferric chloride (17 mg/L), zinc chloride (4 mg/L), manganese chloride (7 mg/L), cobalt chloride (4 mg/L), sodium bicarbonate (5000 mg/L) and EDTA (300 mg/ L), and varying concentration levels of dye.\u003c/p\u003e \u003cp\u003eThe characterization studies of mango leaf sorbents were carried out to assess their utility for sorption of cationic species. The most important aspect which decides the applicability of the biosorbents is the surface charge. The point of zero charge of the sample was determined by the well-known solid addition method adopted for biosorbents (Akpotu 2023; Al-Maliky 2021). In this, a series of 100 mL of Erlenmeyer flasks with 45 mL 0.1M KNO\u003csub\u003e3\u003c/sub\u003e maintained at different pH values in the range of 1\u0026ndash;12 was taken. The pH adjustments were carried out by addition of appropriate volumes of 0.1M nitric acid/sodium hydroxide and then made to 50mL using 0.1 M KNO\u003csub\u003e3\u003c/sub\u003e. After measuring the initial pH of the solution, accurately weighed approximately 0.1 g of biosorbent is added flask is securely capped and shaken manually. After equilibration for a period of 48 h, the final pH of the solution is measured. The difference between the initial and final pH values (ΔpH) is plotted against the initial pH and the point of intersection (ΔpH\u0026thinsp;=\u0026thinsp;0) gives the point of zero charge of the sorbent. In addition to point of zero charge, surface area is another parameter which is very essential to understand the sorption. The surface area of the bio-sorbents was determined methylene blue absorption method (Xia 2019) using the simple relationship Specific surface area (SSA)= (Xm*N*A)/M In this, the maximum capacity obtained by Langmuir fitting of equilibrium data is substituted in the following relationship, wherein Xm is the monolayer capacity in mg/g, (from Langmuir calculation), N is Avagadro number (6.022x 10\u003csup\u003e23\u003c/sup\u003e) and A is area / one molecule of MB which is 130 Ǻ\u003csup\u003e2\u003c/sup\u003e (1.3E-20 m\u003csup\u003e2\u003c/sup\u003e) due to flat orientation and M is the mass of MB. (319.85 g/mol).\u003c/p\u003e \u003cp\u003eSimulation studies were carried out for EA molecule along with Rhodamine B (RhB), Methylene Blue (MB) and Malachite green (MG) dye molecules and hexa-aqua Cu (II) and Zn(II) ions. The structures are optimized at the density functional theory level. The species are optimized with BP86 functional in conjunction with def2-SV(P) basis set for all atoms (Sch\u0026auml;fer 1994; Becke \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Perdew \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). DOCKER program in ORCA 6.0 electronic structure package has been used to identify the binding of three dye molecules and two metal ions with EA molecule (Neese \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Neese \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The molecular electrostatic potentials (MESP) and optimized structures are plotted with AVOGADRO and Chimera softwares (Hanwell 2012; Goddard 2007; Pettersen 2004).\u003c/p\u003e"},{"header":"3.0 Results and Discussion","content":"\u003cp\u003eThe preliminary sorption studies using batches W and S showed that the variation in uptake between the two batches was less than 10%. This suggested that the origin of the leaves was not crucial in this particular study. The removal of cationic dyes by mango leaves could be attributed to the presence of functional groups which could bind or exchange these dyes. It is reported that mango leaf powder contains cellulose (45.2%) and lignin (26.6%) as the major constituents (Khan 2019) while extractives (12.2%) and hemicelluloses (13.3%) and ash (2.7%) form the rest of composition. All these constituents contain functional OH groups which could be responsible for the uptake of cationic species. It is also seen that the organic components containing the functional groups are present in percentage levels while the pollutant used for study is in the ppm levels of concentration. The functional groups present in the mango leaf sorbent has been identified using IR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (A)). The band at 3299 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e corresponds to O\u0026ndash;H stretching of bonded hydroxyl groups present in glucosides and lignin moiety (Kumar 2021). Similarly, the peak around 1618 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e is assigned to bending of O\u0026ndash;H group present in different constituents. The characteristic peak located at 2914 and 2855 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e correspond to the C\u0026ndash;H bond in \u0026ndash;CH\u003csub\u003e2\u003c/sub\u003e and methyl groups respectively. The band at 1726 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e corresponds to the stretching of the C\u0026thinsp;=\u0026thinsp;O group present in the unionized carboxylate structure / carboxylic acid / pectin ester. The peak positions at 1458 and 1370 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e related to the symmetric bending of \u0026ndash;CH\u003csub\u003e3\u003c/sub\u003e groups. Signal observed at 1240 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e can be accounted for \u0026minus;SO\u003csub\u003e3\u003c/sub\u003e stretching in ML. The peak at 1065 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e corresponds to the C\u0026ndash;O stretching of ether groups (Khan 2019; Pandey 2022). Peaks region lower than 800 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e may be related to N-containing bioligands, alkene, halo group, C\u0026ndash;H bending and C\u0026ndash;O\u0026ndash;H twist (Kuppusamy 2016). In this region, bands due to metal-oxygen bond appear. The presence of OH and COOH make it possible for the mango leaf to take up cationic species. The peaks below 1000 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e are attributed to the presence of M-O bonds (Chongrak 1998; Abdulwasiu 2022). In this region, peaks due to Ca-O, Si-O (Khan 2011) and Ca-Si bonds (Khan 2019) are present.