Effective removal of toxic Cd(II) ions from aqueous solution using mixed macroalgal adsorbent: Kinetics and ANN modeling studies

Effective removal of toxic Cd(II) ions from aqueous solution using mixed macroalgal adsorbent: Kinetics and ANN modeling studies | 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 Effective removal of toxic Cd(II) ions from aqueous solution using mixed macroalgal adsorbent: Kinetics and ANN modeling studies P Thamarai, V C Deivayanai, S Karishma, Saravanan Anbalagan, P. R. Yaashikaa, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4195678/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 The research focuses on examining the biosorption capability of raw mixed seaweed biosorbent (RMSB) for the removal of the hazardous metal cadmium (II) under controlled environmental conditions. Using techniques such as elemental dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR), biosorbent was characterized. The impacts of adsorbent dosage, contact time, initial Cd concentration, pH, and temperature have been assessed for the removal of Cd (II) and its adsorption. Optimum levels - pH, biosorbent mass, contact duration, and temperature were 5, 2 g/L, 50 minutes, and 303 K, respectively. The optimum intake of metals Cd (II) has been evaluated with isotherm modeling. Single-layer sorption was confirmed by the Freundlich isotherm, which proved to be an excellent fit. Maximum potential adsorption of Cd (II) was 146.2 mg/g. The biosorption kinetics of Cd (II) onto RMSB exhibit pseudo-first-order behaviour. The feasibility of the sorption process was established, and the thermodynamic parameters were determined. The Cd (II) sorption onto RMSB biomass has been estimated through the use of artificial neural networks (ANNs). With the high cross-correlation coefficient (R) value, the ANN models predicted the Cd (II) adsorption onto RMSB with remarkable accuracy. The outcomes showed that Cd (II) may be effectively removed from the aqueous solution using RMSB. Seaweed Cadmium (II) Adsorption Isotherm Artificial Neural Network Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Water is an essential resource for life on Earth, as both people and the ecosystem depend on obtaining access to clean water. However, during the past few decades, uncontrolled use of natural resources, fast industrialization, growing urbanization, and a constantly growing population have collectively shown a negative impact on water quality [ 1 ]. Since ions containing heavy metals are among the majority of frequently discharged pollutants, they should be considered serious [ 2 , 3 ]. Although heavy metals are naturally occurring trace elements in aquatic environments, industrial wastes, geochemical structures, mining, and agricultural practices have raised the levels of these elements. Mercury (Hg), cadmium (Cd), copper (Cu), zinc (Zn), silver (Ag), iron (Fe), platinum (Pt), arsenic (As), chromium (Cr), thallium (Ti), and lead (Pb) are a few examples of heavy metals [ 4 , 5 ]. Cadmium is one of the most important elements among the metallic contaminants present in wastewater and is frequently discharged by the electroplating, battery, electrode, and metallurgical sectors. The US Environmental Protection Agency (USEPA) has recommended an acceptable concentration of 0.003 mg/L for cadmium in drinking water, but the World Health Organization (WHO) has set a limit contamination threshold of 0.005 mg/L [ 6 ]. Humans have been shown to experience negative effects from low levels of cadmium exposure, including liver disease, bone abnormalities, and renal failure. Metal recovery from aqueous solutions is a difficult task for environmental engineers, and many different approaches use different separation techniques [ 7 , 8 ]. Heavy metal removal from wastewater can be accomplished using a variety of standard methods, including electrolytic recovery, evaporation, chemical oxidation, precipitation, chemical coagulation, solidification, and membrane separation. However, because these methods are highly expensive to treat wastewater with low concentrations of heavy metals, their adoption is constrained by several variables, including technological and economic status [ 9 ]. In comparison with alternative traditional treatment techniques, the biosorption process presents potential benefits like reduced operational expenses, reduced amounts of chemical or biological sludge, enhanced efficacy in removing heavy metals from diluted solutions, biosorbent regeneration, the potential for metal recovery, and environmental friendliness [ 10 ]. The novel technique of biosorption extracts hazardous metals from aqueous solutions by utilizing either living or dead biomasses. The two primary mechanisms of biosorption are chemical adsorption and physical adsorption (electrostatic attraction–Vanderwaal forces of attraction). For the biosorption of metal ions, a variety of biomasses including bacteria, yeast, fungi, and algae have been utilized extensively [ 11 , 12 ]. Marine algae have the highest metal binding capabilities of all living materials because their cell walls include lipids, proteins, or polysaccharides. The seaweed's cell walls are made of three different types of biopolymers that give it its biosorption capacity: cellulose, alginate, a heteropolymer made of mannuronic acid and guluronic acid residues, and fucoidan, a heteropolymer composed of sulfated esters of fucose moieties and glucuronic acid. Amino, sulphate, carboxyl, and hydroxyl groups are among the functional groups found in seaweed biomass [ 13 ]. When compared to single-species biosorbents, mixed seaweed biosorbents have many advantages for the removal of metals. Due to their various surface properties and chemical compositions, several seaweed species when combined can increase the total capacity for binding metals. Furthermore, increased efficiency and specificity in metal removal techniques can result from the synergistic effects between different seaweed components. In addition, mixed seaweed biosorbents frequently have improved stability and durability, ensuring extended use and efficient metal removal over time [ 14 ]. In order to understand the uncertain nonlinear pattern of heavy metal elimination using different treatment procedures, numerous artificial intelligence models have been established. Neural networks, logic, regression, and hybrid models are some of these models. Moreover, these models have been compared to a variety of conventional models, such as mathematical, isothermal, statistical, empirical, and physical models [ 15 ]. To model and optimize contamination removal approaches, these classical tools need to identify a goal for each group of input variables; hence, the target can change while the other variables remain constant at the same time [ 16 ]. Since these surrogate models have primarily been used for adsorption systems with a single pollutant, it is essential to extend their application to the prediction and modeling of adsorption systems with several adsorbates, or multi-component adsorption. Through the use of adsorption systems, scientific and practical models that utilize numerical computational approaches, such as artificial neural networks (ANNs), can greatly enhance the wastewater treatment process generally [ 17 ]. Every year, the tides throw a significant amount of seaweed onto beaches. These organic natural resources are handled as domestic waste since they are waste materials. As of now, researchers have employed a variety of materials to investigate the efficacy of removal from aqueous solutions [ 18 ]. Sargassum fusciformis (brown algae), Codium decorticatum (green algae), and Hypnea valentiae (red algae) were the seaweeds found around the coast that were selected for this investigation due to their component availability. The goal of this study is to develop a mixed seaweed biosorbent that can effectively remove cadmium (II) ions from aqueous solutions. The study assessed the impact of various parameters on the removal efficiency. The metal removal mechanism and kinetic parameter estimates were determined by kinetic experiments. Thermodynamic studies were used to confirm the sorption's nature and feasibility. Additionally, analysis and optimization of the ANN model were conducted to predict the mixed sorbent's efficiency in removing Cd (II). 2. Materials and Methodology 2.1 Preparation of stock Cd (II) solution A solution containing 1000 mg/L of Cd ions is produced by mixing distilled water with a specified quantity of cadmium salt. NaOH (0.1 N) and HCl (0.1 N) have been used to change the adsorbate's pH. The required amount of Cd (II) ions can be obtained by further diluting the resulting stock solution. The total amount of Cd (II) ions was determined through an analysis using an Atomic Absorption Spectrophotometer (AAS). 2.2 Preparation, analysis, and characterization of mixed adsorbent The three fresh samples for the research were obtained from the Ramanathapuram region in Tamil Nadu. After being thoroughly cleaned with tap and deionized water to eliminate impurities, freshly obtained samples of brown algae ( Sargassum fusciformis ), green algae ( Codium decorticatum ), and red algae ( Hypnea valentiae ) have been dried for a day at 70°C in a hot-air oven before being ground in a domestic mixer. “Raw Mixed Seaweed Biosorbent” (RMSB) was a label provided for the produced biosorbent. The biosorbent was maintained in an oven with humidity control throughout the equilibrium studies. The Brunauer Emmett-Teller (BET) nitrogen adsorption/desorption technique was used to determine the pore volume and surface area of RMSB at 77 K. Determining the active sorptive surface locations on the biosorbent's surface can be done with great benefit using scanning electron microscopy. With the aid of elemental dispersive X-ray (EDX) analysis, the elemental composition and mapping of the mixed biosorbent were investigated. By using Fourier Transform Infra-Red (FTIR) spectroscopic analysis, the surface chemistry and functional groups of the biosorbent were deduced. X-ray diffraction spectroscopy (XRD) was utilized to investigate the crystalline quality of the biosorbents. 2.3 Studies on batch biosorption Batch removal studies can be used to evaluate RMSB for Cd (II) removal in an initial way. In batch adsorption experiments, variables – pH, temperature, time, adsorbent dose, and Cd (II) quantity initially have all been examined as influencing factors. Each 250 mL Erlenmeyer flask in the series used for the investigation held 100 ml of the cadmium solution together with the necessary amount of adsorbent. In a shaking incubator with temperature control, the heavy metal solution was agitated at a speed of 100 revolutions per minute for different durations. After a predetermined amount of time, the cadmium solution flasks were taken out of the incubator. The amount of Cd (II) ion elimination percentage (%) can be calculated in the equation below. $$Cd \left(II\right) removal= \frac{\left({C}_{i}- {C}_{f}\right)}{{C}_{i}} \times 100$$ 1 Whereas C f indicates the final Cd (II) solution concentration, C i indicates initial Cd (II) concentration in this equation. 2.4 Investigation of Isotherm and kinetics Optimizing adsorption systems requires the use of adsorption isotherms, which are equilibrium data and adsorption parameters that show the interactions between pollutants and adsorbent materials. Initial Cd (II) concentrations were altered in the range of 20 to 160 mg/L for equilibrium bio-sorption tests, while other variables were held constant. Each flask was filled with the proper dosage of adsorbent material, which was then kept there for the perfect amount of time to reach equilibrium. The samples were removed from the incubator at the specified periods. Using AAS, the amount of Cd (II) left in the filtrate after the solution was filtered was determined. The amount of removed Cd (II) was computed using the following formula. $${q}_{e }= \frac{\left({C}_{i } - {C}_{e}\right)V}{m}$$ 2 The capacity for equilibrium adsorption (qe), The quantity of mixed biomass is indicated by "m," while the equilibrium concentration of Cd (II) ions is regarded by Ce and the volume of the cadmium solution is represented by V. In the present research, the adsorption experimental data was fitted utilizing four distinct adsorption isotherm models. A description of Langmuir's model is given by [ 19 ], $${q}_{e }= \frac{{q}_{m }{K}_{L }{C}_{e}}{1+ {K}_{L }{C}_{e}}$$ 3 Here, q m stands for Langmuir monolayer sorption ability and K L for Langmuir energy constant. The overview of the Freundlich model is as follows [ 20 ], $${q}_{e }= {K}_{F}{C}_{e}^{\frac{1}{n}}$$ 4 The system's capacity and sorption intensity are connected with 1/n and KF, respectively. The term (1/n) measurement yields a favorability indicator for the sorbent/adsorbate systems. The following equation has investigated the Sips isotherm [ 21 ], $${q}_{e }= \frac{{K}_{S}{{C}_{S}}^{{B}_{S}}}{1+{\alpha }_{S}{{C}_{S}}^{{B}_{S}}}$$ 5 where β S is the Sips isotherm exponent, K S , and α S are the Sips isotherm model constant (Lg − 1 ) Toth modeling has been examined by [ 22 ], $${q}_{e }= \frac{{q}_{m }{C}_{e}}{{\left({K}_{T0}+ {C}_{e}^{tn}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$tn$}\right.}}$$ 6 Here, t n is the Toth isotherm exponent; C e tn denotes binding affinity constant. Kinetic analysis can be used to evaluate the rate of sorption mechanism involved in the removal by RMSB. The calculation of Cd (II) ions extracted from the adsorbent material at different time intervals, the following equation has been used. $${q}_{t}= \frac{\left({C}_{0}-{C}_{t}\right)V}{m}$$ 7 Here, Ct stands for the Cd (II) concentration following a specific time. In order to investigate the mechanism of Cd (II) adsorption onto RMSB, the study examined pseudo-first-order, pseudo-second-order, and Elovich kinetic models. The rate expression of the pseudo-first-order reaction can be stated as follows [ 23 ]: $${q}_{t }= {q}_{e}\left(1-\text{exp}\left(-{k}_{1}t\right)\right)$$ 8 The pseudo-second-order kinetics model is described below [ 24 ], $${q}_{t}=\frac{\left({q}_{e }^{2}{k}_{2}t\right)}{\left(1+ {q}_{e}{k}_{2}t\right)}$$ 9 The definition of the Elovich Kinetic model is as follows [ 25 ]: $${q}_{t}= \left(1+{\beta }_{E}\right)In\left(1+{\alpha }_{E }{\beta }_{E}t\right)$$ 10 In this case, α E represents the initial adsorption rate, and k 1 , k 2 , and β E denote the pseudo-first order, pseudo-second order, and desorption constants. 