Removal of Mn(II) ion from aqueous solution onto cassava stem-derived activated carbon: Adsorption isotherms and kinetics studies

Removal of Mn(II) ion from aqueous solution onto cassava stem-derived activated carbon: Adsorption isotherms and kinetics 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 Removal of Mn(II) ion from aqueous solution onto cassava stem-derived activated carbon: Adsorption isotherms and kinetics studies Alhassan Pont This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9301112/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 high cost of commercial activated carbon due to its high production expenses, has driven increasing demand in lignocellulosic biomass-derived activated carbon as low-cost adsorbent for adsorbing emerging contaminants from drinking and wastewater due to its high surface area and high adsorption capacity. In this study, the mechanism of equilibrium adsorption and kinetics of Mn(II) ion in drinking and wastewater onto cassava stem-crude potash derived activated carbon were studied. The cassava stem was carbonized at 818.68 0 C for 1.45 h at impregnation ratio of 2:1. The chemically activated cassava stem charcoal was characterized for pH of 7.01 ± 0.10, moisture content of 4.09 ± 0.29%, volatile matter of 23.66 ± 0.20%, ash content of 3.13 ± 0.12%, fixed carbon content of 69.12 ± 0.09%, bulk density of 0.40 ± 0.11 g/mL, iodine number of 1080.75 ± 0.21 mg/g, surface area of 1101 ± 0.20 m 2 /g and attrition value of 62 ± 0.12%. Data from equilibrium adsorption were analyzed with two model equations which were Langmuir and Freundlich models. The Langmuir (R 2 = 0.9931) model correlated the experimental data better than the Freundlich (R 2 = 0.9881) isotherm model. The maximum adsorption capacity (q m ), intensity of adsorption (K L ) and separation factor (R L ) were obtained from Langmuir plot. Data from adsorption kinetics were analyzed with pseudo-first-order and pseudo-second-order models. The results showed that the pseudo-second-order (R 2 = 0.9997) was a better model, describing the adsorption kinetics data compared to the pseudo-first-order model (R = 0.9789). The maximum monolayer adsorption capacity of the activated carbon was evaluated as 555.56 mg/g. Environmental Policy Adsorption Manganese Langmuir model cassava stem and isotherm Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction In the earth’s crust, manganese is known to be the 10th most abundant element, only second to iron with 0.1% of manganese exiting in crystal rocks (Post, 1999 ). Manganese can exhibit various oxidation states ranging from − 3 to + 7, but in nature, the oxidation states of + 2, +3 and + 4 are the commonly occurring states with Mn 2+ known to be the most stable in solution (Milatovic et al., 2017 ). Despite the numerous advantages offered by manganese such as normal body functioning, growth and development, recent research indicates that it has serious consequences on health when ingested in large amounts (Burton et al., 2009 ). Consumption of excess manganese has been linked to low intelligence quotient especially in children, parkinsonism, abortion, stillbirth in women, reduction in hemoglobin regeneration and malfunctioning of the central nervous system (Martinez-Finley et al., 2013 ; Ke et al., 2019 ; O’Neal and Zheng, 2015 ). Hence the growing concern to manganese pollution in water has contributed to the use of the adsorption treatment method for removal of Mn(II) from aqueous solution (Baysal et al., 2013 ). The severity of polluted water with heavy metals is now a more challenging problem because of the rapid industrial development and the high search for freshwater supplies globally (Kobielska et al., 2018 ). Adsorption is an economical method of treating aqueous solution and it has been used to eliminate different types of heavy metals from aqueous solution due to its simplicity, efficiency and relatively low cost. The process gives a flexible operation and design that has the ability of providing treated effluent free of sludge, colour, and odour. In addition, the adsorbent used in the