\u003c/p\u003e \u003cp\u003eThe chemical composition of mango leaf has been earlier reported (Kumar 2021). It is seen that there are substantial levels of zinc (0.0024\u0026ndash;0.014%), sodium (0.003\u0026ndash;0.23%), boron (0.0016\u0026ndash;0.0042%), copper (0.0021\u0026ndash;0.0029%), iron (0.0062\u0026ndash;0.034%), manganese (0.0028\u0026ndash;0.003%) and cadmium (0.015%). The chemical composition of mango leaf used as sorbent in this study was assessed using EDS analysis. It was seen that sorbent contained C(72.98 wt %), O (24.08 wt%), Si (2.62 wt %) and Ca (0.32 wt%). Therefore, it becomes essential to use the same batch of mango leaf sorbent as well as tap water sample for the entire study. The dye solution was made up using tap water to understand the sorption in real scenario. Prior to making the solution, analysis of tap water was mandatory. The values of concentrations of different species in tap water is Ca (9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08mg/mL)\u0026thinsp;\u0026gt;\u0026thinsp;TOC (7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11mg/mL)\u0026thinsp;\u0026gt;\u0026thinsp;Mg (3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40mg/mL)\u0026thinsp;\u0026gt;\u0026thinsp;K (0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08mg/mL)\u0026thinsp;\u0026gt;\u0026thinsp;Zn (0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 mg/mL)\u0026thinsp;\u0026gt;\u0026thinsp;Fe (0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 mg/mL). It was essential to know the cationic composition of the water as the nature and concentration of these ions may interfere with the sorption studies. The concentration of most of the commonly present ions is in the range of our study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe TOC analysis was crucial as this may interfere with metal ion sorption. If a fresh tap water sample was taken, it was analyzed and the variation was found to be less than 10%. The effects of diverse investigational factors have been evaluated and the results are represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. For this, 10mL of MB solution (2 mg/L of MB) in tap water, was equilibrated under various conditions to achieve maximum uptake. The size and weight of the sorbent is an important parameter as decreased size leads to increased surface for sorption. For this, untreated mango leaf samples S1, S4, S10 and S50 having size of 100, 400, 1000 and 5000 microns respectively were used for equilibration of 120 mins. The amount of sorbent needed in each case was studied systematically and results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a). It is seen that as the sample size decreases from 5000 to 100 microns, there is an increase in the amount of dye sorbed. This could be attributed to the enhanced surface area at lower sizes of sorbent. An interesting observation was that for larger sample size, the amount of dye taken up increased with increase in the weight of sorbent but for smaller sizes this change was quite less. Thus it was possible to use lower sample weights of 100 micron samples. Hence for the samples S1, S4, S10 and S50, the amount needed to achieve maximum sorption was 0.05, 0.2, 2 and 2 gm respectively. Despite the fact that larger sized sorbent required higher sorbent dosage, it was thought of carrying out the complete studies with each of the sample using the appropriate weight. In order to see the effect of equilibration time and pH of initial solution on sorption efficiency, 10mL of 2 mg/L MB was used for the study. The time variation studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (b)) shows that amount taken up is higher as well as faster for S1 while it is quite lethargic for large samples (S10 and S50). Thus, maximum uptake is achieved in 30 mins for S1, 60 mins for S4 and S10 and 90 mins for S50. However, for uniform comparison further studies were carried out at 90 mins.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDespite the fact that the studies were carried out in tap water medium, it was of interest to evaluate the effect of solution pH on sorption characteristics. The solution pH will have profound effect on the uptake efficiency as it can lead to changes in the sorbate structure or the sorbent surface. The variation in pH (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(c)) shows that maximum uptake occurs at pH of 6. This corroborates with our earlier results on dye uptake studies using various sorbents (Jayshree 2021). From earlier studies, it is understood that the two extreme ends of pH will alter the chemical structure of cationic dye thus hindering the uptake. It is reported that pKa of MB is 3.8 and at higher pH the species is cationic in nature (Liany 2014). But with further increase into the alkaline range, the non-ionic form begins to dominate (Liany 2014). This tends to decrease the sorption efficiency. However, the effect of pH on the present sorbent surface was evaluated. From the plot of ΔpH vs pH (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(C)), it is seen that the curve crosses at pH value of 5.4 which is the point of zero charge. This value is in agreement with the reported value of 5.6 (Dey 2022). Thus, it is clear that at lower pH, the surface of sorbent is positive which reduces the sorption of cationic dye. At pH greater than 5.4, the surface becomes negative making it suitable for sorption of cationic dyes. Hence, pH of 6 was found to be optimum.\u003c/p\u003e \u003cp\u003eIn order to understand the effect of size on sorption, the specific surface area of the sorbent is to be calculated. In general, for calculation of specific surface area for biosorbents, MB sorption method is preferred. This could be due to the fact that BET being a dry method can produce erroneous results due to non-uniform sorption / desorption of N2 due to the non-homogeneous structure of the sorbent (seen from SEM images in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(B)). Due to irregular structure, the chances of N2 sorption and desorption need not necessarily be uniform during leading to variations in the measured values. Additionally, the methylene blue sorption is a liquid phase method, which allows the loading of the dye over the entire available sites. For biosorbent material, surface area values obtained using BET and Methylene blue sorption show variations (Chongrak 1998). In water, MB has easy access to the sites due to formation of linkages and the water medium helps in the sorbate reaching to most of the sites. The presence of water medium ensures the opening of the porous structures (seen from SEM) leading to uptake of MB. However, in N\u003csub\u003e2\u003c/sub\u003e sorption, preconditioning could alter the sample structure which can affect the measured values. Hence no attempt was made to use the BET method in this particular study. Thus, reports making use of MB sorption as a tool to measure surface area of biosorbents suggest that the method is very easy and results obtained have greater precision (Abdulwasiu 2022; Reddy 2012; Kipling \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In certain reports, the maximum experimental capacity value is taken but in others, the value from Langmuir fitting is taken. The basis of this calculation is the dimensions of methylene blue. Methylene molecule has parallel pipe shape with dimensions of 1.7 nm \u0026times; 0.76 nm \u0026times; 0.33 nm and surface area covered by one molecule can be 1.30 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;18\u003c/sup\u003e, 6.6 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;19\u003c/sup\u003e or 2.47 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;19\u003c/sup\u003e m\u003csup\u003e2\u003c/sup\u003e/ molecule. In all the earlier reports, 1.30 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;18\u003c/sup\u003e m\u003csup\u003e2\u003c/sup\u003e / molecule is taken for further calculations. In this study attempt has been made to use both these values to calculate the specific surface area. It is seen that the values obtained using Qmax was higher than that with qe by a maximum of 2 times. Hence, the computed value using experimental qe and Qmax follow the same