2.5 Thermodynamics Thermic characteristics and spontaneity of the process of elimination have been determined by evaluating thermodynamic parameters. The following formulas were used to determine the thermodynamic parameters: $${K}_{C }=\frac{{C}_{Ae}}{{C}_{e}}$$ 11 $$\varDelta {G}^{^\circ }= -RTIn{K}_{C}$$ 12 $$Log{K}_{c}= \frac{\varDelta S^\circ }{2.303R}- \frac{\varDelta H^\circ }{2.303RT}$$ 13 where R is the ideal gas constant (8.314 J/molK) and T is the Kelvin temperature of adsorption. The equilibrium concentration of Cd (II) in the solution is C e , and the amount of Cd (II) adsorbed on the adsorbent per litre of the solution at equilibrium is C Ae . 2.6 Artificial Neural Network (ANN) Model Artificial neural network prediction was performed using experimental data sets for the Cd (II) sorption process by raw mixed seaweed adsorbent. Approximately 264 experimental findings were used in the ANN process since the ANN model fits and performs effectively with huge numbers of data in data sets. The ANN analysis is trained and cross-validated using a second-order algorithm called Levenberg-Marquardt (LM). To achieve improved convergence behaviour, LM algorithm makes use of the second-order derivatives of the mean squared error between the predicted and actual outputs. The neural network fitting process has been carried out using MATLAB 2022a software. The neural network was composed of input, output, and neural network training layers. The percentage of elimination was taken into consideration as an output variable, while the absorbed dosage, contact time, pH, and temperature were used as input elements or variables. Ideal neuron numbers has been determined by trial and error. 30% of the entire set of data, were used for validating and testing the neural network model, while the remaining 70% were used for network training. When the network reaches the threshold value or the minimal value of the cross-validation MSE, optimization is achieved. 3. Results and Discussion 3.1 Characterization studies of biosorbent BET area of mixed biosorbent was calculated using N 2 sorption-desorption data. The average specific surface area was calculated using BET analysis, and the average pore diameter and pore volume were calculated using Barrett–Joyner–Halenda (BJH) analysis. According to the results, the surface area, pore volume, and pore diameter under the BJH pore size distribution were approximated and determined to be 3.003 m 2 /g, 0.007 cc/g, and 3.968 nm. Surface area of the sample was 1.777 m 2 /g under multipoint BET. Methods, properties, and values for BET analysis are shown in Table 1 . Table 1 BET analysis of RMSB Methods Properties Values BJH pore size distribution Surface area 3.003 m 2 /g Pore volume 0.007 cc/g Pore diameter Dv (d) 3.968 nm Multi-point BET Surface area 1.777 m 2 /g SEM images of RMSB indicating morphological changes before and after adsorption are shown in Figs. 1 (a) and 1(b). Before Cd (II) adsorption, the SEM images of RMSB show a diverse surface morphology with irregularities and roughness. The surface seems to be comparatively smooth, with random pores and fissures. SEM images demonstrate noticeable alterations in surface morphology following adsorption. The formation of irregular aggregates and increased roughness characterize the surface as it becomes more textured. This suggests that Cd (II) ions interact with the biosorbent since pores and fissures appear to be more noticeable. Clusters or aggregates of Cd (II) ions are visible and adhere to the biosorbent matrix. Effective adsorption across the surface is indicated by the homogenous distribution of Cd (II) ions, which suggests efficient utilization of active binding sites throughout the RMSB surface. Figures 2 (a) and 2(b) illustrate the elemental composition determined by EDX analysis of RMSB before and after adsorption. The elemental compositions of the raw mixed seaweed biosorbent, as inferred by the EDX analysis, are C (28.35%), O (39.89%), and Na (14.40%), indicating that carbon is the major element, followed by oxygen and sodium. The natural components of seaweed biomass, where carbon and oxygen are important elements of organic matter, are apparent in this composition. The carbon content of the biosorbent noticeably increases with Cd (II) ion adsorption, indicating that the ions have bound to the carbonaceous sites in the biomass. The effective sorption of Cd (II) ions onto the biosorbent surface is shown by this rise in carbon content. Furthermore, a trace amount of nitrogen is found, which may be explained by the binding of Cd (II) ions to functional groups in the seaweed biomass that contain nitrogen. The presence of the functional group on the surface of the mixed seaweed biosorbent can be determined using the FTIR technique. Figure 3 (a) and Fig. 3 (b) depict FTIR images of RMSB before and after being loaded with heavy metal Cd (II). FTIR spectroscopy showed that the different functional groups of the biosorbents were examined before adsorption, and those were O-H, N-H, C-H, C ≡ C, C = O, C = C, and M-O. A significant peak located at 4455.56cm − 1 has been attributed to the intermolecular O-H stretching vibrations of hydroxyl groups that occur in alcohols or phenols. After the adsorption of Cd (II) ions, peaks such as 2274.07 cm − 1 and 2054.19 cm − 1 changed from 2270.22 cm − 1 and 2065.76 cm − 1 , which would constitute an indication of modifications to the C ≡ C stretching vibrations. Following adsorption, the majority of the peaks were linked to stretching vibrations of metal-oxygen (M-O). Peak shifts indicate interactions between the mixed seaweed adsorbent's functional groups and Cd (II) ions. After adsorption, some peaks that were there formerly disappeared or significantly diminished. The existence of Cd (II) ions on the surface of the seaweed adsorbent is indicated by the appearance or changes of peaks associated with metal-oxygen stretching vibrations. This shows that the Cd (II) ions have been successfully adsorbed onto the adsorbent material. The XRD diffractogram of RMSB is shown in Fig. 4 . Based on the known 2θ values, the d-spacing for each peak was calculated using the standard X-ray wavelength of 1.5406 Å. Following that, through analysis and comparison with known patterns, these d-spacing values became related to certain crystallographic planes. The detected peaks imply that the seaweed biosorbent has a variety of crystalline phases. It is possible to attribute the peaks at 27.434°, 28.520°, and 31.704° to different cellulose or hemicellulose components which have plane correlations of (002), (200), and (020). The presence of additional crystalline phases, probably connected to minerals like calcite, aragonite, or other carbonate compounds, is suggested by the peaks at 40.533°, 42.559°, 43.572°, and 45.526°. Higher angles are associated with the peaks at 56.454°, 66.223°, 75.342°, and 83.881°; these points may represent additional mineral phases, such as silica, quartz, or other silicates, or more sophisticated crystalline structures. These peaks might also be signs of the existence of particular crystalline organic molecules. 3.2 Performance of batch adsorption 3.2.1 Solution pH The impact of pH on the removal efficiency of RMSB is shown in Fig. 5 (a). The elimination percentages at different pH values showed that biosorption effectiveness is strongly pH-dependent. The findings show that until it reaches an optimal level, the removal effectiveness of RMSB rises with increasing pH. After that, it begins to decrease. Elimination efficiency is comparatively poor at acidic pH values (pH 1 and pH 2) (20.62% and 50.98%, respectively). The protonation of active sites on RMSB clarifies that this leads to a reduction in the number of binding sites that are available for the target contaminants. A significant increase in removal efficiency occurs when pH rises from acidic to neutral (pH 3 to pH 7), suggesting that biosorption is likely under favourable conditions. The higher electrostatic interaction between the biosorbent and the pollutant ions can be ascribed because of deprotonation in functional groups of biosorbent. pH 5 (90.25%), or almost neutral conditions, is where the maximum removal effectiveness is found. The RMSB's surface charge is probably optimized at this pH, which makes it easier for contaminants to be adsorbed. A minor drop in removal effectiveness is seen above pH 5, which may be brought on by hydroxide ions and pollutant ions competing for active sites on the surface of the biosorbent. 3.2.2 Biosorbent Dose The influence of the adsorbent dose on the removal of Cd (II) ions is illustrated in Fig. 5 (b). The removal efficiency significantly improved when the adsorbent dosage varied between 0.5 g/L and 1 g/L, and removal rising between 24.32% and 59.82%. This significant improvement can be ascribed to the larger concentration of adsorbent material in the solution, which increases the availability of adsorption sites. A range of 1 g/L to 2 g/L of adsorbent was added, and the removal efficiency increased noticeably to 92.765%. This implies that there were still accessible active sites for adsorption and that the RMSB's adsorption capacity had not yet reached saturation. However, the improvement in removal efficiency was not as significant at 2 g/L. In comparison to the greatest efficiency attained at 2.5 g/L (92.34%), there was a minor drop in removal efficiency at 3 g/L (90.75%) and 3.5 g/L (90.24%). This phenomenon could be explained by the adsorbent particles aggregating at higher dosages, which would reduce the effective surface area that is accessible for adsorption. The pattern that has been seen suggests that there is a dosage of adsorbent that is ideal for effectively removing the desired contaminant. The elimination efficiency is maximized at a dosage of about 2 g/L, which seems to be the ideal one in this instance. Increasing the adsorbent dosage after this point might not significantly improve removal efficiency and might even have the opposite effect. 3.2.3 Initial Concentration The removal efficiency, which ranges from 92.53–94.55%, is still strong at lower values (20–60 mg/L). However, there is a noticeable decrease in removal efficiency as the starting concentration rises above 80 mg/L. This implies that at higher initial Cd (II) concentrations, the RMSB's biosorption capacity reaches saturation. Initial concentration impact over Cd (II) ions removal has been given in Fig. 5 (c). There are multiple reasons for the decline in removal efficiency at greater doses. First, the accessible binding sites on the biosorbent get saturated more quickly when the concentration of Cd (II) in the solution rises, which reduces the biosorbent's adsorption capacity per unit mass. Higher concentrations can also result in a reduction in the overall removal efficiency because of the increased competition between Cd (II) ions for binding sites on the biosorbent surface. The limitations of utilizing RMSB for the removal of large quantities of Cd (II) from aqueous solutions are highlighted by the rapid drop in removal effectiveness shown at concentrations above 100 mg/L. 3.2.4 Contact Time To determine the adsorption rate in a batch process, contact time investigations were performed. The contact period for RMSB was varied during the tests, ranging from 10 to 80 minutes. Figure 5 (d) demonstrates the impact of contact time on the batch process for RMSB, respectively. Initially, the removal capabilities of RMSB were comparatively poor, at a contact duration of 10 minutes. This can be understood by the fact that the biosorbent did not have enough time to form efficient interactions with the target pollutants in the solution. The removal capabilities showed a continuous increase with increasing contact time, suggesting that pollutants were gradually adsorbed into the RMSB surface. The removal capacities were maximized at 50 minutes of contact time, indicating that the adsorption and desorption processes had achieved equilibrium. Prolonged contact times do not considerably improve the adsorption performance of RMSB; after 50 minutes, the removal efficiency tends to flatten or even slightly decline. 3.2.5 Temperature The influence of temperature on RMSB is depicted in Fig. 5 (e). With a range of 20 mg/L − 160 mg/L, the concentrations of Cd (II) ions were investigated at several different solution temperatures (303K, 313K, 323K, and 333K). As the initial concentration of pollutants increases, it is common to notice a decline in removal effectiveness during biosorption procedures. This is most likely because to the biosorbent's restricted ability to absorb pollutants at greater concentrations. The effectiveness of removal usually increases with increasing temperature as the initial concentration fluctuates, up to a certain point, at which point higher temperatures lead to a decrease in removal efficiency. The mentioned behaviour might be attributed to the less favourable adsorption process at lower temperatures, which is caused by a decline in molecule kinetic energy. However, elevated temperatures may cause excessive thermal agitation that disrupts adsorption or induces desorption of pollutants that have already been adsorbed. Based on similar findings across all initial concentrations examined, it appears that 303K is the optimum temperature for removing pollutants using RMSB. When compared to other examined temperatures, this temperature shows higher removal efficiency due to the biosorbent's superior adsorption characteristics. 