process of adsorption can be regenerated and reapplied (Kale et al., 2017 ). Therefore adsorption emerges as a favourable and economical technique for Mn(II) ion adsorption from aqueous solution. The removal process takes place when a liquid or gas molecule is adsorbed onto the surface of the adsorbent and generates atomic or a molecular film (Lakherwal, 2014 ). This occurs because of the existence of residual unbalanced forces on the surface of a solid or liquid media. The residual unbalanced forces keep on attracting and retaining the species of the molecules as they get to the adsorbent surface. The adsorbent adsorbs the adsorbate due to the attraction between them with bonding forces such as covalent bond and Van der Waals forces (Kale et al., 2017 ). Adsorption can be classified into two forms: physical and chemical adsorption. Physical adsorption: It occurs when the adsorbent and adsorbate interact through weak Van der Waals forces, hydrogen bonding and dipole-dipole interactions. Metal ions are adsorbed electrostatically by this physical process across the materials surface. In addition, the process occurs at almost equal or at a lower temperature of the adsorbed component. Physisorption has the capacity to form a multilayer adsorption process that gives high adsorption ability (Kale et al., 2017 ). Chemisorption: In this process, there exist a chemical reaction between the adsorbent surface and the adsorbate which forms new chemical bonds at the surface of the adsorbent. It is an irreversible reaction also called activated adsorption that requires high activation energy. Unlike the physisorption, chemisorption is a permanent reaction (Liu et al., 2019 ). Chemical adsorption can only form monolayer adsorption and remove trace materials selectively from aqueous solution. Therefore, there is difficulty in the regeneration and reusability of the adsorbent because it is an irreversible reaction (Amin et al., 2018 ). Models of Adsorption isotherms are represented by describing the relationship between the amount of adsorbate in fluid and quantity distributed onto the adsorbent phase as a function of concentration and pressure at equilibrium at a fixed temperature. Adsorption studies of heavy metals onto activated carbon and other carbon materials showed that Langmuir isotherm and Freundlich isotherm are often applied (Sadegh et al., 2018 ; Yang et al., 2019 ). But between the two, the Langmuir isotherm is more frequently applied, which is an indication of a chemical monolayer adsorption and a uniform adsorption performance of the surface of the adsorbent (Xu et al., 2018 ; Wen et al., 2018 ). . However, some carbonaceous materials do not adhere to that ideal uniform condition and as such Freundlich isotherm is applied as an empirical formular for multilayer adsorption (Zhang et al., 2014 ; Zhou et al., 2019 ). The adsorptive capacity of adsorbent, elucidate adsorption kinetics. The kinetic models commonly applied in adsorption are the pseudo-first-order, pseudo-second-order, Weber Morris models and Elovich kinetic models (Vikrant et al., 2019 ). But the heavy metals adsorption behavior in water by carbon-containing adsorbents is often elucidated by applying the pseudo-first-order and pseudo-second-order kinetic models, while the pseudo-second-order is often used frequently (Ho, 2006 ). Generally, the pseudo-first-order model assumes that in the adsorption process, there exist a reversible physical adsorption but the pseudo-second-order occurs as an irreversible adsorption (Ma et al., 2019 ). The alarming rate of Mn(II) pollution in water has led to the production of different adsorbents for adsorption of Mn(II) from aqueous solution (Baysal et al., 2013 ). Activated carbon’s high adsorption capacity as a result of its free active valence, small particle size and maximum internal suface area makes it a unique and advanced adsorption adsorbent for adsorption of Mn(II) from aqueous solution (Palodkar et al., 2017 ; Angin and Sarikulce., 2017). Below are some researchers who have assessed the adsorption capacities of activated carbon for Mn(II) onto its