trend. Thus, the values of surface area obtained from qe and Qmax (given in parenthesis) for the samples are in the order S1 (79.40 (115) m\u003csup\u003e2\u003c/sup\u003e/g:) \u0026gt; S4 (48.12 (62.3) m\u003csup\u003e2\u003c/sup\u003e/g) \u0026gt; S10 (18.10 (32.3) m\u003csup\u003e2\u003c/sup\u003e/g) \u0026gt; S50 (6.82 (14.0) m\u003csup\u003e2\u003c/sup\u003e/g) attributing to the increased sorption efficiency in the same order. It was seen that the uptake did not lead to any changes in the pH, which would be expected if ion exchange mechanism operated. Since the solution pH did not alter much, it became essential to understand the composition of the mango leaf sorbent. From EDS it is seen that Ca is present in sorbent and this can be replaced with the dye and make the solution alkaline. However, the solution left behind does not show any significant variation nor contains proportional quantities of Ca which is expected when ion exchange occurs. To get a possible explanation for the same, XRD characterization of the sorbent was carried out (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(C)). It was observed that the sample overall is amorphous as characterized by broad noisy humps. In addition to that there are discrete sharp peaks at 2θ values of 14.95\u0026ordm; (maximum), 24.45\u0026ordm;, 30.13\u0026ordm; and 46.25\u0026ordm;. In earlier reports, these peaks have been attributed to the presence of low crystalline cellulosic material arranged in ordered lattice and bonded with O-H group through strong secondary forces (Reddy 2012; Kipling \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, from EDS, we know that both Si and Ca are present in reasonable amounts. So, it becomes essential to understand whether these contribute to the peaks. It is reported that silica microspheres tend to show a peak at 22\u0026ordm; (Liang 2011). For pure CaO nanoparticles, peaks are obtained at 2θ values of 32\u0026ordm; (Jalu 2021) and this is slightly different to the values in our study. This is probably due to the fact that CaO is not isolated but as a part of the organic group present in the mango leaf. Thus, the peaks of mango leaf sorbent is due to the presence of O-H, C\u003csub\u003e2\u003c/sub\u003eH\u003csub\u003e6\u003c/sub\u003e, C\u003csub\u003e6\u003c/sub\u003eH\u003csub\u003e5\u003c/sub\u003eOH, NH\u003csub\u003e3\u003c/sub\u003e, MgO\u003csub\u003e2\u003c/sub\u003e, Na-O, and Ca-O. The crystallinity index gives a good understanding of the structure (Park 2010). The average crystallite grain size computed from XRD pattern is 84 nm while the crystalline index is around 50%. Further characterization is done using Nuclear Magnetic Resonance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(E)). In the high field region (0\u0026ndash;3 ppm), presence of organic acids is confirmed. It is reported earlier that in addition to citric, malic, and succinic acids, shikimic and quinic acids dominate (Duarte 2005). In addition, aliphatic acid like Alanine is also present (Lizada 1993). The spin values at 0.89, 1.29, 1.55, and 2.21 ppm indicate saturated fatty acids like palmitic acid (Wu 1993). The signals in the region 1.20\u0026ndash;1.32 ppm indicate presence of fucose and rhamnose (Tucker \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The mid filed of 3-5.5 ppm indicate the presence of indicates the presence of various types of sugars (Tjan 1974). In the low field region of 5.5\u0026ndash;10 ppm, the prominent peak indicates shikmic acid and other acids and aldehyde. Thus, the presence of phenolic and acidic groups tends to be responsible for the uptake of MB. The optimized pH which resulted in maximum MB sorption is 6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(C)). However, in order to understand the sorption mechanism and kinetics, it becomes essential to study the time variation of the other cationic species before studying the concentration effect. These studies indicate that nearly complete sorption is achieved for all the species in 120 mins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(B)). To obtain the concentration isotherm, further studies with other sorbates were carried out at pH 6 with equilibration for 120 mins and the appropriate amounts of the sorbents. The results are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The experimental capacity values are in the order MG\u0026thinsp;\u0026gt;\u0026thinsp;Rh B\u0026thinsp;\u0026gt;\u0026thinsp;MB \u0026cong;Cu(II)\u0026thinsp;\u0026gt;\u0026thinsp;Zn(II) for all sorbent sizes. Since the MB data was fitted to compute the specific surface area, it becomes essential to fit these data to different equilibrium models. Different models were used to understand the sorption behavior of all the sorbates on the four types of sorbents. The equilibrium data were fitted to Langmuir, Freundlich, DR and Temkin models (Jayshree \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The studies reveal that MB sorption shows better fit to Langmuir model while sorption of RhB and MG shower better fit for Freundlich model for all the sorbents S1, S4, S10 and S50 based on the R\u003csup\u003e2\u003c/sup\u003e value\u0026thinsp;\u0026ge;\u0026thinsp;0.95. This indicates that only one molecule of MB is sorbed on definite, localized site without interfering with the adjacent sites. Further this model indicates that the amount sorbed does not affect the rate and once a monolayer is formed, saturation occurs. The value of b (Langmuir constant) can be related to the variation of the area and porosity of the sorbent and for MB sorption it is found to increase in the order for the four sorbents S4 (0.10) ~S1(0.09) \u0026gt; S10 (0.05) \u0026gt; S50 (0.03). An analogous trend was observed for both RhB and MG. The capacity values computed using this model is useful for comparing the efficiency of various sorbents. S1 has the highest capacity value for all the sorbates studied. The dimensionless constant, RL, is the Langmuir separation factor (Eq.\u0026nbsp;3) which can predict the favourability of sorption. In this study, the values of RL is in the region of 0\u0026ndash;1 indicating the sorption to be favourable. The value is closer to 1 for MB sorption on S1 indicating sorption to be both favoured and as well as linear in nature. It is interesting to note that only the sorption using S1 can be fitted to the Langmuir model for the uptake of both Cu(II) and Zn (II) and the values indicate a favourable sorption.