3.3 Modeling studies 3.3.1 Adsorption isotherms The relationship between the equilibrium concentration of Cd (II) in aqueous solution (C e ) and the amount of adsorbed Cd (II) on biosorbent in equilibrium (q e ) is represented by the isotherm. In this work, the Langmuir, Freundlich, sips, and Toth equilibrium isotherm models were investigated. Parameters of four isotherm models involving the RMSB in batch sorption investigations are listed in Table 2 and Fig. 6 shows the isotherm model fits for this current study. The best-fitting model for the current sorption system has been identified based on lower SSE (Sum of squared errors) and RMSE (Root mean squared error) values and better correlation coefficients, or R 2 values. The experimental adsorption data described best-fit for Cd (II) sorption among four-parameter isotherm models utilized in this investigation as follows: Freundlich > Langmuir > Toth > Sips. The Cd (II) sorption process on RMSB biosorbent was found to obey the Freundlich isotherm, according to the coefficient of determination (R 2 ) value of 0.9708 showing the maximum adsorption capacity of 146.2 mg/g. Additionally, the value of parameter "n" in a Freundlich isotherm indicates the intensity of adsorption through the Freundlich exponent. A value of "n" greater than 1 denotes both the strong binding of Cd (II) onto RMSB and the well-fitting of the Freundlich model to the sorption system. Table 3 displayed the maximum sorption capacity of RMSB in comparison to other seaweed-based biosorbents. Table 2 Isotherm model constants for the adsorption of Cd (II) Isotherm Model Conditions R 2 Langmuir K L (L/mg) = 0.5356 q m (mg/g) = 146.2 0.9542 Freundlich K F [(mg/g) (L/mg) (1/n) ] = 39.94 n = 4.009 0.9708 Toth q m (mg/g) = 19.085 K = 0.0584 0.8911 Sips Ks(L/mg) = 0.233 qm (mg/g) = 88.2 n = 0.219 8.715 Table 3 Comparison of the maximal adsorption capabilities of various seaweed adsorbents for the removal of heavy metal ions Seaweed adsorbent utilized Best fitted model qm (mg/g) References Sargassum sp. and Turbinaria Sips 585 [ 26 ] Eucheuma denticulatum Langmuir 416.67 [ 27 ] Ascophyllum nodosum Langmuir 223 [ 28 ] Cymopolia barbata Langmuir 192.2 [ 29 ] Raw mixed seaweed biosorbent (RMSB) Freundlich 146.2 Present study Chaetomorpha sp., Polysiphonia sp., Ulva sp. and Cystoseira sp. Sips 115.198 [ 30 ] Caulerpa scalpelliformis Langmuir 111.11 [ 31 ] Sargassum crassifolium Freundlich 93.65 [ 32 ] Ulva lactuca Freundlich 78.0 [ 33 ] Sargassum carpophyllum Langmuir 52.37 [ 34 ] Hizikia fusiformis Langmuir 22.03 [ 35 ] Chara aculeolata Langmuir 23.0 [ 36 ] Bifurcaria bifurcata Freundlich 20.27 [ 37 ] Cymodocea nodosa Langmuir 11.6 [ 38 ] 3.3.2 Adsorption kinetic modeling The appropriate design of wastewater treatment equipment operating in continuous mode requires research on removal rate. The process of adsorption mechanism and the step that regulates the rate of adsorption can be determined through the study of kinetics. This helps with industrial-scale scaling and upholding ideal conditions during continuous operations. Three models have been used in this study to analyze the experimental data and forecast the adsorption kinetics mechanism of cadmium ions: pseudo-first-order kinetics, Elovich kinetics, and pseudo-second-order kinetics. Table 4 provides the parameters of each model with their corresponding R 2 values for each unique initial concentration of Cd (II) solutions. Figure 7 depicts the kinetic models for the adsorption of Cd (II) ions by RMSB. The order of fitness of the three models for cadmium sorption using RMSB is determined to be pseudo-first order > pseudo-second order > Elovich based on error % and correlation coefficients (R 2 ). The pseudo-first-order model's applicability provided evidence that the adsorption rate is dependent on adsorption capacity. Table 4 Parameters of the pseudo-first-order, pseudo-second-order, and Elovich kinetic models for the biosorption of Cd (II) using RMSB Conc. (mg/mL) Pseudo-first-order Pseudo-second-order Elovich qe (mg/g) k 1 (min − 1 ) R 2 qe (mg/g) k 2 (g/mg min) R 2 αE (mg/g min) βE (g/mg) R 2 20 19.7 0.2226 0.9692 22.28 0.005695 0.9646 0.9921 1.534 0.9024 40 38.24 0.03078 0.9779 43.71 0.002578 0.957 0.9598 2.361 0.8911 60 54.84 0.3049 0.9699 62.37 0.001907 0.9516 0.7384 3.663 0.8839 80 70.99 0.2871 0.9613 81.15 0.001388 0.9543 0.4302 5.245 0.8981 100 84.57 0.3213 0.9711 96.55 0.001179 0.954 0.3676 6.381 0.8903 120 97.24 0.0252 0.9411 111 0.001021 0.9368 0.3092 7.502 0.8834 140 106.3 0.4028 0.98 122 0.000882 0.9498 0.2269 8.61 0.8798 160 116 0.1073 0.9828 135.1 0.0006917 0.9502 0.1233 10.42 0.8869 3.3.3 Biosorption Thermodynamics Since energy cannot be gained or lost, the fundamental principle of thermodynamics states that the only force acting upon an isolated system is a shift in entropy. Energy and entropy concerns need to be considered into account during ecological engineering practice in order to determine which process will arise spontaneous. Thermodynamic factors in biosorption of Cd (II) onto RMSB were computed. Table 5 displays the Gibbs free energy values that change during the biosorption process. Figure 8 further supports the thermodynamic nature of Cd (II) adsorption. The findings show that these values fluctuate in a negative direction. The values of ΔG° decreased with an increase in temperature, suggesting the spontaneous nature of biosorption for Cd (II), and the negative ΔG° values of Cd (II) at various temperatures revealed that the biosorption process is spontaneous. It is implied that the process is exothermic by negative ΔH° values. Table 5 Thermodynamic parameters for RMSB-mediated Cd (II) adsorption RMSB Initial Cd (II) concentration (mg/L) 20 40 60 80 100 120 140 160 ΔG° (kJ/mol) 303 K -12.311 -7.119 -5.6867 -3.980 -3.294 -2.541 -1.961 -1.633 313 K -9.146 -6.808 -4.824 -3.722 -3.086 -2.397 -1.947 -1.359 323 K -7.987 -5.769 -4.506 -3.500 -2.578 -1.771 -1.119 -0.451 333 K -5.319 -4.514 -3.576 -2.928 -2.339 -1.420 -0.872 -0.103 ΔH° (kJ/mol) -53.105 -49.474 -46.311 -33.460 -29.230 -26.693 -20.306 -18.339 ΔS° (J/mol/K) -141.371 -134.188 -128.2571 -91.3108 -81.802 -76.42 -58.7625 -54.881 3.4 ANN Modeling The developed artificial neural network (ANN) model has the potential to characterize the behaviour of a complex process, given the parameters of the experimental situation. The current study develops an artificial neural network (ANN) to remove Cd (II) ions from an aqueous solution using RMSB. Using the Levenberg-Marquardt backpropagation method (trainlm), neural network training was carried out. With 70% of the data used for training and the remaining 30% for validation and testing, 264 data points were employed in neural network fitting. 70% of the data are used as inputs in training for predicting the output percentage of removal. The accuracy of the model has been tested using a multilayer perceptual network with four different input variables: pH, RMSB dosage, contact time, and temperature. The experimental data has been divided to prevent over-parameterization and training. Using the trial-and-error procedure, a range of neurons, from 5 to 30, were evaluated. The algorithm determines the weight changes by determining the total training error for a given epoch. Every epoch, the weights are changed to a minimal value that is randomly selected by adding or subtracting from the weights that occurred previously and these were recorded and stored at a specific synapse. The performance plot displays differences in MSE and the number of epochs. The fifth epoch iterations of the ANN produced the lowest validation mean square error. Figure 9 (a) shows the error histogram for the adsorption of Cd (II) ions. Plots for the experimental and predicted outcomes for the training, testing, and validation datasets are displayed in Fig. 9 (b). For training, validation, testing, and all experimental data, the corresponding correlation coefficients were 0.9596, 0.94396, 0.90449, and 0.94679. The MSE for training the ANN model was less than 0.005. The model fitness was appropriate because all of the correlation coefficients were within proximity of one another. The ANN model's predicted outcomes matched the experimental target data set. The linear fit model obtained from validation outputs is displayed in the following equation: Y vs. T in the ANN model. $$Y=0.87\left(Target\right)+8$$ 14 4. Conclusion In the present study, Raw mixed seaweed biosorbent (RMSB) has been utilized for the removal of Cd (II) ions from the aqueous solution. Phase identification, surface area, porous nature, and functional groups involved in the removal of metal ions from aqueous solutions were examined using BET, SEM-EDX, XRD, and FTIR techniques. The optimal ranges of various operating parameters for Cd (II) adsorption on RMSB has been found as pH- of 5, time-50 min, temperature 303 K, and adsorbent dosage of 2 g/L. The adsorption process experimental results showed significant correlations with Freundlich model alongwith the maximal adsorption capacity − 146.2 mg/g. The nature of physisorption was confirmed by the pseudo-first-order model, which described the biosorption of cadmium. Thermodynamic analysis revealed that the process is essentially exothermic, with negative entropy (ΔS°) and enthalpy changes (ΔH°). The modeling approach utilizing ANN showed a great deal of effectiveness in representing the adsorption system using RMSB. During the training phase, the Levenberg-Marquardt (LM) method was used to determine the ideal topology for the ANN. The trained ANN model performed better in predicting the Cd (II) removal process than the chosen model, as evidenced by the strong correlation coefficient of 0.94679 for the Cd (II) removal onto RMSB. For the removal of metal ions from waste water streams, RMSB has been proven to be a potential biosorbent. Declarations Funding The authors did not receive support or funding from any organization for the submitted work. Conflicts of interest/Competing interests The authors have no conflicts of interest to declare that are relevant to the content of this article. Availability of data and material The data will be made available on request. Code availability Not applicable Authors' contributions Writing, Editing, and Drafting- P. Thamarai, V. C. Deivayanai, S. Karishma Conceptualization, Visualization, Supervision, and Methodology - A. Saravanan, P.R. Yaashikaa, A.S. Vickram Ethics approval This study does not contain any studies with human or animal subjects performed by any of the authors. Consent to participate Not applicable Consent for publication Not applicable References Ahirvar BP, Das P, Srivastava V, Kumar M. Perspectives of heavy metal pollution indices for soil, sediment, and water pollution evaluation: An insight. Total Environ Res Themes. 2023;6:100039. https://doi.org/10.1016/j.totert.2023.100039 . Zamora-Ledezma C, Negrete-Bolagay D, Figueroa F, Zamora-Ledezma E, Ni M, Alexis F, et al. Heavy metal water pollution: A fresh look about hazards, novel and conventional remediation methods. Environ Technol Innov. 2021;22:101504. https://doi.org/10.1016/j.eti.2021.101504 . Malik LA, Bashir A, Qureashi A, Pandith AH. Detection and removal of heavy metal ions: a review. Environ Chem Lett. 2019;17:1495–521. https://doi.org/10.1007/s10311-019-00891-z . Babuji P, Thirumalaisamy S, Duraisamy K, Periyasamy G. Human health risks due to exposure to water pollution: a review. Water. 2023;15:2532. https://doi.org/10.3390/w15142532 . Bharti R, Sharma R. Effect of heavy metals: An overview. Mater Today Proc. 2022;51: 880–885. https://doi.org/10.1016/j.matpr.2021.06.278 . Pyrzynska K. Removal of cadmium from wastewaters with low-cost adsorbents. J Environ Chem Eng. 2019;7:102795. https://doi.org/10.1016/j.jece.2018.11.040 . Krishnan S, Zulkapli NS, Kamyab H, Taib SM, Din MFBM, Abd Majid Z, et al. Current technologies for recovery of metals from industrial wastes: An overview. Environ Technol Innov. 2021;22:101525. https://doi.org/10.1016/j.eti.2021.101525 . Carolin CF, Kumar PS, Saravanan A, Joshiba GJ, Naushad M. Efficient techniques for the removal of toxic heavy metals from aquatic environment: A review. J Environ Chem Eng. 2017;5:2782–99. https://doi.org/10.1016/j.jece.2017.05.029 . Verma A, Agarwal M, Sharma S, Singh N. Competitive removal of cadmium and lead ions from synthetic wastewater using Kappaphycus striatum. Environ Nanotechnol Monit Manag. 2021;15:100449. https://doi.org/10.1016/j.enmm.2021.100449 . Ghoneim MM, El-Desoky HS, El-Moselhy KM, Amer A, Abou El-Naga EH, Mohamedein LI, et al. Removal of cadmium from aqueous solution using marine green algae, Ulva lactuca. Egypt J Aquat Res. 2014;40:235–42. https://doi.org/10.1016/j.ejar.2014.08.005 . Bibak M, Sattari M, Tahmasebi S. Investigation of Biosorption Capacity of Algae: Selection of Most Efficient Biosorbent for Metal Removal. Proc Natl Acad Sci India B - Biol Sci. 2023;25:1–10. https://doi.org/10.1007/s40011-023-01524-w . Michalak I, Chojnacka K, Witek-Krowiak A. State of the art for the biosorption process—a review. Appl Biochem Biotechnol. 2013;170:1389–416. https://doi.org/10.1007/s12010-013-0269-0 . Bilal M, Rasheed T, Sosa-Hernández JE, Raza A, Nabeel F, Iqbal HM. Biosorption: an interplay between marine algae and potentially toxic elements—a review. Mar Drugs. 2018;16:65. https://doi.org/10.3390/md16020065 . Saravanan A, Kumar PS, Varjani S, Karishma S, Jeevanantham S, Yaashikaa PR. Effective removal of Cr (VI) ions from synthetic solution using mixed biomasses: kinetic, equilibrium and thermodynamic study. J Water Process Eng. 2021;40:101905. https://doi.org/10.1016/j.jwpe.2020.101905 . Bhagat SK, Tung TM, Yaseen ZM. Development of artificial intelligence for modeling wastewater heavy metal removal: State of the art, application assessment and possible future research. J Clean Prod. 2020;250:119473. https://doi.org/10.1016/j.jclepro.2019.119473 . Fiyadha SS, Alardhi SM, Al Omar M, Aljumaily MM, Al Saadic MA, Fayaedd SS, et al. A comprehensive review on modelling the adsorption process for heavy metal removal from water using artificial neural network technique. Heliyon. 2023;9:e15455. https://doi.org/10.1016/j.heliyon.2023.e15455 . Buaisha M, BALKU Ş, YAMAN ŞÖ. ANN-assisted forecasting of adsorption efficiency to remove heavy metals. Turk J Chem. 2019;43:1407–24. https://doi.org/10.3906/kim-1902-28 . Ahmady-Asbchin S, Andres Y, Gerente C, Cloirec PL. Natural seaweed waste as sorbent for heavy metal removal from solution. Environ Technol. 2009;30:755–62. https://doi.org/10.1080/09593330902919401 . Langmuir I. The adsorption of gases on plane surfaces of glass, mica and platinum. J Am Chem Soc. 1918;40:1361–403. https://doi.org/10.1021/ja02242a004 . Freundlich HMF. Over the adsorption in solution. J Phys Chem. 1906;57:385–470. Al-Asheh S, Banat F, Al-Omari R, Duvnjak Z. Predictions of binary sorption isotherms for the sorption of heavy metals by pine bark using single isotherm data. Chemosphere. 2000;41:659–65. https://doi.org/10.1016/S0045-6535(99)00497-X . Tóth J. Uniform interpretation of gas/solid adsorption. Adv Colloid Interface Sci. 1995;55:1–239. https://doi.org/10.1016/0001-8686(94)00226-3 . Lagergren S. About the theory of so-called adsorption of soluble substances. K Sven Vetensk Akad Handl. 1898;24:1–39. Ho YS, McKay G. Pseudo-second order model for sorption processes. Process Biochem. 1999;34:451–65. https://doi.org/10.1016/S0032-9592(98)00112-5 . Low MJD. Kinetics of chemisorption of gases on solids. Chem Rev. 1960;60:267–312. Aden M, Husson J, Monney S, Franchi M, Knorr M, Euvrard M. Biosorption of Pb (II) ions from aqueous solution using alginates extracted from Djiboutian seaweeds and deposited on silica particles. Pure Appl Chem. 2019;91:459–75. https://doi.org/10.1515/pac-2018-1003 . Rahman MS, Sathasivam KV. Heavy metal biosorption potential of a Malaysian Rhodophyte (Eucheuma denticulatum) from aqueous solutions. Int J Environ Sci Technol. 2016;13:1973–88. https://doi.org/10.1007/s13762-016-1022-3 . Katiyar R, Patel AK, Nguyen TB, Singhania RR, Chen CW, Dong CD. Adsorption of copper (II) in aqueous solution using biochars derived from Ascophyllum nodosum seaweed. Bioresour Technol. 