surface. Niksirat et al. prepared activated carbon for removal of Mn(II) from aqueous solution. The results obtained from isotherm models showed that the Mn(II) adsorption onto the activated carbon was more compatible with the Freundlich model. Also, Langmuir adsorption capacity of 120 mg/g was recorded. Again from the results, the process of adsorption suited well with the pseudo-second-order kinetics model (Niksirat et al., 2019 ). Omri and Benzina in their research used activated carbon to adsorb Mn(II) ion from aqueous solutions. The maximum adsorption capacity around 172 mg/g of Mn(II) ion was calculated from the Langmuir isotherm model (Omri and Benzina, 2012 ). Rachel et al. used granular activated carbon and activated carbon modified with iron oxide to remove Mn(II) ion from aqueous solution. The adsorption equilibrium was more compatible to the Freunlich isotherm model. A recorded Langmuir adsorption capacities of 14.49 and 6.94 mg/g for modified activated carbon and granular activated carbon respectively were obtained (Rachel et al., 2015 ). El-sherif et al. carried out, adsorption and precipitation methods to eliminate manganese from aqueous solution. Analyzes of the adsorption data equilibrium employing the Langmuir and Freundlich isotherm models showed that the manganese adsorption behaviour was more compatible to the Langmuir isotherm model with the monolayer adsorption capacity of 4.72 mg/g Mn(II) ion (El-sherif et al., 2013 ). Wanjara also used activated carbon to adsorbed Mn(II) ion from aqueous solution. The pseudo-second-order was the best for succesful description of the adsorption process. In conclusion, the activated carbon used for the study proved to be a good economical carbon to remove Mn(II) ion from aqueous solution (Wanjari, 2016 ). The aim of this study is to investigate the use of cassava stem-derived activated carbon for removal of Mn(II) from aqueous solutions through equilibrium adsorption isotherms and kinetic models. Materials and methods Materials Cassava stems, the primary raw material for the synthesis were obtained from an agricultural farm from Bole in the Savannah Region of northern Ghana. Crude potash, the material used as an inexpensive activating agent, was obtained from Bekwai in the Ashanti Region of Ghana. The other chemicals and reagents used were of analytical grade. Solutions were prepared using distilled water. Adsorbent preparation The impregnation of the biomass precursor was carried out by mixing about 20 g of the powdered cassava stem with crude potash in a beaker, varying the impregnation ratio (activating agent/biomass precursor) from 0.5 to 2.0. Thereafter, 100 mL of distilled water was added, and the resulting mixture was continuously stirred at room temperature for 24 h to achieve homogeneity. The resulting mixture was oven-dried at a temperature of 105 0 C for 24 h. The simultaneous carbonization and activation of the impregnated precursor was carried out from 600 to 900 ℃ and activation time ranged from 0.5 to 2 h at a heating rate of 25 ℃/min in a muffle furnace. The resulting material was cooled down in a desiccator, washed with distilled water and 0.1 M HCl until the pH of the washed solution was approximately 7. It was dried in an oven at 105 ℃ for 24 h, allowed to cool in a desiccator and kept in an air-tight container. Characterization pH The pH of the activated charcoal was determined using the standard method ASTM D 3838-80. 1 g of powdered activated charcoal was put in a beaker and 100 mL of deionized water was added to it. The mixture was stirred and allowed to stand for about 30 min. The pH reading was then taken using the pH meter. The readings were done in triplicate. Moisture Content The moisture content of the obtained powdered activated charcoal was determined following ASTM D 121. An empty crucible with its lid was heated at 105 0 C for about 1 h and cooled in a desiccator. 