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFreundlich isotherm model gives an idea of heterogeneity of sorbent surface. The values computed Kf (Freundlich sorption capacity (L mg/ g)) and n (sorption intensity) could give an understanding of the nature of sorption. For all the systems, it is seen that the value of 1/n is in the range 0\u0026ndash;1 indicating favourable sorption. The increased sorption capacity for Cu as compared to Zn (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) could be due to the interaction between the carboxylate groups within the sorbent (Bunting \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1970\u003c/span\u003e). Even though the system was studied at room temperature, the energy associated with the process was evaluated using Dubinin-Radushkevich (D-R) isotherm model. Despite its original use for vapour sorption onto microporous solids, DR can be used to study the energetics of the sorption process. It is seen that for all the systems in the present study, the sorption followed DR (R\u003csup\u003e2\u003c/sup\u003e value\u0026thinsp;\u0026gt;\u0026thinsp;0.98 for all the sorbents) and the free energy calculated is in the range of 8\u0026ndash;16 kJ/ mol indicating the process to be chemisorption at room temperature. To understand the effect of interactions between sorbent and sorbate on the overall process, Temkin isotherm model is used. The results showed that both binding energy and heat of sorption increases with decrease in particle size indicating better interaction with the sorbent sites for sample S1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComputed Parameters from equilibrium modelling\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eS50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eS10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eFreundlich\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eqe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eK\u003csub\u003ef\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csub\u003eL\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1/n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRhB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eqe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eK\u003csub\u003ef\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csub\u003eL\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1/n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eqe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eK\u003csub\u003ef\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csub\u003eL\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1/n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eqe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" morerows=\"2\" nameend=\"c5\" namest=\"c3\" rowspan=\"3\"\u003e \u003cp\u003eDoes not follow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" morerows=\"5\" nameend=\"c11\" namest=\"c8\" rowspan=\"6\"\u003e \u003cp\u003eDoes not follow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csub\u003eL\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eqe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" morerows=\"2\" nameend=\"c5\" namest=\"c3\" rowspan=\"3\"\u003e \u003cp\u003eDoes not follow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csub\u003eL\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe kinetics depict the advancement of uptake with time until saturation occurs due to equilibrium. The modelling of this data could give an understanding of the mechanism. The data has been subjected to both rate as well as mechanism equations. The best-fit model was selected based on the values of linear regression correlation coefficient, R\u003csup\u003e2\u003c/sup\u003e. The well known kinetic models of Lagergren pseudo first-order and pseudo second-order kinetic model developed by Ho and McKay have been used (Jayshree \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The parameters computed from the kinetic models are given in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Pseudo first-order and pseudo second-order kinetic models are used to describe the rate of sorption and understand the mechanism. Lagergren pseudo first-order model assumes that sorption rate is proportional to the difference between maximum adsorption capacity and the amount of adsorbate adsorbed at any given time. It indicates a diffusion-controlled process. Pseudo-Second-Order Model assumes that chemisorption is the rate determining step suggesting the interactions between sorbate and sorbent.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eComputed Parameters from kinetic modelling\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003ePsuedo First order\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003ePsuedo Second order\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS50\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eS50\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eS10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eS4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eq\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eq\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd 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align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e270.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e498.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eq\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eq\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ek1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the present study, it is interesting to note that the model followed depends on nature of sorbate. It is seen that the sorption of all the sorbate follow the pseudo first order reaction in the entire time period for all the sizes of the sorbent. However, only Methylene blue obeys pseudo second order model in the entire time period of study. In case of RhB and Cu sorption, the sorption data after 20 mins of start follows the pseudo second order model. It is further noted that for MG and Zn, the pseudo second order model is applicable after 50 mins of sorption. Therefore as no one particular model can be used to fit the sorption data it is very clear that the biosorption in this study is quite complex and there are many rate determining steps. The pseudo first order kinetics assumes that the adsorption rate is proportional to the number of vacant sites on the adsorbent surface, while the pseudo second order kinetics assumes that the adsorption rate is proportional to the square of the number of vacant sites (Jayshree \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, in this case the qe values predicted are also much greater than the experimentally obtained values. This indicates that sorption does not occur only on the vacant sites of the sorbent. When experimental results are lower than those predicted by kinetic models, it suggests that the model is not fit to the reaction due to the sorption being very complex. Moreover, the dyes sorbed on to the sorbent may tend to interact with its immediate neighbours leading to the formation of dimers and trimers which may lead to multilayer sorption. In order to validate the above results, Elovich model was used. It was originally used to assess the chemisorption behaviour of gases on solid surface. The model assumes that the rate of adsorption of solute decreases exponentially as the amount of adsorbed solute increase. In this model, constant 'a' represents the initial adsorption rate of a substance onto a surface, while 'b' represents the desorption constant. The higher value of b indicates high activation energy of chemisorption making it difficult to desorb. From Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it is seen that values of a and b are higher as size decreases for all sorbates. However, the values are maximum for Rhodamine B indicating less tendency to desorb. This could be due to the fact that the sorbed rhodamine B could form dimers and trimers making it difficult to desorb.