2021;328:124829. https://doi.org/10.1016/j.biortech.2021.124829 . Mullerova S, Baldikova E, Prochazkova J, Pospiskova K, Afarik I. Magnetically modified macroalgae Cymopolia barbata biomass as an adsorbent for safranin O removal. Mater Chem Phys. 2019;225:174–80. https://doi.org/10.1016/j.matchemphys.2018.12.074 . Deniz F, Karabulut A. Biosorption of heavy metal ions by chemically modified biomass of coastal seaweed community: Studies on phycoremediation system modeling and design. Ecol Eng. 2017;106:101–8. https://doi.org/10.1016/j.ecoleng.2017.05.024 . Jayakumar V, Govindaradjane S, Kumar PS, Rajamohan N, Rajasimman M. Sustainable removal of cadmium from contaminated water using green alga–Optimization, characterization and modeling studies. Environ Res. 2021;199:111364. https://doi.org/10.1016/j.envres.2021.111364 . Atugoda T, Gunawardane C, Ahmad M, Vithanage M. Mechanistic interaction of ciprofloxacin on zeolite modified seaweed (Sargassum crassifolium) derived biochar: Kinetics, isotherm and thermodynamics. Chemosphere. 2021;281:130676. https://doi.org/10.1016/j.chemosphere.2021.130676 . Asencios YJO, Parreira LM, Perpetuo EA, Rotta AL. Characterization of seaweeds collected from Baixada Santista litoral, and their potential uses as biosorbents of heavy metal cations. Rev Mex Ing Quim. 2022;21:IA2600–2600. https://doi.org/10.24275/rmiq/IA2600 . Li R, Zhang T, Zhong H, Song W, Zhou Y, Yin X. Bioadsorbents from algae residues for heavy metal ions adsorption: chemical modification, adsorption behaviour and mechanism. Environ Technol. 2021;42:3132–43. https://doi.org/10.1080/09593330.2020.1723711 . Shin WS. Adsorption characteristics of phenol and heavy metals on biochar from Hizikia fusiformis. Environ Earth Sci. 2017;76:1–9. https://doi.org/10.1007/s12665-017-7125-4 . Sooksawat N, Meetam M, Kruatrachue M, Pokethitiyook P, Inthorn D. Equilibrium and kinetic studies on biosorption potential of charophyte biomass to remove heavy metals from synthetic metal solution and municipal wastewater. Bioremediat J. 2016;20:240–51. https://doi.org/10.1080/10889868.2016.1212810 . Silva A, Stawiński W, Romacho J, Santos LH, Figueiredo SA, Freitas OM, et al. Adsorption of fluoxetine and venlafaxine onto the marine seaweed Bifurcaria bifurcata. Environ Eng Sci. 2019;36:573–82. https://doi.org/10.1089/ees.2018.0332 . Moawad MN, El-Sayed AA, El-Naggar NA. Biosorption of cadmium and nickel ions using marine macrophyte, Cymodocea nodosa. Chem Ecol. 2020;36:458–74. https://doi.org/10.1080/02757540.2020.1752199 . Supplementary Files GraphicalAbstract.pdf 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. 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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-4195678","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":295727615,"identity":"8ac9013d-c82b-4510-a02f-5db5c2a6ae55","order_by":0,"name":"P Thamarai","email":"","orcid":"","institution":"Saveetha School of Engineering","correspondingAuthor":false,"prefix":"","firstName":"P","middleName":"","lastName":"Thamarai","suffix":""},{"id":295727616,"identity":"bca79df1-fd13-400e-b1d8-0b6dbdec9d01","order_by":1,"name":"V C Deivayanai","email":"","orcid":"","institution":"Saveetha School of Engineering","correspondingAuthor":false,"prefix":"","firstName":"V","middleName":"C","lastName":"Deivayanai","suffix":""},{"id":295727617,"identity":"8a2958cc-68e5-42e2-a026-a14389b423a4","order_by":2,"name":"S Karishma","email":"","orcid":"","institution":"Saveetha School of Engineering","correspondingAuthor":false,"prefix":"","firstName":"S","middleName":"","lastName":"Karishma","suffix":""},{"id":295727618,"identity":"b9b18582-573f-4cf3-982c-7dafa6a5bebd","order_by":3,"name":"Saravanan Anbalagan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYLACxgYJEMXG/KcCSDEzNxCnhQeohYHnDEgLI1FaGCBaeNtgXDxAvv3sww+MOyzk7Nmbnz2QnFcbzd8O1PKjYhtOLQZn0o0lGM9IGPPwHDM3MNx2PHfGYcYGxp4zt3FrYUhjkGBsk0jskchhk0jcdiy3AaiFmbENtxb5/mfMP8Ba5N+wSRyccyx3PiEtDDfS2KC28LBJNjbU5G4gpMXgxjM2i0SQX86kmUkzHDuQuxGo5SA+v8j3pzHf+LijTo69/fAzaYaautx55w8ffPCjAo/DQCABwTwMJg/gV48K6khRPApGwSgYBSMEAAAas1SBmbzm/wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-5420-8662","institution":"Saveetha School of Engineering","correspondingAuthor":true,"prefix":"","firstName":"Saravanan","middleName":"","lastName":"Anbalagan","suffix":""},{"id":295727619,"identity":"b870d78f-5e4a-4491-807d-adacb21fe4d0","order_by":4,"name":"P. 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Vickram","email":"","orcid":"","institution":"Saveetha School of Engineering","correspondingAuthor":false,"prefix":"","firstName":"A.S.","middleName":"","lastName":"Vickram","suffix":""}],"badges":[],"createdAt":"2024-03-31 12:49:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4195678/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4195678/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55766947,"identity":"f3502000-c62d-4a41-82ae-9196936c35cb","added_by":"auto","created_at":"2024-05-02 20:13:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1137308,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) SEM images of RMSB before adsorption\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b). SEM images of RMSB after adsorption\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4195678/v1/3b11cc4f02ce9a444d38c157.png"},{"id":55766950,"identity":"8e1eed52-1453-454d-950a-42be25497a1d","added_by":"auto","created_at":"2024-05-02 20:13:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":169136,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a). EDX examination of the RMSB before the Cd (II) adsorption\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b). EDX study of RMSB following the surface attachment of Cd (II).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4195678/v1/6146001d2a039a0eacf3e473.png"},{"id":55766946,"identity":"742024e9-1092-44b0-9eea-cd6384ebbcf6","added_by":"auto","created_at":"2024-05-02 20:13:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":108020,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a). FTIR analysis of RMSB before adsorption\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b). FTIR analysis of RMSB after adsorption of Cd (II)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4195678/v1/04bf7802036c0b0e00c902ca.png"},{"id":55766944,"identity":"54fca299-b3d3-4ed9-bf2d-f78f02b30fe3","added_by":"auto","created_at":"2024-05-02 20:13:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eXRD examination of Raw modified seaweed biosorbent\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4195678/v1/d1c0250d4c081dfd13a7406d.png"},{"id":55767721,"identity":"304f587b-8bd4-4f6e-b0ca-b4ef0aefb5b5","added_by":"auto","created_at":"2024-05-02 20:21:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":245194,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a). Influence of pH on Cd (II) ion adsorption by RMSB\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b). The impact of adsorbent dose on Cd (II) adsorption\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(c). Impact of Cd (II) removal by RMSB upon initial concentration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(d). The percentage of Cd (II) ions removed with contact time\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(e). Effect of temperature on the uptake of Cd (II) ions by RMSB\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4195678/v1/1aa49ef3df201c9e009e6783.png"},{"id":55766949,"identity":"41e6f99d-9c36-43ca-b2bc-2d1ddecd3c78","added_by":"auto","created_at":"2024-05-02 20:13:20","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":176362,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParameters of the isotherm model constants for Cd (II) adsorption\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4195678/v1/3e1e2dec2849928e8a5b92e6.jpeg"},{"id":55766952,"identity":"79b732b5-033e-4d78-a653-2acebec11a68","added_by":"auto","created_at":"2024-05-02 20:13:20","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":491584,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRMSB-based kinetic data for Cd (II) biosorption\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4195678/v1/cd5fd8b2e8a52fbe2e322660.jpeg"},{"id":55766945,"identity":"0fbe07a2-804f-4bfc-a3e7-00e1ec9fb836","added_by":"auto","created_at":"2024-05-02 20:13:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":31549,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFit for thermodynamic analysis using RMSB for the removal of Cd (II)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4195678/v1/afc8d3e68dfa5fe81d79f786.png"},{"id":55766951,"identity":"48f63a50-78e6-40a3-a723-1794cdd14a96","added_by":"auto","created_at":"2024-05-02 20:13:20","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1263178,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a). Error histogram for artificial neural network modelling of Cd (II) adsorption by RMSB\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b). Regression plot for Cd (II) ion removal using the Levenberg-Marquardt algorithm's correlation coefficient (R-value)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4195678/v1/1171bc9499ff613e488249f7.png"},{"id":61727722,"identity":"a7fdfdc0-633e-48e5-89d3-ea4feeede950","added_by":"auto","created_at":"2024-08-04 20:36:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4823136,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4195678/v1/613827de-5a33-4077-ac00-8063138fad90.pdf"},{"id":55766953,"identity":"c1992b38-fb9a-4a09-bddf-7be5a441c77c","added_by":"auto","created_at":"2024-05-02 20:13:20","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":201147,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4195678/v1/ba97c1fc681d73c25f288989.pdf"}],"financialInterests":"","formattedTitle":"Effective removal of toxic Cd(II) ions from aqueous solution using mixed macroalgal adsorbent: Kinetics and ANN modeling studies","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWater is an essential resource for life on Earth, as both people and the ecosystem depend on obtaining access to clean water. However, during the past few decades, uncontrolled use of natural resources, fast industrialization, growing urbanization, and a constantly growing population have collectively shown a negative impact on water quality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Since ions containing heavy metals are among the majority of frequently discharged pollutants, they should be considered serious [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although heavy metals are naturally occurring trace elements in aquatic environments, industrial wastes, geochemical structures, mining, and agricultural practices have raised the levels of these elements. Mercury (Hg), cadmium (Cd), copper (Cu), zinc (Zn), silver (Ag), iron (Fe), platinum (Pt), arsenic (As), chromium (Cr), thallium (Ti), and lead (Pb) are a few examples of heavy metals [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Cadmium is one of the most important elements among the metallic contaminants present in wastewater and is frequently discharged by the electroplating, battery, electrode, and metallurgical sectors. The US Environmental Protection Agency (USEPA) has recommended an acceptable concentration of 0.003 mg/L for cadmium in drinking water, but the World Health Organization (WHO) has set a limit contamination threshold of 0.005 mg/L [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHumans have been shown to experience negative effects from low levels of cadmium exposure, including liver disease, bone abnormalities, and renal failure. Metal recovery from aqueous solutions is a difficult task for environmental engineers, and many different approaches use different separation techniques [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Heavy metal removal from wastewater can be accomplished using a variety of standard methods, including electrolytic recovery, evaporation, chemical oxidation, precipitation, chemical coagulation, solidification, and membrane separation. However, because these methods are highly expensive to treat wastewater with low concentrations of heavy metals, their adoption is constrained by several variables, including technological and economic status [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn comparison with alternative traditional treatment techniques, the biosorption process presents potential benefits like reduced operational expenses, reduced amounts of chemical or biological sludge, enhanced efficacy in removing heavy metals from diluted solutions, biosorbent regeneration, the potential for metal recovery, and environmental friendliness [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The novel technique of biosorption extracts hazardous metals from aqueous solutions by utilizing either living or dead biomasses. The two primary mechanisms of biosorption are chemical adsorption and physical adsorption (electrostatic attraction\u0026ndash;Vanderwaal forces of attraction). For the biosorption of metal ions, a variety of biomasses including bacteria, yeast, fungi, and algae have been utilized extensively [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMarine algae have the highest metal binding capabilities of all living materials because their cell walls include lipids, proteins, or polysaccharides. The seaweed's cell walls are made of three different types of biopolymers that give it its biosorption capacity: cellulose, alginate, a heteropolymer made of mannuronic acid and guluronic acid residues, and fucoidan, a heteropolymer composed of sulfated esters of fucose moieties and glucuronic acid. Amino, sulphate, carboxyl, and hydroxyl groups are among the functional groups found in seaweed biomass [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. When compared to single-species biosorbents, mixed seaweed biosorbents have many advantages for the removal of metals. Due to their various surface properties and chemical compositions, several seaweed species when combined can increase the total capacity for binding metals. Furthermore, increased efficiency and specificity in metal removal techniques can result from the synergistic effects between different seaweed components. In addition, mixed seaweed biosorbents frequently have improved stability and durability, ensuring extended use and efficient metal removal over time [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn order to understand the uncertain nonlinear pattern of heavy metal elimination using different treatment procedures, numerous artificial intelligence models have been established. Neural networks, logic, regression, and hybrid models are some of these models. Moreover, these models have been compared to a variety of conventional models, such as mathematical, isothermal, statistical, empirical, and physical models [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To model and optimize contamination removal approaches, these classical tools need to identify a goal for each group of input variables; hence, the target can change while the other variables remain constant at the same time [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Since these surrogate models have primarily been used for adsorption systems with a single pollutant, it is essential to extend their application to the prediction and modeling of adsorption systems with several adsorbates, or multi-component adsorption. Through the use of adsorption systems, scientific and practical models that utilize numerical computational approaches, such as artificial neural networks (ANNs), can greatly enhance the wastewater treatment process generally [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvery year, the tides throw a significant amount of seaweed onto beaches. These organic natural resources are handled as domestic waste since they are waste materials. As of now, researchers have employed a variety of materials to investigate the efficacy of removal from aqueous solutions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. \u003cem\u003eSargassum fusciformis\u003c/em\u003e (brown algae), \u003cem\u003eCodium decorticatum\u003c/em\u003e (green algae), and \u003cem\u003eHypnea valentiae\u003c/em\u003e (red algae) were the seaweeds found around the coast that were selected for this investigation due to their component availability. The goal of this study is to develop a mixed seaweed biosorbent that can effectively remove cadmium (II) ions from aqueous solutions. The study assessed the impact of various parameters on the removal efficiency. The metal removal mechanism and kinetic parameter estimates were determined by kinetic experiments. Thermodynamic studies were used to confirm the sorption's nature and feasibility. Additionally, analysis and optimization of the ANN model were conducted to predict the mixed sorbent's efficiency in removing Cd (II).