2.07 g of the powdered activated charcoal in a crucible was put in an oven at 105 0 C for 12 h and afterwards cooled in a desiccator. The experiment was conducted in triplicate. The moisture content was determined using Eq. 1. MC = [(W 0 – W 1 )/W 0 ] × 100% (1), where W 0 is the initial weight sample (powdered activated charcoal) (g) and W 1 is the weight of the oven dried activated charcoal (g). Volatile Matter The volatile matter of the activated charcoal was obtained following ASTM D 3175-02. About 2.06 g of activated charcoal was weighed and transferred into a crucible, covered with its lid, and later placed in a muffle furnace at 930 0 C for 10 mins. The crucible and its content were cooled in a desiccator and the volatile component determined using Eq. 2. The experiment was conducted in triplicate. VC = [(W 2 – W 3 )/(W 2 – W 1 )] \(\:\times\:\) 100% (2). Remarks: W 1 is the mass of empty crucible + lid (g), W 2 is the mass of empty crucible + sample (powdered activated charcoal) + lid (g) and W 3 is the weight of empty crucible + residue + lid (g). Ash Content The ash content of the prepared activated charcoal was determined following ASTM D 2866-94. 2.06 g of the powdered activated charcoal weighed into a crucible was transferred into a furnace and heated at 750 0 C for 6 h. The experiment was carried out in triplicate. It was thereafter cooled in a desiccator and the ash content was determined using Eq. 3. AC = (ash weight/ initial weight) × 100% (3) Fixed Carbon Content The fixed carbon content was determined using Eq. 4, where the experiment was conducted in triplicate. % Fixed carbon = 100 – (moisture content + volatile content + ash content) (4) Bulk density The bulk density was obtained through a tapping method described by Nyamful et al. (Nyamful et al., 2020 ). A weighed amount of 5.00 g of powdered activated charcoal was put into a 100 mL graduated cylinder. The cylinder was tapped constantly until there was no volume change and the bulk density was calculated using Eq. 5. Bulk density (g/mL) = \(\:\frac{weight\:of\:activated\:charcoal\:\left(g\right)}{volume\:of\:packed\:activated\:charcoal\:\left(mL\right)}\) (5) Iodine number The iodine number is the milligrams of elemental iodine removed by a gram of dry activated charcoal (mg/g) when it is in equilibrium with a 0.010 M solution of I 2 . The iodine number was determined following the ASTM D4607-94 (2006) method. The activated charcoal samples were mixed with 10 mL of 5% HCl and heated for 30 s and then cooled. About 100 mL of the 0.1 N iodine solution was then added to the mixture and stirred for 30 s. The obtained solution was filtered, and 50 mL of the filtrate was titrated against 0.1 N Na 2 S 2 O 3 , using starch as an indicator. The iodine amount removed per gram of activated charcoal (X/M) was calculated using Eq. ( 6 –9). $$\:\:\frac{X}{\:M}=\:\frac{[A-\left(DF\right)\left(B\right)\left(S\right)]}{M}$$ 6 A = (N 1 ) \(\:\times\:\) (12693) (7) B = (N 2 ) \(\:\times\:\) (126.93) (8) DF = (1 + H) / F (9), where the iodine adsorbed per gram of activated charcoal is represented as X/M (mg/g), DF represents the dilution factor, S is the volume of the sodium thiosulfate used (mL), M represents the quantity of activated charcoal used (g), N 1 is the normality of iodine (N), N 2 represents the normality of sodium thiosulphate (N), I is the iodine volume used (mL), H is the 5% HCl utilized (mL) and F represents the filtrate (mL). Attrition measurement The hardness of the activated charcoal was determined following the wet attrition test as described by Toles et al. (Toles et al., 2000 ). 1 g of powdered cassava stem-derived activated charcoal was weighed and transferred into 100 mL of 0.07 M sodium acetate 0.03 M acetic acid buffer solution of pH 4.8. The mixture was subsequently stirred at 500 rpm for 24 h at 25 0 C. Thereafter the mixture was placed on a 50-mesh sieve and using distilled water, it was washed. The samples were then dried by heating to 105 0 C for 2 h and finally allowed to cool and the final weight determined. Calculation of the final attrition was carried out using the following Eq. 10. % attrition = \(\:\frac{initial\:weight\:\left(g\right)\:-final\:weight\:\left(g\right)\:}{initial\:weight\:\left(g\right)\:}\) × 100 (10) Surface area determination The surface area of the cassava stem-derived activated charcoal was determined following Sear’s approximation method with slight modification (Abate et al., 2020 ). 