\u003c/p\u003e \u003cp\u003eThe uptake involves different stages. The initial state involves the presence of active sites of sorbent. The sorbate molecules tend to diffuse through the liquid film surrounding the sorbate particle (Film diffusion). The sorbate then tends to get sorbed onto sorbent site. The sorbed sorbates tends to diffuse through the pores of sorbent (intraparticle diffusion) and then finally reaches equilibrium (rates of sorption and desorption are equal). To get a better understanding, the kinetic data were subjected to mechanism models namely Webber Morris Intraparticle diffusion and Boyd diffusion models (Jayshree \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) are used to further assess whether sorption follows film or intraparticle diffusion. The Boyd model, assuming infinite bath conditions (near constant value of sorbate concentration) and a linear equilibrium relationship between the sorbate concentrations in the solution and sorbent phase, describes the rate of diffusion in liquid. According to this model, external film diffusion is the rate-limiting or determining step in sorption. The major assumptions of this model may not be applicable in many systems and in such cases, the model is not applicable. The parameters calculated from Boyd\u0026rsquo;s Model is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for the data obtained in this study. It is seen that Boyd\u0026rsquo;s model was applicable to all the systems from 10 mins after start of equilibration till 60 mins. The linearity as given by R\u003csup\u003e2\u003c/sup\u003e\u0026gt; 0.95 indicate that external film diffusion operates within the time period. It is seen that for the same sorbate, value of I is higher for S1 which has the least particle size. When film diffusion is rate-controlling, a smaller sorbent particle with larger surface area and thinner diffusion path shows higher diffusion rate. This is indicated by higher I indicating faster kinetics. The value of B as obtained from the slop is used to calculate the diffusion coefficient (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). It is seen that with decrease in particle size, the diffusion coefficient increases indicating higher diffusion rate. From Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e it is seen that the value of intercept in Boyd\u0026rsquo;s model is different for the different ions. It is seen that the values for transition metal ions is higher than that of cationic dyes. Boyd\u0026rsquo;s model can help to understand whether the diffusion through the liquid film surrounding the sorbent or the pores is rate determining step. The value of intercept can help in understanding the mechanism. If the linear plot passes through the origin (intercept value is zero), then mechanism is internal pore diffusion. However, if the plot does not pass through intercept, then this value becomes an important parameter to understand the process. Higher value of intercept indicates higher resistance to mass transfer due to film diffusion. Thus, it is seen that Cu and Zn ions show the least resistance to mass transfer due to film diffusion as compared to the dyes studied. The mechanism can be further evaluated. The plot of Bt vs t being linear if crosses the origin indicates mechanism to be internal pore diffusion. In the present study, it is seen that only for Cu and Zn, internal pore diffusion is visible for all the sizes of the samples. The pore size of sorbent is about 20\u0026ndash;50 nm. Therefore all the sorbates should be possible to enter the pores. However, it is seen that pore diffusion is not an important step for the sorption of the dyes. This indicates that the dye cations may tend to form dimers and trimers which lead to bulky structure thus restricting the internal pore diffusion mechanism. The migration of ions from bulk to the liquid film surrounding the sorbent particle is the first step involved. A diffusion barrier created, can play a major role in the rate determining step. The external diffusion regulates the transfer of solute from bulk solution to the liquid film around the sorbent particle while internal diffusion controls the transfer to pores. The movement of sorbates to external surface of sorbent (external or film diffusion) or to the pores (internal diffusion) can decide the rate of reaction. The values of diffusion coefficient give further understanding of the process. It is reported that diffusion coefficient for film diffusion is in the range of 1E-6 to 1E-8 cm\u003csup\u003e2\u003c/sup\u003e/s and that for pore diffusion is within the range of 1E-11 to 1E-13 cm\u003csup\u003e2\u003c/sup\u003e/s (Jayshree 2021). From the table, it is seen that for all the systems in this study, the value is in the range of 1E-6 to 1E-8 cm\u003csup\u003e2\u003c/sup\u003e/s indicating film diffusion-controlled process. Webber Morris model can further assess the nature of rate determining step. In this the plot of qt vs t gives the value of intraparticle diffusion rate constant from slope and intercept I gives an idea of thickness of boundary layer. Theoretically a single plot is expected. However, due to the different mechanisms, it may not be a single plot. Different linear portions are attributed to different mechanisms. The first linear portion defines the mechanism for film diffusion or external mass transfer process. The second portion defines the pore diffusion mechanism while the third linear portion indicates adsorption-desorption equilibrium. Generally, the last stage is not the rate determining step as it is fast. The time durations of each of these processes can give further clue about the mechanism. For S50, there is a single plot which indicates that sorption is controlled by film diffusion mechanism. For S10, plots corresponding to film diffusion and adsorption-desorption are seen for all the sorbates. For S4, MB and RhB sorption show three distinct linear regions while MG, Cu and Zn show two regions confirming film diffusion mechanism. For MB, the first two linear regions are in the time period of 2\u0026ndash;40 min and 40\u0026ndash;60 min respectively. Thus the ratio of film diffusion to pore diffusion is 2:1 indicating film diffusion to be dominant but present along with pore diffusion. also contributes. A similar result is obtained for RhB sorption onto S4. For S1, MB sorption shows first two distinct portions in time period of 0\u0026ndash;40 and 40\u0026ndash;60 (ratio 2:1) indicating film diffusion to be dominant. A similar mechanism is evident for RhB and Zn. For MG and Cu, there are only two peaks indicating the presence of film diffusion mechanism being dominant. These results are in agreement with the results of Boyd\u0026rsquo;s model. It is to be mentioned that the decrease in particle size leads to complexity in the sorption mechanism.