\u003c/p\u003e"},{"header":"2. Materials and Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Preparation of stock Cd (II) solution\u003c/h2\u003e \u003cp\u003eA solution containing 1000 mg/L of Cd ions is produced by mixing distilled water with a specified quantity of cadmium salt. NaOH (0.1 N) and HCl (0.1 N) have been used to change the adsorbate's pH. The required amount of Cd (II) ions can be obtained by further diluting the resulting stock solution. The total amount of Cd (II) ions was determined through an analysis using an Atomic Absorption Spectrophotometer (AAS).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Preparation, analysis, and characterization of mixed adsorbent\u003c/h2\u003e \u003cp\u003eThe three fresh samples for the research were obtained from the Ramanathapuram region in Tamil Nadu. After being thoroughly cleaned with tap and deionized water to eliminate impurities, freshly obtained samples of brown algae (\u003cem\u003eSargassum fusciformis\u003c/em\u003e), green algae (\u003cem\u003eCodium decorticatum\u003c/em\u003e), and red algae (\u003cem\u003eHypnea valentiae\u003c/em\u003e) have been dried for a day at 70\u0026deg;C in a hot-air oven before being ground in a domestic mixer. \u0026ldquo;Raw Mixed Seaweed Biosorbent\u0026rdquo; (RMSB) was a label provided for the produced biosorbent. The biosorbent was maintained in an oven with humidity control throughout the equilibrium studies.\u003c/p\u003e \u003cp\u003eThe Brunauer Emmett-Teller (BET) nitrogen adsorption/desorption technique was used to determine the pore volume and surface area of RMSB at 77 K. Determining the active sorptive surface locations on the biosorbent's surface can be done with great benefit using scanning electron microscopy. With the aid of elemental dispersive X-ray (EDX) analysis, the elemental composition and mapping of the mixed biosorbent were investigated. By using Fourier Transform Infra-Red (FTIR) spectroscopic analysis, the surface chemistry and functional groups of the biosorbent were deduced. X-ray diffraction spectroscopy (XRD) was utilized to investigate the crystalline quality of the biosorbents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Studies on batch biosorption\u003c/h2\u003e \u003cp\u003eBatch removal studies can be used to evaluate RMSB for Cd (II) removal in an initial way. In batch adsorption experiments, variables \u0026ndash; pH, temperature, time, adsorbent dose, and Cd (II) quantity initially have all been examined as influencing factors. Each 250 mL Erlenmeyer flask in the series used for the investigation held 100 ml of the cadmium solution together with the necessary amount of adsorbent. In a shaking incubator with temperature control, the heavy metal solution was agitated at a speed of 100 revolutions per minute for different durations. After a predetermined amount of time, the cadmium solution flasks were taken out of the incubator. The amount of Cd (II) ion elimination percentage (%) can be calculated in the equation below.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$Cd \\left(II\\right) removal= \\frac{\\left({C}_{i}- {C}_{f}\\right)}{{C}_{i}} \\times 100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhereas C\u003csub\u003ef\u003c/sub\u003e indicates the final Cd (II) solution concentration, C\u003csub\u003ei\u003c/sub\u003e indicates initial Cd (II) concentration in this equation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Investigation of Isotherm and kinetics\u003c/h2\u003e \u003cp\u003eOptimizing adsorption systems requires the use of adsorption isotherms, which are equilibrium data and adsorption parameters that show the interactions between pollutants and adsorbent materials. Initial Cd (II) concentrations were altered in the range of 20 to 160 mg/L for equilibrium bio-sorption tests, while other variables were held constant. Each flask was filled with the proper dosage of adsorbent material, which was then kept there for the perfect amount of time to reach equilibrium. The samples were removed from the incubator at the specified periods. Using AAS, the amount of Cd (II) left in the filtrate after the solution was filtered was determined. The amount of removed Cd (II) was computed using the following formula.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${q}_{e }= \\frac{\\left({C}_{i } - {C}_{e}\\right)V}{m}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe capacity for equilibrium adsorption (qe), The quantity of mixed biomass is indicated by \"m,\" while the equilibrium concentration of Cd (II) ions is regarded by Ce and the volume of the cadmium solution is represented by V. In the present research, the adsorption experimental data was fitted utilizing four distinct adsorption isotherm models.\u003c/p\u003e \u003cp\u003eA description of Langmuir's model is given by [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e],\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${q}_{e }= \\frac{{q}_{m }{K}_{L }{C}_{e}}{1+ {K}_{L }{C}_{e}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, q\u003csub\u003em\u003c/sub\u003e stands for Langmuir monolayer sorption ability and K\u003csub\u003eL\u003c/sub\u003e for Langmuir energy constant.\u003c/p\u003e \u003cp\u003eThe overview of the Freundlich model is as follows [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e],\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${q}_{e }= {K}_{F}{C}_{e}^{\\frac{1}{n}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe system's capacity and sorption intensity are connected with 1/n and KF, respectively. The term (1/n) measurement yields a favorability indicator for the sorbent/adsorbate systems.\u003c/p\u003e \u003cp\u003eThe following equation has investigated the Sips isotherm [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e],\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$${q}_{e }= \\frac{{K}_{S}{{C}_{S}}^{{B}_{S}}}{1+{\\alpha }_{S}{{C}_{S}}^{{B}_{S}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere β\u003csub\u003eS\u003c/sub\u003e is the Sips isotherm exponent, K\u003csub\u003eS\u003c/sub\u003e, and α\u003csub\u003eS\u003c/sub\u003e are the Sips isotherm model constant (Lg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003eToth modeling has been examined by [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e],\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${q}_{e }= \\frac{{q}_{m }{C}_{e}}{{\\left({K}_{T0}+ {C}_{e}^{tn}\\right)}^{\\raisebox{1ex}{$1$}\\!\\left/ \\!\\raisebox{-1ex}{$tn$}\\right.}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, t\u003csub\u003en\u003c/sub\u003e is the Toth isotherm exponent; C\u003csub\u003ee\u003c/sub\u003e\u003csup\u003etn\u003c/sup\u003e denotes binding affinity constant.\u003c/p\u003e \u003cp\u003eKinetic analysis can be used to evaluate the rate of sorption mechanism involved in the removal by RMSB. The calculation of Cd (II) ions extracted from the adsorbent material at different time intervals, the following equation has been used.\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$${q}_{t}= \\frac{\\left({C}_{0}-{C}_{t}\\right)V}{m}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, Ct stands for the Cd (II) concentration following a specific time.\u003c/p\u003e \u003cp\u003eIn order to investigate the mechanism of Cd (II) adsorption onto RMSB, the study examined pseudo-first-order, pseudo-second-order, and Elovich kinetic models. The rate expression of the pseudo-first-order reaction can be stated as follows [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]:\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$${q}_{t }= {q}_{e}\\left(1-\\text{exp}\\left(-{k}_{1}t\\right)\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe pseudo-second-order kinetics model is described below [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e],\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$${q}_{t}=\\frac{\\left({q}_{e }^{2}{k}_{2}t\\right)}{\\left(1+ {q}_{e}{k}_{2}t\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe definition of the Elovich Kinetic model is as follows [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]:\u003cdiv id=\"Equ10\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e\n$${q}_{t}= \\left(1+{\\beta }_{E}\\right)In\\left(1+{\\alpha }_{E }{\\beta }_{E}t\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this case, α\u003csub\u003eE\u003c/sub\u003e represents the initial adsorption rate, and k\u003csub\u003e1\u003c/sub\u003e, k\u003csub\u003e2\u003c/sub\u003e, and β\u003csub\u003eE\u003c/sub\u003e denote the pseudo-first order, pseudo-second order, and desorption constants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Thermodynamics\u003c/h2\u003e \u003cp\u003eThermic characteristics and spontaneity of the process of elimination have been determined by evaluating thermodynamic parameters. The following formulas were used to determine the thermodynamic parameters:\u003cdiv id=\"Equ11\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ11\" name=\"EquationSource\"\u003e\n$${K}_{C }=\\frac{{C}_{Ae}}{{C}_{e}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e11\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ12\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ12\" name=\"EquationSource\"\u003e\n$$\\varDelta {G}^{^\\circ }= -RTIn{K}_{C}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e12\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ13\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ13\" name=\"EquationSource\"\u003e\n$$Log{K}_{c}= \\frac{\\varDelta S^\\circ }{2.303R}- \\frac{\\varDelta H^\\circ }{2.303RT}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e13\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere R is the ideal gas constant (8.314 J/molK) and T is the Kelvin temperature of adsorption. The equilibrium concentration of Cd (II) in the solution is C\u003csub\u003ee\u003c/sub\u003e, and the amount of Cd (II) adsorbed on the adsorbent per litre of the solution at equilibrium is C\u003csub\u003eAe\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Artificial Neural Network (ANN) Model\u003c/h2\u003e \u003cp\u003eArtificial neural network prediction was performed using experimental data sets for the Cd (II) sorption process by raw mixed seaweed adsorbent. Approximately 264 experimental findings were used in the ANN process since the ANN model fits and performs effectively with huge numbers of data in data sets. The ANN analysis is trained and cross-validated using a second-order algorithm called Levenberg-Marquardt (LM). To achieve improved convergence behaviour, LM algorithm makes use of the second-order derivatives of the mean squared error between the predicted and actual outputs. The neural network fitting process has been carried out using MATLAB 2022a software. The neural network was composed of input, output, and neural network training layers. The percentage of elimination was taken into consideration as an output variable, while the absorbed dosage, contact time, pH, and temperature were used as input elements or variables. Ideal neuron numbers has been determined by trial and error. 30% of the entire set of data, were used for validating and testing the neural network model, while the remaining 70% were used for network training. When the network reaches the threshold value or the minimal value of the cross-validation MSE, optimization is achieved.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characterization studies of biosorbent\u003c/h2\u003e \u003cp\u003eBET area of mixed biosorbent was calculated using N\u003csub\u003e2\u003c/sub\u003e sorption-desorption data. The average specific surface area was calculated using BET analysis, and the average pore diameter and pore volume were calculated using Barrett\u0026ndash;Joyner\u0026ndash;Halenda (BJH) analysis. According to the results, the surface area, pore volume, and pore diameter under the BJH pore size distribution were approximated and determined to be 3.003 m\u003csup\u003e2\u003c/sup\u003e/g, 0.007 cc/g, and 3.968 nm. Surface area of the sample was 1.777 m\u003csup\u003e2\u003c/sup\u003e/g under multipoint BET. Methods, properties, and values for BET analysis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eBET analysis of RMSB\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethods\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProperties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValues\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBJH pore size distribution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurface area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.003 m\u003csup\u003e2\u003c/sup\u003e/g\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePore volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007 cc/g\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePore diameter Dv (d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.968 nm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulti-point BET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurface area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.777 m\u003csup\u003e2\u003c/sup\u003e/g\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\u003eSEM images of RMSB indicating morphological changes before and after adsorption are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e(a) and 1(b). Before Cd (II) adsorption, the SEM images of RMSB show a diverse surface morphology with irregularities and roughness. The surface seems to be comparatively smooth, with random pores and fissures. SEM images demonstrate noticeable alterations in surface morphology following adsorption. The formation of irregular aggregates and increased roughness characterize the surface as it becomes more textured. This suggests that Cd (II) ions interact with the biosorbent since pores and fissures appear to be more noticeable. Clusters or aggregates of Cd (II) ions are visible and adhere to the biosorbent matrix. Effective adsorption across the surface is indicated by the homogenous distribution of Cd (II) ions, which suggests efficient utilization of active binding sites throughout the RMSB surface.