1 g of the powdered activated charcoal was mixed with 20 g NaCl, and the mixture dissolved with 100 mL of distilled water in a 250 mL conical flask. The mixture was constantly stirred for five minutes after the dissolution. Afterwards, the solution pH was adjusted to 4, and the obtained solution was titrated against 0.1 M NaOH until the pH finally reached 9. The NaOH volumes needed to change the pH values from 4 to 9 were recorded. The sample specific surface area was hence obtained using Eq. 11. Specific surface area (m 2 /g) = 32V – 25 (11), where V represents volume of 0.1 M NaOH needed to raise the pH from 4.0 to 9.0. Standard Mn(II) solution Mn(II) stock solution of 1000 mg/L was prepared by dissolving 3.1393 g of Manganese sulphate monohydrate (MnSO 4 .H 2 O) in 250 mL of distilled water and the obtained solution was diluted to 1,000 mL mark of volumetric flask using distilled water. The stock solution was diluted with distilled water to prepare working concentrations. Concentrations prior as well as subsequent adsorption were estimated using UV – Vis spectrophotometer at a wavelength of 525 nm. Adsorption Isotherm, Experiments and Modeling The isotherms equilibrium experiments were conducted using conical flasks containing 0.47 g/L of activated carbon (optimized dose) and 100 mL of Mn(II) ion solution with initial Mn(II) ion concentrations (500, 1000, 1500, 2000, 2500, 3000, 3500 and 4000 mg/L) under an optimized temperature (45 0 C). The solutions pH was adjusted to 9.0 (optimized pH) using 0.1M NaOH or 0.1M HCl. The mixture in each flask was agitated at a constant speed of 100 rpm for equilibrium time of 200 min. Each flask content was filtered and the residual Mn(II) ions in the filtrate were analyzed at a wavelength of 525 nm using UV – Vis spectrophotometer. The quantity of Mn(II) ion removed at equilibrium was obtained using Eq. 12. q t = \(\:\frac{(C0-Ct)}{m}\) \(\:\times\:\) V (12) The obtained experimental equilibrium data were applied to analyze Langmuir and Freundlich models and the linear regressions obtained were used to determine the model with a better fit. Below is Langmuir isotherm model equation: \(\:\frac{Ce}{qe}\) = \(\:\frac{1}{qmKL}\) + \(\:\frac{Ce}{qm}\) (13) The constants K L and q m were derived from Eq. 13 by plotting \(\:\frac{\text{C}\text{e}}{\text{q}\text{e}}\) against C e where the slope is represented as \(\:\:\:\frac{Ce}{qm}\) and \(\:\:\frac{1}{qmKL}\) representing the intercept. The adsorption process fitness to Langmuir isotherm model was examined using Eq. 14. The obtained value of R L was applied to determine the isotherm model’s shape (Chakravarty et al., 2010 ). R L = \(\:\frac{1}{1+KLCo}\) (14) where, R L represents separation factor, C 0 represents the initial Mn(II) ion concentration and K L is the Langmuir constant (L/mg) associated to the adsorption energy through the Arrhenius equation. Eq. 15 represents Freundlich isotherm model. log q e = log K F + \(\:\frac{1}{\text{n}}\) log C e (15) where, q e represents the removal capacity, C e represents the adsorbent concentration in the solution at equilibrium, K F is a constant representing the adsorption capacity of the activated carbon and n represents the constant of the adsorption intensity. Using the plot of log q e against log C e in Eq. 15, the constant K F was derived from the intercept of log K F and the constant n was obtained from the slope of \(\:\frac{1}{n}\) . The surface heterogeneity or adsorption intensity of the activated carbon was measured from the slope ranging from 0 to 1. Proximity of the value to zero, makes the surface of the adsorbent more heterogeneous. Also, an obtained value of \(\:\frac{1}{n}\) less than 1 demonstrated a chemisorption process, but an obtained value of \(\:\frac{1}{n}\) greater than 1 usually indicates cooperative adsorption (Brasquet et al., 1997 ; Haghseresht and Lu, 1998 ). The linear plots of Langmuir and Freundlich isotherm models are represented