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters computed from Boyd and Webber Morris models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eBoyd\u0026rsquo;s Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eWebber Morris\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eS50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eS10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD(E-8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e 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colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e 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colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eMG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd 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align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD(E-8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD(E-8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe kinetic data was subjected to diffusion chemisorption models (Jayshree \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It was seen that the model is not applicable throughout the first 60 mins of sorption process. Thus, it confirms that the sorption process obtained in the present study is dominated by film diffusion as given by both Boyd and Webber Morris model. It is also clear that chemisorption does not control the overall sorption rate. Thus, it is clear that pore diffusion is very rapid as can be seen from the porous structure of the sorbent (SEM image in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Furthermore, Cu tends to diffuse to the pores and bind with groups present and therefore monolayer sorption is not followed (Langmuir model not followed). Regeneration is an important aspect of sorption studies. Even though the sorbent used in the present study was a biowaste, it was of interest to evaluate the possibility of regeneration and reuse of the sorbent. The regenerations studies were carried out with MB sorption on sample S1. The first approach was to use dilute acid as the regenerant. It was seen that the removal was more than 90% for all the sorbates. However subsequent reuse showed a decreased uptake efficiency. Since the sorbent is a biowaste, thermal treatment was thought of as an attractive alternative. The idea behind this was that even if the sorbent is destroyed along with the dye, the waste is reduced. Thus, the samples were heated at 50\u003csup\u003eo\u003c/sup\u003eC and tested. It was seen that regenerated sorbent showed an increase in uptake efficiency\u003c/p\u003e \u003cp\u003eIn order to understand the changes, a systematic study was carried out by pretreating sample S1 by acid as well as heat treatment in temperature range of 50\u003csup\u003eo\u003c/sup\u003eC to 500\u003csup\u003eo\u003c/sup\u003eC. The uptake efficiency with respect to methylene blue was studied to understand the changes. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (A). It is seen that uptake efficiency increased with temperature increase up to 100\u003csup\u003eo\u003c/sup\u003eC and there after decrease was observed. To get an insight of these changes, it was thought pertinent to characterize the pretreated samples. The pretreated samples were analysed using various characterization techniques. The scanned images of samples Pure, AW (Acid washed), ML-50 (heated at 50\u003csup\u003eo\u003c/sup\u003eC), ML-100 (heated at 100\u003csup\u003eo\u003c/sup\u003eC) and ML-200 (heated at 200\u003csup\u003eo\u003c/sup\u003eC). The preheating beyond 250 made the samples very dark and image was black. Also beyond preheating at 200\u003csup\u003eo\u003c/sup\u003eC, the samples when equilibrated with dyes imparted additional blackish brown colour to the dye solution making them unsuitable for use as sorbents. The images of these samples without and with MB are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(B). It is seen that with increase in temperature, ML tend to become brown and finally becomes black. Heating at 500\u003csup\u003eo\u003c/sup\u003eC gives black powder.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe XRD of these samples are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(C). Pure ML sample shows XRD pattern with peaks at 2θ values of 14.95\u003csup\u003eo\u003c/sup\u003e 24.45, 30.13 and 46.25 corresponding to cellulosic material with regular lattices network due to bonding of O\u0026ndash;H groups (Khan 2019). In acid treated samples, XRD pattern shows sharpest peak at 15.07 indicating that the O-H peak of Pure ML is altered. This is expected as the acid attacks the O-H group converting it into a different moiety. The peak at 30.13 is also shifted to 31.12 indicating some bond cleavage due to acid treatment and changes in the cellulose network. However new peaks are formed at 22.13 and 55.53. Due to acid treatment the peaks due to MgO, NaO and CaO are clearly visible (Dey 2022). In the temperature variation study, it is seen that at 150\u003csup\u003eo\u003c/sup\u003eC, the structure becomes more crystalline in nature while it tends to amorphous nature when heated at 200 \u003csup\u003eo\u003c/sup\u003eC. ML-50 gave three sharp peaks at 15.05, 27.24 and 30.15 corresponding to O-H and C2H6. The peak at 42 degrees corresponds to NH2. For ML-100 gave sharp peaks at 15.10, 24.47, 30.129, 38.55 correspond to cellulose network. The peak at 38.55 corresponds to C2H6 group. ML-150 gave peaks at 10.153, 13.225,15.21, 15.47, 17.68, 25.89 indicating the loss of phenolic groups present. Proton NMR Spectroscopy was also used to understand the changes in structure. The results are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(D). It is seen that in the high field region (0\u0026ndash;3.0 ppm) shows signals due to presence of various acids like citric, malic, succinic acids in predominance along with lower amounts of lactic, propionic, and 3-hydroxybutyric acids. The presence of lipids is confirmed by the peaks at 0.89, 1.29, 1.55, and 2.21 ppm, which may arise from one or more saturated fatty acids (e.g. palmitic acid. The mid field region (3.0\u0026ndash;5.5 ppm) is dominated by intense and overlapped signals of the major sugars. In low field region, the prominent signal is due to shikimic acid. It is seen that NMR spectra changes with pretreatment indicating the possible structural changes of Mangiferin into isomanguiferolic acid, Ambolic acid etc (Reddy 2012; Escobedo 2012; Neiss 1999; Belton 1997). Thus, it is evident that the pretreatment by heating tends to change the original dominant species in mango leaf.