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a) and 2(b) illustrate the elemental composition determined by EDX analysis of RMSB before and after adsorption. The elemental compositions of the raw mixed seaweed biosorbent, as inferred by the EDX analysis, are C (28.35%), O (39.89%), and Na (14.40%), indicating that carbon is the major element, followed by oxygen and sodium. The natural components of seaweed biomass, where carbon and oxygen are important elements of organic matter, are apparent in this composition. The carbon content of the biosorbent noticeably increases with Cd (II) ion adsorption, indicating that the ions have bound to the carbonaceous sites in the biomass. The effective sorption of Cd (II) ions onto the biosorbent surface is shown by this rise in carbon content. Furthermore, a trace amount of nitrogen is found, which may be explained by the binding of Cd (II) ions to functional groups in the seaweed biomass that contain nitrogen.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe presence of the functional group on the surface of the mixed seaweed biosorbent can be determined using the FTIR technique. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e(a) and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e(b) depict FTIR images of RMSB before and after being loaded with heavy metal Cd (II). FTIR spectroscopy showed that the different functional groups of the biosorbents were examined before adsorption, and those were O-H, N-H, C-H, C\u0026thinsp;\u0026equiv;\u0026thinsp;C, C\u0026thinsp;=\u0026thinsp;O, C\u0026thinsp;=\u0026thinsp;C, and M-O. A significant peak located at 4455.56cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e has been attributed to the intermolecular O-H stretching vibrations of hydroxyl groups that occur in alcohols or phenols. After the adsorption of Cd (II) ions, peaks such as 2274.07 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 2054.19 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e changed from 2270.22 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 2065.76 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, which would constitute an indication of modifications to the C\u0026thinsp;\u0026equiv;\u0026thinsp;C stretching vibrations. Following adsorption, the majority of the peaks were linked to stretching vibrations of metal-oxygen (M-O). Peak shifts indicate interactions between the mixed seaweed adsorbent's functional groups and Cd (II) ions. After adsorption, some peaks that were there formerly disappeared or significantly diminished. The existence of Cd (II) ions on the surface of the seaweed adsorbent is indicated by the appearance or changes of peaks associated with metal-oxygen stretching vibrations. This shows that the Cd (II) ions have been successfully adsorbed onto the adsorbent material.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe XRD diffractogram of RMSB is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Based on the known 2θ values, the d-spacing for each peak was calculated using the standard X-ray wavelength of 1.5406 \u0026Aring;. Following that, through analysis and comparison with known patterns, these d-spacing values became related to certain crystallographic planes. The detected peaks imply that the seaweed biosorbent has a variety of crystalline phases. It is possible to attribute the peaks at 27.434\u0026deg;, 28.520\u0026deg;, and 31.704\u0026deg; to different cellulose or hemicellulose components which have plane correlations of (002), (200), and (020). The presence of additional crystalline phases, probably connected to minerals like calcite, aragonite, or other carbonate compounds, is suggested by the peaks at 40.533\u0026deg;, 42.559\u0026deg;, 43.572\u0026deg;, and 45.526\u0026deg;. Higher angles are associated with the peaks at 56.454\u0026deg;, 66.223\u0026deg;, 75.342\u0026deg;, and 83.881\u0026deg;; these points may represent additional mineral phases, such as silica, quartz, or other silicates, or more sophisticated crystalline structures. These peaks might also be signs of the existence of particular crystalline organic molecules.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Performance of batch adsorption\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Solution pH\u003c/h2\u003e \u003cp\u003eThe impact of pH on the removal efficiency of RMSB is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003e(a). The elimination percentages at different pH values showed that biosorption effectiveness is strongly pH-dependent. The findings show that until it reaches an optimal level, the removal effectiveness of RMSB rises with increasing pH. After that, it begins to decrease. Elimination efficiency is comparatively poor at acidic pH values (pH 1 and pH 2) (20.62% and 50.98%, respectively). The protonation of active sites on RMSB clarifies that this leads to a reduction in the number of binding sites that are available for the target contaminants. A significant increase in removal efficiency occurs when pH rises from acidic to neutral (pH 3 to pH 7), suggesting that biosorption is likely under favourable conditions. The higher electrostatic interaction between the biosorbent and the pollutant ions can be ascribed because of deprotonation in functional groups of biosorbent. pH 5 (90.25%), or almost neutral conditions, is where the maximum removal effectiveness is found. The RMSB's surface charge is probably optimized at this pH, which makes it easier for contaminants to be adsorbed. A minor drop in removal effectiveness is seen above pH 5, which may be brought on by hydroxide ions and pollutant ions competing for active sites on the surface of the biosorbent.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Biosorbent Dose\u003c/h2\u003e \u003cp\u003eThe influence of the adsorbent dose on the removal of Cd (II) ions is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003e(b). The removal efficiency significantly improved when the adsorbent dosage varied between 0.5 g/L and 1 g/L, and removal rising between 24.32% and 59.82%. This significant improvement can be ascribed to the larger concentration of adsorbent material in the solution, which increases the availability of adsorption sites. A range of 1 g/L to 2 g/L of adsorbent was added, and the removal efficiency increased noticeably to 92.765%. This implies that there were still accessible active sites for adsorption and that the RMSB's adsorption capacity had not yet reached saturation. However, the improvement in removal efficiency was not as significant at 2 g/L. In comparison to the greatest efficiency attained at 2.5 g/L (92.34%), there was a minor drop in removal efficiency at 3 g/L (90.75%) and 3.5 g/L (90.24%). This phenomenon could be explained by the adsorbent particles aggregating at higher dosages, which would reduce the effective surface area that is accessible for adsorption. The pattern that has been seen suggests that there is a dosage of adsorbent that is ideal for effectively removing the desired contaminant. The elimination efficiency is maximized at a dosage of about 2 g/L, which seems to be the ideal one in this instance. Increasing the adsorbent dosage after this point might not significantly improve removal efficiency and might even have the opposite effect.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Initial Concentration\u003c/h2\u003e \u003cp\u003eThe removal efficiency, which ranges from 92.53\u0026ndash;94.55%, is still strong at lower values (20\u0026ndash;60 mg/L). However, there is a noticeable decrease in removal efficiency as the starting concentration rises above 80 mg/L. This implies that at higher initial Cd (II) concentrations, the RMSB's biosorption capacity reaches saturation. Initial concentration impact over Cd (II) ions removal has been given in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003e(c). There are multiple reasons for the decline in removal efficiency at greater doses. First, the accessible binding sites on the biosorbent get saturated more quickly when the concentration of Cd (II) in the solution rises, which reduces the biosorbent's adsorption capacity per unit mass. Higher concentrations can also result in a reduction in the overall removal efficiency because of the increased competition between Cd (II) ions for binding sites on the biosorbent surface. The limitations of utilizing RMSB for the removal of large quantities of Cd (II) from aqueous solutions are highlighted by the rapid drop in removal effectiveness shown at concentrations above 100 mg/L.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Contact Time\u003c/h2\u003e \u003cp\u003eTo determine the adsorption rate in a batch process, contact time investigations were performed. The contact period for RMSB was varied during the tests, ranging from 10 to 80 minutes. Figure\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003e(d) demonstrates the impact of contact time on the batch process for RMSB, respectively. Initially, the removal capabilities of RMSB were comparatively poor, at a contact duration of 10 minutes. This can be understood by the fact that the biosorbent did not have enough time to form efficient interactions with the target pollutants in the solution. The removal capabilities showed a continuous increase with increasing contact time, suggesting that pollutants were gradually adsorbed into the RMSB surface. The removal capacities were maximized at 50 minutes of contact time, indicating that the adsorption and desorption processes had achieved equilibrium. Prolonged contact times do not considerably improve the adsorption performance of RMSB; after 50 minutes, the removal efficiency tends to flatten or even slightly decline.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.2.5 Temperature\u003c/h2\u003e \u003cp\u003eThe influence of temperature on RMSB is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003e(e). With a range of 20 mg/L \u0026minus;\u0026thinsp;160 mg/L, the concentrations of Cd (II) ions were investigated at several different solution temperatures (303K, 313K, 323K, and 333K). As the initial concentration of pollutants increases, it is common to notice a decline in removal effectiveness during biosorption procedures. This is most likely because to the biosorbent's restricted ability to absorb pollutants at greater concentrations. The effectiveness of removal usually increases with increasing temperature as the initial concentration fluctuates, up to a certain point, at which point higher temperatures lead to a decrease in removal efficiency. The mentioned behaviour might be attributed to the less favourable adsorption process at lower temperatures, which is caused by a decline in molecule kinetic energy. However, elevated temperatures may cause excessive thermal agitation that disrupts adsorption or induces desorption of pollutants that have already been adsorbed. Based on similar findings across all initial concentrations examined, it appears that 303K is the optimum temperature for removing pollutants using RMSB. When compared to other examined temperatures, this temperature shows higher removal efficiency due to the biosorbent's superior adsorption characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Modeling studies\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Adsorption isotherms\u003c/h2\u003e \u003cp\u003eThe relationship between the equilibrium concentration of Cd (II) in aqueous solution (C\u003csub\u003ee\u003c/sub\u003e) and the amount of adsorbed Cd (II) on biosorbent in equilibrium (q\u003csub\u003ee\u003c/sub\u003e) is represented by the isotherm. In this work, the Langmuir, Freundlich, sips, and Toth equilibrium isotherm models were investigated. Parameters of four isotherm models involving the RMSB in batch sorption investigations are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the isotherm model fits for this current study. The best-fitting model for the current sorption system has been identified based on lower SSE (Sum of squared errors) and RMSE (Root mean squared error) values and better correlation coefficients, or R\u003csup\u003e2\u003c/sup\u003e values. The experimental adsorption data described best-fit for Cd (II) sorption among four-parameter isotherm models utilized in this investigation as follows: Freundlich\u0026thinsp;\u0026gt;\u0026thinsp;Langmuir\u0026thinsp;\u0026gt;\u0026thinsp;Toth\u0026thinsp;\u0026gt;\u0026thinsp;Sips. The Cd (II) sorption process on RMSB biosorbent was found to obey the Freundlich isotherm, according to the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) value of 0.9708 showing the maximum adsorption capacity of 146.2 mg/g. Additionally, the value of parameter \"n\" in a Freundlich isotherm indicates the intensity of adsorption through the Freundlich exponent. A value of \"n\" greater than 1 denotes both the strong binding of Cd (II) onto RMSB and the well-fitting of the Freundlich model to the sorption system. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displayed the maximum sorption capacity of RMSB in comparison to other seaweed-based biosorbents.\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\u003eIsotherm model constants for the adsorption of Cd (II)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsotherm Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eK\u003csub\u003eL\u003c/sub\u003e(L/mg)\u0026thinsp;=\u0026thinsp;0.5356\u003c/p\u003e \u003cp\u003eq\u003csub\u003em\u003c/sub\u003e (mg/g)\u0026thinsp;=\u0026thinsp;146.