in Figs. 1 and 2 respectively whereas the obtained results of the constants and correlation coefficients (R 2 ) of the two models are summarized in Table 1 . Adsorption Kinetic, Experiments and Modeling The adsorption kinetics experiment was conducted using conical flasks of 250 mL capacity containing a mixture of 0.47 g/L of activated carbon (optimized dose) and 100 mL of Mn(II) ion solutions at specific times (10, 20, 30, 60, 90, 120, 150, 180, 210 and 240 min) respectively using the highest initial Mn(II) ion concentration of 4000 mg/L at an optimized temperature of 45 0 C. The solutions pH was adjusted to an optimized pH of 9.0 using 0.1 M HCl or 0.1 M NaOH. The activated carbon-Mn(II) ion solutions were agitated at a constant speed of 100 rpm to achieve equilibrium time (10, 20, 30, 60, 90, 120, 150, 180, 210 and 240 min) respectively. Each flask content was filtered with Whatman filter paper at the stop of each adsorption time and the residual Mn(II) ions in the filtrate were analyzed at a wavelength of 525 nm using UV – Vis spectrophotometer. The amount of Mn(II) ion adsorbed at a particular period (t) was obtained using Eq. 12 above. Using the obtained experimental data from the kinetic experiment, the adsorption mechanism of Mn(II) ion onto the activated carbon was determined by testing the pseudo-first-order and the pseudo-second-order. Results and discussion Characterization The results obtained from the characterization of the powdered activated charcoal are found in Table 1 . Table 1 Characterization of the prepared activated charcoal Parameters Obtained values pH 7.01 ± 0.10 Moisture content 4.09 ± 0.29% Volatile matter 23.66 ± 0.20% Ash content 3.13 ± 0.12% Fixed carbon content 69.12 ± 0.09% Bulk density 0.40 ± 0.11 g/mL Iodine number 1080.75 ± 0.21 mg/g Surface area 1101 ± 0.20 m 2 /g Attrition 62 ± 0.12% pH pH is a strong indicator of the efficacy of adsorption process, hence adsorption using activated charcoal is affected by pH. At neutral and moderate (within the lower alkaline region) pH, activated charcoals are more effective in adsorption than at lower and higher pH (Evbuomwan et al., 2013 ). The pH value of the activated charcoal obtained in this work from Table 2 falls within the pH range of most commercial activated charcoal (pH 7 to pH 9) (Erhayem et al., 2016 ). Moisture Content An excellent adsorbent such as activated charcoal should be low in moisture content (Ekpete et al., 2017 ). The moisture content recorded in this work was 4.09 ± 0.29% which was lower than work conducted by Ulfah et al. whose moisture content was 5.42% (Ulfah et al., 2016). The obtained result in this study suggest that the prepared activated charcoal will make a better adsorbent since it has lower moisture content. Relative to the moisture content obtained by Ulfah et al. and Devi et al. it could be concluded that the activated charcoal prepared is of appreciable quality and thus, indicates that cassava stem is a suitable precursor for preparing high performance activated charcoal (Ulfah et al., 2016; Devi et al., 2012 ). Volatile Content A low volatile matter of 23.66 ± 0.20% (Table 1 ) was obtained for the activated charcoal. The value was lower compared to work conducted by Noor et al. who obtained 81.51% volatile matter using cassava stem (Noor et al., 2012 ). The low percentage of volatile matter of the activated charcoal in this work might be because of the dehydrating effect of the crude potash (salt), the chemical activating agent which could have caused the biomass to vaporize and strengthen the covalent bond in the carbon matrix leaving no chance for the loss of carbon (Noor et al., 2012 ). Ash Content The obtained ash content of the activated charcoal was 3.13 ± 0.12% (Table 1 ). Ash content is the actual characteristic of activated charcoals. Higher ash content decreases the efficacy of activated charcoal. The value of the ash content obtained in this study was lower compared to works carried out by Noor et al. (Noor et al., 2012 ). The lower percentage of ash content derived in this study indicates that the activated charcoal