\u003c/p\u003e \u003cp\u003eFTIR spectroscopy was used to further understand the changes with pretreatment. The results are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (E). FTIR is useful to identify the functional groups based on their vibrations (Adelaja 2019; Kuppuswamy 2016; Peydayesh \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gupta 2012; Reddy 2012; Uddin 2009). The IR spectra of pristine Mango leaf showed characteristic peaks at 3299, 2913.9, 1618 and 1065 cm\u0026thinsp;\u0026minus;\u0026thinsp;1 with small peaks at 2855, 1726 cm-1 and multiple small peaks between 1458\u0026thinsp;\u0026minus;\u0026thinsp;1240 cm-1 and 800\u0026thinsp;\u0026minus;\u0026thinsp;640 cm-1. The peak at 3299 cm-1 was due to the O-H group stretching due to intra and intermolecular hydrogen bonding of alcohols or phenols present in lignin, cellulose, pectin, and hemicelluloses (Adelaja 2019). The peak at 2855 cm\u0026thinsp;\u0026minus;\u0026thinsp;1 corresponds to C\u0026ndash;H stretching of methyl and methylene groups while the peak at 1618 cm-1 is attributed to the carbonyl group (C\u0026thinsp;=\u0026thinsp;O) stretching in the carboxylate structure or carboxylic acid. The stretching of C\u0026thinsp;=\u0026thinsp;O of unionized carboxylic acid is reflected in the peak at 1726 cm-1. The peak at 1065 cm-1 corresponds to the C-N stretching of aliphatic amines while peak around 1300 cm-1 corresponds to C-N stretching of aromatic amines or symmetric bending of CH3 group. The peak near 800 cm-1 indicated the presence of =\u0026thinsp;C\u0026mdash;H bending of alkenes. In ML-AW, the major peaks corresponding to the O-H group, C\u0026ndash;H stretching of methyl and methylene groups and carbonyl group (C\u0026thinsp;=\u0026thinsp;O) stretching are not altered much. The acid treatment is found to enhance the peaks at 2855 cm-1 (C\u0026ndash;H stretching of methyl and methylene groups) and 1726 cm-1 (stretching of C\u0026thinsp;=\u0026thinsp;O of unionized carboxylic acid). Thus, the C\u0026thinsp;=\u0026thinsp;O is affected by acid treatment. Heat treatment shows marked changes in the spectra. The peak at 3299 cm-1 is found to decrease in intensity with increase in temperature of heating. This indicates that heating removes the water molecules adsorbed on the surface and also within the structure. Similar trend is observed for the peak at 2855 cm\u0026thinsp;\u0026minus;\u0026thinsp;1 (corresponds to C\u0026ndash;H stretching of methyl and methylene groups) indicating the cleavage of C-H bonds. The peak at 1618 cm-1 (attributed to the carbonyl group (C\u0026thinsp;=\u0026thinsp;O) stretching in the carboxylate structure or carboxylic acid) is also found to show decreased intensity indicating the possible thermal cleavage of this group. It is interesting to note that the peaks at 1726 cm-1 (stretching of C\u0026thinsp;=\u0026thinsp;O of unionized carboxylic acid) and 1065 cm-1 (corresponds to the C-N stretching of aliphatic amines) increases in intensity with temperature indicating the thermal ionization of these groups.\u003c/p\u003e \u003cp\u003eIn order to understand the changes, SEM and EDS analysis were carried out. The SEM images (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(E)) showed a change in the microstructure with the pretreatment. Acid treatment resulted in the loss of the porous structure while heating till 200\u003csup\u003eo\u003c/sup\u003eC resulted in a more homogeneous structure with well-defined pores making it more suited for uptake studies. The results of EDS given in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e give a further understanding of the chemical changes upon pretreatment. From EDS results (table along with SEM images) it is seen that S1 has got reasonable amounts of C (72.98 wt. %), O (24.08 wt. %), followed by lower amounts of Si (2.62 wt. %) and small amount of Ca (0.32 wt.%). It is seen that upon acid treatment and heating at 100 and 200\u003csup\u003eo\u003c/sup\u003eC, the calcium is removed. Furthermore, heating at higher temperatures tends to increase the C and O content while Si content is reduced. Thus, it is clear that there are structural changes upon heating. Moreover, the decreased Ca levels result in increased uptake as the sites are available and the structure is more open for uptake\u003c/p\u003e"},{"header":"4.0 Simulation Studies","content":"\u003cp\u003eIn order to understand the mechanism involved in the biosorption, simulation studies were carried out. This has been achieved by density functional theory (state of the complexes within DFT) calculations. The hydrated metal ions, orthophenanthroline and their complexes are scrutinized using DFT calculations. All structures are optimized with BP86 functional in conjunction with def2-TZVP basis set (Niesse 2022, Niesse 2012; Hanwell 2012; Goddard 2007; Sch\u0026auml;fer 1994; Sch\u0026auml;fer 1992; Becke \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Perdew \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). The importance of weak interactions is recognized using Grimme\u0026rsquo;s D3-BJ corrections to the functional. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The computed MESP of EA, RB, MB and MG is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(a). The blue and red color codes indicate moieties with positively and negatively charged lobes. Among the three dye molecules, MG has the largest negative potential and can bind strongly with EA which is largely having positive potential. The same can be confirmed with distances among the three pairs, EA:RB, EA:MB, EA:MG, where MG has the shortest distance with EA as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(b).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEven though the dyes studied are cationic in nature, it is interesting to note that MG exhibits a negative potential due to the presence of electron-donating groups around its three conjugated phenyl rings, as well as in the central ring. These groups enhance electron density and promote delocalization of negative charge, making the molecule more electronegative. Conversely, EA contains electron-withdrawing hydroxyl (-OH) groups, which reduce electron density and create an electropositive character. Due to the large flexible nature of EA and the dye molecules, several possible stacked structures are possible. To identify the most probable structure, we have used the DOCKER algorithm. The minimum energy structures of the three dye molecules along with Cu2\u0026thinsp;+\u0026thinsp;and Zn2\u0026thinsp;+\u0026thinsp;ions are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(b). The binding energies (kcal/mol) computed for the five systems follows the order of EA:MG (-312.26)\u0026thinsp;\u0026gt;\u0026thinsp;EA:MB (-311.39)\u0026thinsp;\u0026gt;\u0026thinsp;EA:RhB (-307.56)\u0026thinsp;\u0026gt;\u0026thinsp;EA: Cu(H2O)\u003csub\u003e6\u003c/sub\u003e\u003csup\u003e2+\u003c/sup\u003e (-98.80)\u0026thinsp;\u0026gt;\u0026thinsp;EA: Zn(H2O)\u003csub\u003e6\u003c/sub\u003e\u003csup\u003e2+\u003c/sup\u003e (-80.90). This order amongst the dyes can be attributed to the presence of several hydrogen bonding and π-π interactions. However, for the two transition metal ions, it is observed that Cu\u003csup\u003e2+\u003c/sup\u003e ion binds stronger than Zn\u003csup\u003e2+\u003c/sup\u003e ion. Mulliken charge analysis reveals net charges of 0.509 and 0.796 on Cu\u003csup\u003e2+\u003c/sup\u003e and Zn\u003csup\u003e2+\u003c/sup\u003e respectively, indicating a greater charge transfer from Cu\u003csup\u003e2+\u003c/sup\u003e to the EA ligand compared to Zn\u003csup\u003e2+\u003c/sup\u003e. Due to its higher charge-to-radius ratio, Cu\u0026sup2;⁺ behaves as a stronger hard Lewis acid compared to Zn\u0026sup2;⁺. This results in a stronger interaction with EA, driven by the principle of hard-hard acid-base interactions. Additionally, due to the Jahn-teller nature of Cu\u003csup\u003e2+\u003c/sup\u003e ion, there is a redistribution of electronic density within the molecule. This distortion influences molecular polarizability, further reinforcing the strong interaction between Cu\u003csup\u003e2+\u003c/sup\u003e and the EA whereas in the case of Zn\u003csup\u003e2+\u003c/sup\u003e ion, this interaction is rather weak leading to the least binding energy. The same can be confirmed with the DFT optimized distances between EA and M\u003csup\u003e2+\u003c/sup\u003e(H\u003csub\u003e2\u003c/sub\u003eO)\u003csub\u003e6\u003c/sub\u003e in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(b). The binding energies are significantly lower for ions compared to dye molecules. This difference arises from the presence of hexa-coordinated water molecules surrounding the ions, which mediate the interaction with EA ligand. In contrast, dye molecules engage in direct interactions with EA, leading to stronger binding affinities. The computed binding affinities are corroborated with the experimentally obtained uptake capacity values.