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreundlich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eK\u003csub\u003eF\u003c/sub\u003e[(mg/g) (L/mg) \u003csup\u003e(1/n)\u003c/sup\u003e]\u0026thinsp;=\u0026thinsp;39.94\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;4.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.9708\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eq\u003csub\u003em\u003c/sub\u003e (mg/g)\u0026thinsp;=\u0026thinsp;19.085\u003c/p\u003e \u003cp\u003eK\u0026thinsp;=\u0026thinsp;0.0584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSips\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKs(L/mg)\u0026thinsp;=\u0026thinsp;0.233\u003c/p\u003e \u003cp\u003eqm (mg/g)\u0026thinsp;=\u0026thinsp;88.2\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.715\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\u003e \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\u003eComparison of the maximal adsorption capabilities of various seaweed adsorbents for the removal of heavy metal ions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeaweed adsorbent utilized\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBest fitted model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eqm (mg/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSargassum sp.\u003c/em\u003e and \u003cem\u003eTurbinaria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSips\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEucheuma denticulatum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e416.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAscophyllum nodosum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCymopolia barbata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRaw mixed seaweed biosorbent (RMSB)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFreundlich\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e146.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePresent study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChaetomorpha\u003c/em\u003e sp., \u003cem\u003ePolysiphonia\u003c/em\u003e sp., \u003cem\u003eUlva\u003c/em\u003e sp.\u003c/p\u003e \u003cp\u003eand \u003cem\u003eCystoseira\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSips\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCaulerpa scalpelliformis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSargassum crassifolium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreundlich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUlva lactuca\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreundlich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSargassum carpophyllum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHizikia fusiformis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChara aculeolata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBifurcaria bifurcata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreundlich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCymodocea nodosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Adsorption kinetic modeling\u003c/h2\u003e \u003cp\u003eThe appropriate design of wastewater treatment equipment operating in continuous mode requires research on removal rate. The process of adsorption mechanism and the step that regulates the rate of adsorption can be determined through the study of kinetics. This helps with industrial-scale scaling and upholding ideal conditions during continuous operations. Three models have been used in this study to analyze the experimental data and forecast the adsorption kinetics mechanism of cadmium ions: pseudo-first-order kinetics, Elovich kinetics, and pseudo-second-order kinetics. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides the parameters of each model with their corresponding R\u003csup\u003e2\u003c/sup\u003e values for each unique initial concentration of Cd (II) solutions. Figure\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e7\u003c/span\u003e depicts the kinetic models for the adsorption of Cd (II) ions by RMSB. The order of fitness of the three models for cadmium sorption using RMSB is determined to be pseudo-first order\u0026thinsp;\u0026gt;\u0026thinsp;pseudo-second order\u0026thinsp;\u0026gt;\u0026thinsp;Elovich based on error % and correlation coefficients (R\u003csup\u003e2\u003c/sup\u003e). The pseudo-first-order model's applicability provided evidence that the adsorption rate is dependent on adsorption capacity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters of the pseudo-first-order, pseudo-second-order, and Elovich kinetic models for the biosorption of Cd (II) using RMSB\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConc.\u003c/p\u003e \u003cp\u003e(mg/mL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePseudo-first-order\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePseudo-second-order\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eElovich\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eqe\u003c/p\u003e \u003cp\u003e(mg/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ek\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eqe\u003c/p\u003e \u003cp\u003e(mg/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ek\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(g/mg min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eαE\u003c/p\u003e \u003cp\u003e(mg/g min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eβE\u003c/p\u003e \u003cp\u003e(g/mg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.9024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.8911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.7384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.8839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.4302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.8981\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.8903\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.8834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.8798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0006917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.8869\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\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Biosorption Thermodynamics\u003c/h2\u003e \u003cp\u003eSince energy cannot be gained or lost, the fundamental principle of thermodynamics states that the only force acting upon an isolated system is a shift in entropy. Energy and entropy concerns need to be considered into account during ecological engineering practice in order to determine which process will arise spontaneous. Thermodynamic factors in biosorption of Cd (II) onto RMSB were computed. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays the Gibbs free energy values that change during the biosorption process. Figure\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003e further supports the thermodynamic nature of Cd (II) adsorption. The findings show that these values fluctuate in a negative direction. The values of ΔG\u0026deg; decreased with an increase in temperature, suggesting the spontaneous nature of biosorption for Cd (II), and the negative ΔG\u0026deg; values of Cd (II) at various temperatures revealed that the biosorption process is spontaneous. It is implied that the process is exothermic by negative ΔH\u0026deg; values.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThermodynamic parameters for RMSB-mediated Cd (II) adsorption\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=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eRMSB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c3\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c11\" namest=\"c4\"\u003e \u003cp\u003eInitial Cd (II) concentration (mg/L)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c2\" namest=\"c1\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eΔG\u0026deg; (kJ/mol)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e303 K\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.6867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e313 K\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.359\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e323 K\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e333 K\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eΔH\u0026deg; (kJ/mol)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-53.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-49.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-46.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-33.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-29.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-26.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-20.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-18.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eΔS\u0026deg; (J/mol/K)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-141.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-134.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-128.2571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-91.3108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-81.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-76.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-58.7625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-54.881\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\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4 ANN Modeling\u003c/h2\u003e \u003cp\u003eThe developed artificial neural network (ANN) model has the potential to characterize the behaviour of a complex process, given the parameters of the experimental situation. The current study develops an artificial neural network (ANN) to remove Cd (II) ions from an aqueous solution using RMSB. Using the Levenberg-Marquardt backpropagation method (trainlm), neural network training was carried out. With 70% of the data used for training and the remaining 30% for validation and testing, 264 data points were employed in neural network fitting. 70% of the data are used as inputs in training for predicting the output percentage of removal. The accuracy of the model has been tested using a multilayer perceptual network with four different input variables: pH, RMSB dosage, contact time, and temperature. The experimental data has been divided to prevent over-parameterization and training. Using the trial-and-error procedure, a range of neurons, from 5 to 30, were evaluated. The algorithm determines the weight changes by determining the total training error for a given epoch. Every epoch, the weights are changed to a minimal value that is randomly selected by adding or subtracting from the weights that occurred previously and these were recorded and stored at a specific synapse. The performance plot displays differences in MSE and the number of epochs. The fifth epoch iterations of the ANN produced the lowest validation mean square error. Figure\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e9\u003c/span\u003e(a) shows the error histogram for the adsorption of Cd (II) ions. Plots for the experimental and predicted outcomes for the training, testing, and validation datasets are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e9\u003c/span\u003e(b). For training, validation, testing, and all experimental data, the corresponding correlation coefficients were 0.9596, 0.94396, 0.90449, and 0.94679. The MSE for training the ANN model was less than 0.005. The model fitness was appropriate because all of the correlation coefficients were within proximity of one another. The ANN model's predicted outcomes matched the experimental target data set.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe linear fit model obtained from validation outputs is displayed in the following equation: Y vs. T in the ANN model.\u003cdiv id=\"Equ14\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ14\" name=\"EquationSource\"\u003e\n$$Y=0.87\\left(Target\\right)+8$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e14\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eIn the present study, Raw mixed seaweed biosorbent (RMSB) has been utilized for the removal of Cd (II) ions from the aqueous solution. Phase identification, surface area, porous nature, and functional groups involved in the removal of metal ions from aqueous solutions were examined using BET, SEM-EDX, XRD, and FTIR techniques. The optimal ranges of various operating parameters for Cd (II) adsorption on RMSB has been found as pH- of 5, time-50 min, temperature 303 K, and adsorbent dosage of 2 g/L. The adsorption process experimental results showed significant correlations with Freundlich model alongwith the maximal adsorption capacity \u0026minus;\u0026thinsp;146.2 mg/g. The nature of physisorption was confirmed by the pseudo-first-order model, which described the biosorption of cadmium. Thermodynamic analysis revealed that the process is essentially exothermic, with negative entropy (ΔS\u0026deg;) and enthalpy changes (ΔH\u0026deg;). The modeling approach utilizing ANN showed a great deal of effectiveness in representing the adsorption system using RMSB. During the training phase, the Levenberg-Marquardt (LM) method was used to determine the ideal topology for the ANN. The trained ANN model performed better in predicting the Cd (II) removal process than the chosen model, as evidenced by the strong correlation coefficient of 0.94679 for the Cd (II) removal onto RMSB. For the removal of metal ions from waste water streams, RMSB has been proven to be a potential biosorbent.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive support or funding from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWriting, Editing, and Drafting- P. Thamarai, V. C. Deivayanai, S. Karishma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConceptualization, Visualization, Supervision, and Methodology - A. Saravanan, P.R. Yaashikaa, A.S. Vickram\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not contain any studies with human or animal subjects performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhirvar BP, Das P, Srivastava V, Kumar M. Perspectives of heavy metal pollution indices for soil, sediment, and water pollution evaluation: An insight. Total Environ Res Themes. 2023;6:100039. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.totert.2023.100039\u003c/span\u003e\u003cspan address=\"10.1016/j.totert.2023.100039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZamora-Ledezma C, Negrete-Bolagay D, Figueroa F, Zamora-Ledezma E, Ni M, Alexis F, et al. Heavy metal water pollution: A fresh look about hazards, novel and conventional remediation methods. Environ Technol Innov. 2021;22:101504. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eti.2021.101504\u003c/span\u003e\u003cspan address=\"10.1016/j.eti.2021.101504\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalik LA, Bashir A, Qureashi A, Pandith AH. Detection and removal of heavy metal ions: a review. Environ Chem Lett. 2019;17:1495\u0026ndash;521. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10311-019-00891-z\u003c/span\u003e\u003cspan address=\"10.1007/s10311-019-00891-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBabuji P, Thirumalaisamy S, Duraisamy K, Periyasamy G. Human health risks due to exposure to water pollution: a review. Water. 2023;15:2532. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w15142532\u003c/span\u003e\u003cspan address=\"10.3390/w15142532\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBharti R, Sharma R. Effect of heavy metals: An overview. Mater Today Proc. 2022;51: 880\u0026ndash;885. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.matpr.2021.06.278\u003c/span\u003e\u003cspan address=\"10.1016/j.matpr.2021.06.278\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePyrzynska K. Removal of cadmium from wastewaters with low-cost adsorbents. J Environ Chem Eng. 2019;7:102795. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jece.2018.11.040\u003c/span\u003e\u003cspan address=\"10.1016/j.jece.2018.11.040\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrishnan S, Zulkapli NS, Kamyab H, Taib SM, Din MFBM, Abd Majid Z, et al. Current technologies for recovery of metals from industrial wastes: An overview. Environ Technol Innov. 2021;22:101525. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eti.2021.101525\u003c/span\u003e\u003cspan address=\"10.1016/j.eti.2021.101525\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarolin CF, Kumar PS, Saravanan A, Joshiba GJ, Naushad M. Efficient techniques for the removal of toxic heavy metals from aquatic environment: A review. J Environ Chem Eng. 2017;5:2782\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jece.2017.05.029\u003c/span\u003e\u003cspan address=\"10.1016/j.jece.2017.05.029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerma A, Agarwal M, Sharma S, Singh N. Competitive removal of cadmium and lead ions from synthetic wastewater using Kappaphycus striatum. Environ Nanotechnol Monit Manag. 2021;15:100449. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enmm.2021.100449\u003c/span\u003e\u003cspan address=\"10.1016/j.enmm.2021.100449\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhoneim MM, El-Desoky HS, El-Moselhy KM, Amer A, Abou El-Naga EH, Mohamedein LI, et al. Removal of cadmium from aqueous solution using marine green algae, Ulva lactuca. Egypt J Aquat Res. 2014;40:235\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejar.2014.08.005\u003c/span\u003e\u003cspan address=\"10.1016/j.ejar.2014.08.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBibak M, Sattari M, Tahmasebi S. Investigation of Biosorption Capacity of Algae: Selection of Most Efficient Biosorbent for Metal Removal. Proc Natl Acad Sci India B - Biol Sci. 2023;25:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40011-023-01524-w\u003c/span\u003e\u003cspan address=\"10.1007/s40011-023-01524-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichalak I, Chojnacka K, Witek-Krowiak A. State of the art for the biosorption process\u0026mdash;a review. Appl Biochem Biotechnol. 2013;170:1389\u0026ndash;416. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12010-013-0269-0\u003c/span\u003e\u003cspan address=\"10.1007/s12010-013-0269-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBilal M, Rasheed T, Sosa-Hern\u0026aacute;ndez JE, Raza A, Nabeel F, Iqbal HM. Biosorption: an interplay between marine algae and potentially toxic elements\u0026mdash;a review. Mar Drugs. 2018;16:65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/md16020065\u003c/span\u003e\u003cspan address=\"10.3390/md16020065\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaravanan A, Kumar PS, Varjani S, Karishma S, Jeevanantham S, Yaashikaa PR. Effective removal of Cr (VI) ions from synthetic solution using mixed biomasses: kinetic, equilibrium and thermodynamic study. J Water Process Eng. 2021;40:101905. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jwpe.2020.101905\u003c/span\u003e\u003cspan address=\"10.1016/j.jwpe.2020.101905\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhagat SK, Tung TM, Yaseen ZM. Development of artificial intelligence for modeling wastewater heavy metal removal: State of the art, application assessment and possible future research. J Clean Prod. 2020;250:119473. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2019.119473\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2019.119473\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiyadha SS, Alardhi SM, Al Omar M, Aljumaily MM, Al Saadic MA, Fayaedd SS, et al. A comprehensive review on modelling the adsorption process for heavy metal removal from water using artificial neural network technique. Heliyon. 2023;9:e15455. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2023.e15455\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2023.e15455\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuaisha M, BALKU Ş, YAMAN Ş\u0026Ouml;. ANN-assisted forecasting of adsorption efficiency to remove heavy metals. Turk J Chem. 2019;43:1407\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3906/kim-1902-28\u003c/span\u003e\u003cspan address=\"10.3906/kim-1902-28\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmady-Asbchin S, Andres Y, Gerente C, Cloirec PL. Natural seaweed waste as sorbent for heavy metal removal from solution. Environ Technol. 2009;30:755\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09593330902919401\u003c/span\u003e\u003cspan address=\"10.1080/09593330902919401\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLangmuir I. The adsorption of gases on plane surfaces of glass, mica and platinum. J Am Chem Soc. 1918;40:1361\u0026ndash;403. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/ja02242a004\u003c/span\u003e\u003cspan address=\"10.1021/ja02242a004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreundlich HMF. Over the adsorption in solution. J Phys Chem. 1906;57:385\u0026ndash;470.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Asheh S, Banat F, Al-Omari R, Duvnjak Z. Predictions of binary sorption isotherms for the sorption of heavy metals by pine bark using single isotherm data. Chemosphere. 2000;41:659\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0045-6535(99)00497-X\u003c/span\u003e\u003cspan address=\"10.1016/S0045-6535(99)00497-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT\u0026oacute;th J. Uniform interpretation of gas/solid adsorption. Adv Colloid Interface Sci. 1995;55:1\u0026ndash;239. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0001-8686(94)00226-3\u003c/span\u003e\u003cspan address=\"10.1016/0001-8686(94)00226-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLagergren S. About the theory of so-called adsorption of soluble substances. K Sven Vetensk Akad Handl. 1898;24:1\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHo YS, McKay G. Pseudo-second order model for sorption processes. Process Biochem. 1999;34:451\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0032-9592(98)00112-5\u003c/span\u003e\u003cspan address=\"10.1016/S0032-9592(98)00112-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLow MJD. Kinetics of chemisorption of gases on solids. Chem Rev. 1960;60:267\u0026ndash;312.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAden M, Husson J, Monney S, Franchi M, Knorr M, Euvrard M. Biosorption of Pb (II) ions from aqueous solution using alginates extracted from Djiboutian seaweeds and deposited on silica particles. Pure Appl Chem. 2019;91:459\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/pac-2018-1003\u003c/span\u003e\u003cspan address=\"10.1515/pac-2018-1003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman MS, Sathasivam KV. Heavy metal biosorption potential of a Malaysian Rhodophyte (Eucheuma denticulatum) from aqueous solutions. Int J Environ Sci Technol. 2016;13:1973\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13762-016-1022-3\u003c/span\u003e\u003cspan address=\"10.1007/s13762-016-1022-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatiyar R, Patel AK, Nguyen TB, Singhania RR, Chen CW, Dong CD. Adsorption of copper (II) in aqueous solution using biochars derived from Ascophyllum nodosum seaweed. Bioresour Technol. 2021;328:124829. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biortech.2021.124829\u003c/span\u003e\u003cspan address=\"10.1016/j.biortech.2021.124829\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMullerova S, Baldikova E, Prochazkova J, Pospiskova K, Afarik I. Magnetically modified macroalgae Cymopolia barbata biomass as an adsorbent for safranin O removal. Mater Chem Phys. 2019;225:174\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.matchemphys.2018.12.074\u003c/span\u003e\u003cspan address=\"10.1016/j.matchemphys.2018.12.074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeniz F, Karabulut A. Biosorption of heavy metal ions by chemically modified biomass of coastal seaweed community: Studies on phycoremediation system modeling and design. Ecol Eng. 2017;106:101\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoleng.2017.05.024\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoleng.2017.05.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJayakumar V, Govindaradjane S, Kumar PS, Rajamohan N, Rajasimman M. Sustainable removal of cadmium from contaminated water using green alga\u0026ndash;Optimization, characterization and modeling studies. Environ Res. 2021;199:111364. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envres.2021.111364\u003c/span\u003e\u003cspan address=\"10.1016/j.envres.2021.111364\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtugoda T, Gunawardane C, Ahmad M, Vithanage M. Mechanistic interaction of ciprofloxacin on zeolite modified seaweed (Sargassum crassifolium) derived biochar: Kinetics, isotherm and thermodynamics. Chemosphere. 2021;281:130676. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chemosphere.2021.130676\u003c/span\u003e\u003cspan address=\"10.1016/j.chemosphere.2021.130676\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsencios YJO, Parreira LM, Perpetuo EA, Rotta AL. Characterization of seaweeds collected from Baixada Santista litoral, and their potential uses as biosorbents of heavy metal cations. Rev Mex Ing Quim. 2022;21:IA2600\u0026ndash;2600. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24275/rmiq/IA2600\u003c/span\u003e\u003cspan address=\"10.24275/rmiq/IA2600\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi R, Zhang T, Zhong H, Song W, Zhou Y, Yin X. Bioadsorbents from algae residues for heavy metal ions adsorption: chemical modification, adsorption behaviour and mechanism. Environ Technol. 2021;42:3132\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09593330.2020.1723711\u003c/span\u003e\u003cspan address=\"10.1080/09593330.2020.1723711\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShin WS. Adsorption characteristics of phenol and heavy metals on biochar from Hizikia fusiformis. Environ Earth Sci. 2017;76:1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12665-017-7125-4\u003c/span\u003e\u003cspan address=\"10.1007/s12665-017-7125-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSooksawat N, Meetam M, Kruatrachue M, Pokethitiyook P, Inthorn D. Equilibrium and kinetic studies on biosorption potential of charophyte biomass to remove heavy metals from synthetic metal solution and municipal wastewater. Bioremediat J. 2016;20:240\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10889868.2016.1212810\u003c/span\u003e\u003cspan address=\"10.1080/10889868.2016.1212810\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva A, Stawiński W, Romacho J, Santos LH, Figueiredo SA, Freitas OM, et al. Adsorption of fluoxetine and venlafaxine onto the marine seaweed Bifurcaria bifurcata. Environ Eng Sci. 2019;36:573\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/ees.2018.0332\u003c/span\u003e\u003cspan address=\"10.1089/ees.2018.0332\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoawad MN, El-Sayed AA, El-Naggar NA. Biosorption of cadmium and nickel ions using marine macrophyte, Cymodocea nodosa. Chem Ecol. 2020;36:458\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02757540.2020.1752199\u003c/span\u003e\u003cspan address=\"10.1080/02757540.2020.1752199\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":"Seaweed, Cadmium (II), Adsorption, Isotherm, Artificial Neural Network","lastPublishedDoi":"10.21203/rs.3.rs-4195678/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4195678/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe research focuses on examining the biosorption capability of raw mixed seaweed biosorbent (RMSB) for the removal of the hazardous metal cadmium (II) under controlled environmental conditions. Using techniques such as elemental dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR), biosorbent was characterized. The impacts of adsorbent dosage, contact time, initial Cd concentration, pH, and temperature have been assessed for the removal of Cd (II) and its adsorption. Optimum levels - pH, biosorbent mass, contact duration, and temperature were 5, 2 g/L, 50 minutes, and 303 K, respectively. The optimum intake of metals Cd (II) has been evaluated with isotherm modeling. Single-layer sorption was confirmed by the Freundlich isotherm, which proved to be an excellent fit. Maximum potential adsorption of Cd (II) was 146.2 mg/g. The biosorption kinetics of Cd (II) onto RMSB exhibit pseudo-first-order behaviour. The feasibility of the sorption process was established, and the thermodynamic parameters were determined. The Cd (II) sorption onto RMSB biomass has been estimated through the use of artificial neural networks (ANNs). With the high cross-correlation coefficient (R) value, the ANN models predicted the Cd (II) adsorption onto RMSB with remarkable accuracy. The outcomes showed that Cd (II) may be effectively removed from the aqueous solution using RMSB.\u003c/p\u003e","manuscriptTitle":"Effective removal of toxic Cd(II) ions from aqueous solution using mixed macroalgal adsorbent: Kinetics and ANN modeling studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-02 20:13:15","doi":"10.21203/rs.3.rs-4195678/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":"b16ef812-121b-4dd8-aa19-5ceefc90aa96","owner":[],"postedDate":"May 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-04T20:28:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-02 20:13:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4195678","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4195678","identity":"rs-4195678","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}