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe present study utilizes waste mango leaf as possible sorbent for cationic organic dyes as well as cations in tap water medium. The sorbents were characterized with different techniques to understand the sorption process. The equilibrium and kinetic data obtained were subjected to modelling which gives an deeper insight of the mechanism involved. Pretreatment of the sorbents resulted in a marked changes in the uptake efficiency. The probable reason for this change was understood using characterization techniques. To get a better understanding of the overall process, simulation studies were carried out. The binding energies of the solute with a particular component of the sorbet was calculated. The order was found to match the order of capacity values for solutes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e The authors thank Head ACD and colleagues in BARC for the support during this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e No special funding was obtained for this work\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnalytical Chemistry Division, Bhabha Atomic Research Centre, Mumbai \u0026ndash; 400 085.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJayshree Ramkumar, S.Chandramouleeswaran, Naman K. Bharti\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eChemistry Division, Bhabha Atomic Research Centre, Mumbai \u0026ndash; 400 085\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMahesh Sundararajan\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHomi Bhabha National Institute, Anushaktinagar, Mumbai \u0026ndash; 400 094.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJayshree Ramkumar, Mahesh Sundararajan, Naman K. Bharti\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIsotope and Rad. Application Division, Bhabha Atomic Research Centre, Mumbai \u0026ndash; 400 085\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBhumika Kumari\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eK.J. Somaiya College of Science and Commerce VidyaVihar, Mumbai-400077\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlan John Mathew\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e: All authors whose names appear on the submission\u003c/p\u003e\n\u003cp\u003e1) made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work;\u003c/p\u003e\n\u003cp\u003e2) drafted the work or revised it critically for important intellectual content;\u003c/p\u003e\n\u003cp\u003e3) approved the version to be published; and\u003c/p\u003e\n\u003cp\u003e4) agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e\n\u003cp\u003eJayshree Ramkumar - Conceptualization, Data Analysis, Writing the original draft\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBhumika Kumari \u0026ndash; Data Acquisition\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eS.Chandramouleeswaran - Data Acquisition\u003c/p\u003e\n\u003cp\u003eAlan John Mathew - Data Acquisition\u003c/p\u003e\n\u003cp\u003eNaman K. Bharti and Mahesh Sundararajan\u0026ndash; Theoretical simulations and writing of this part\u003c/p\u003e\n\u003cp\u003eCorresponding author \u0026ndash; Jayshree Ramkumar and Mahesh Sundararajan (for theoretical simulations)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e The submitted work should be original. The manuscript is not submitted to more any other journal for simultaneous consideration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u003c/strong\u003e NA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish:\u003c/strong\u003e NA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e: The authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e: All data generated or analysed during this study are included in this published article and its supplementary information files.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdulwasiu OS, Emmanuel IA, Hajara Y (2022) Application of Methylene Blue Adsorption Technique in the Determination of Specific Surface Area of Termite Feathers. 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J Haz Mater 420:126596\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mangifera indica, biosorption, dyes, metal ions, pretreatment, molecular simulations","lastPublishedDoi":"10.21203/rs.3.rs-8729755/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8729755/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBiosorption is a cost effective and clean process for the removal of toxic species. In this paper, an attempt has been made to make use of Mangifera indica biowaste for removal of cationic species. Different characterization techniques were used to characterize as well as understand the sorption process. The sorption behaviour was found to be dependent on the sorbent size with higher efficiency seen for samples with least size. The sorption of the dyes was found to be dependent on the nature of dye. The studies were carried out around pH\u0026thinsp;~\u0026thinsp;6 as the pzc was obtained as 5.4. The differences in the sorption behaviour were further understood using molecular simulations. It was found that there was agreement between the experimental and simulation studies. Extensive equilibrium and kinetic modelling were carried out on the equilibraium and kinetic data obtained in this study. It was interesting to note that the mechanism followed in the sorption was dependent on the nature of solute as well as the size and pretreatment protocol.\u003c/p\u003e","manuscriptTitle":"Biosorption behaviour of Mangifera indica biowaste as function of Pretreatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-20 13:22:54","doi":"10.21203/rs.3.rs-8729755/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d42519a8-f346-47ed-bfb6-6a1452d6f4a9","owner":[],"postedDate":"February 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-14T11:05:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-20 13:22:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8729755","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8729755","identity":"rs-8729755","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}