produced is of remarkable quality and hence will make a better adsorbent. Fixed Carbon Content Carbon content refers to the percentage of carbon that remains after the volatiles are eliminated [41]. Table 1 shows a high carbon content of 69.12 ± 0.09% which is an indication that the prepared activated charcoal has appreciable quality compared to work done by Noor et al. who obtained 16.07% using cassava stem as a precursor (Noor et al., 2012 ). It also indicates that the activated charcoal would have a high adsorption capacity. Bulk density Bulk density refers to the mass of carbon contained in a filter of a given solid and treated amount of liquid that a filter cake can retain. The filterability of the activated charcoal becomes better with higher density (Nyamful et al., 2020 ). A bulk density of 0.40 ± 0.11 g/mL (Table 1 ) was recorded for the cassava stem-derived activated charcoal. The material hardness could have contributed to this result. The value of bulk density in this study agreed closely to work done by Ekpete et al. (Ekpete et al., 2017 ). Iodine number The iodine number is usually used for rough estimation of surface area of activated charcoal at conditions of room temperature. It represents the relative indicator of the porosity and adsorbent capacity in an activated charcoal. Higher iodine numbers of activated charcoal are usually attributed to the availability of micropore structure and to the great approximation of the activated charcoal to possess a large surface area because of the enlargement of their pore structures (Ekpete and Harcourt, 2014 ). The iodine number obtained in this study was 1080.75 ± 0.21 mg/g (Table 1 ). The value obtained is relatively close to work conducted by Li et al. who reported a maximum iodine number of 1085 mg/g (Li et al., 2009 ). Surface area The prepared activated charcoal’s specific surface area based on Sear method of analysis obtained in this study was 1101 ± 0.20 m 2 /g (Table 1 ). Based on the value obtained for surface area in this work, it can be concluded that the obtained activated charcoal has a very high surface area, which might have arisen from the crude potash activation. The result obtained in this research agreed closely to the work done by Xin-hui et al. and Abate et al. (Xin-hui et al., 2012 ; Abate et al., 2020 ). Attrition measurement Another important physical property of any activated charcoal is its attrition or hardness, especially granular activated charcoals which due to their nature of applications may encounter intraparticle abrasion. The relatively low density of the cassava stem precursor might have caused the relatively high attrition value observed in this study. The value obtained (Table 1 ) is in concordance with work done by Toles et al . (Toles et al., 2000 ). Adsorption Isotherms and Modeling The detailed results of the Langmuir and Freundlich isotherm models are found in Tables 2 and 3 and summarized in Table 4 . Table 2 Data on Langmuir Isotherm C 0 (mg/L) C e (mg/L) Rep 1 C e (mg/L) Rep 2 Average reps of C e (mg/L) q e (mg/g) C e /q e (g/L) 500 147.77 150.22 149.0 74.68 1.995 1000 349.18 347.03 348.11 138.70 2.510 1500 585.56 582.76 584.16 183.80 3.178 2000 847.21 849.18 848.20 245.06 3.461 2500 1138.20 1136.01 1137.10 289.98 3.921 3000 1462.77 1458.66 1460.72 327.51 4.460 3500 1848.94 1851.05 1850 351.06 5.270 4000 2241.44 2238.55 2240 374.47 5.982 Table 3 Data on Freundlich Isotherm C 0 (mg/L) C e (mg/L) Rep1 C e (mg/L) Rep 2 Average C e (mg/L) q e (mg/g) log C e (mg/L) log q e (mg/g) 500 147.77 150.22 149.00 74.68 2.173 1.873 1000 349.18 347.03 348.11 138.70 2.542 2.142 1500 585.56 582.76 584.16 183.80 2.767 2.264 2000 847.21 849.18 848.20 245.06 2.928 2.389 2500 1138.20 1136.01 1137.10 289.98 3.056 2.462 3000 1462.77 1458.66 1460.72 327.51 3.165 2.515 3500 1848.94 1851.05 1850 351.06 3.267 2.545 4000 2241.44 2238.55 2240 374.47 3.350 2.573 Table 4 Summarized results of the isotherm models Langmuir Freundlich q m (mg/g) K L (L/mg) R L R 2 K F (mg/g) n 1/n R 2 555.56 9.596 × 10 − 4 0.2066 0.9931 3.867 1.654 0.6045 0.9881 From Fig. 1 , the correlation coefficient (R 2 ) of 0.9931 of the Langmuir isotherm models showed its fitness to the equilibrium data better than the Freundlich model. This fitness of the Langmuir model could be an indication of monolayer adsorption by the cassava stem-crude potash activated carbon surface possessing a fixed number of adsorption sites. Table 4 indicates that the R L value was between 0 and 1, which demonstrated that under the studied conditions, the adsorption process was favourable. The monolayer adsorption capacity (q m ) value was found to be 555.56 mg/g indicating the high removal capacity of the cassava stem-crude potash activated carbon. The value of K L was relatively high indicating that the surface of the activated carbon possessed high energy and consequently high bonding between Mn(II) ions and the activated carbon. Researchers have earlier investigated the adsorption capacities of various activated carbons on adsorption of Mn(II) onto the surfaces of these activated carbons. The reported maximum monolayer adsorption capacities of Mn(II) onto various activated carbons are listed in Table 5 . However, the adsorption capacity of cassava stem-derived activated carbon (555.56 mg/g) obtained in this study was a very large value compared to the values obtained by other researchers as shown in Table 5 . This implies that the large value obtained in this study makes the cassava stem activated carbon to be a promising inexpensive adsorbent for the adsorption of Mn(II) ion from aqueous solution. Table 5 Maximum monolayer capacities of activated carbons for removal of Mn(II) Adsorbent Adsorption Capacity (mg/g) References Activated carbon 120.00 Niksirat et al. ( 2019 ) Activated carbon 172.00 Omri and Benzina, ( 2012 ) Activated carbon 149.43 Rachel et al. ( 2015 ) Activated carbon 4.72 El-sherif et al. ( 2013 ) Activated carbon 28.60 Wanjari, ( 2016 ) Canola flower, activated carbon 40.00 Feizi and Jalali, ( 2015 ) Potato, activated carbon 47.60 Feizi and Jalali, ( 2015 ) Sunflower, activated carbon 47.60 Feizi and Jalali, ( 2015 ) Cassava stem activated carbon 555.56 This study Similarly, the Freundlich model isotherm (Fig. 2 ) also had a fit to the experimental data. But the level of fitness was low compared to the fitness of the Langmuir model as indicated by the Freundlich correlation coefficient of 0.9881. From Table 4 above, the value of n was obtained as 1.654 proving that the activated carbon had a heterogenous surface since the obtained value falls within the heterogeneity range of 1 < n < 10. Again, the result of \(\:\frac{1}{n}\) was less than 1, indicating a chemical adsorption process. The value of K F (3.867 mg/g) was not very high showing that there was low adsorption of the Mn(II) ions onto the activated carbon surface. The investigated models when compared, the Langmuir model demonstrated a better fitness to the equilibrium data compared to the Freundlich model, thereby indicating a monolayer adsorption. Studies from literature has shown that the Langmuir isotherm model is more frequently used than the Freundlich model and other isotherm models in the adsorption studies of heavy metals onto activated carbon as shown in Table 6 and confirmed in this study. Table 6 Reported adsorption isotherm models of activated carbon in heavy metal removal Adsorbent Heavy metal Best-fit isotherm model References Activated carbon Cu(II) Zn(II) Langmuir Kazmierczak-razna et al., ( 2021 ) Activated carbon Mn(II) Lagmuir Omri and Benzina, ( 2012 ) Activated carbon Cu(II) Freundlich Muslim and Said, ( 2017 ) Activated carbon Mn(II) Freundlich Mengistie et al., ( 2012 ) Corn straw porous carbon Cr (VI) Langmuir Ma et al., ( 2019 ) Rice husk carbon Cr(VI) Langmuir Khan et al., ( 2016 ) Walnut shell activated carbon Cr(VI) Langmuir Nethaji and Sivasamy, ( 2014 ) Activated carbon Mn(II) Freundlich Niksirat et al., ( 2019 ) Mesoporous carbon Mn(II) Langmuir Anbia and Amirmahmoodi, ( 2011 ) Activated carbon Pb(II) Ni(II) Cd(II) Zn(II) Freundlich Karnib et al., ( 2014 ) Activated carbon Mn(II) Fe(II) Fowler - Guggenheim Koubaissy et al., ( 2014 ) Activated carbon Mn(II) Langmuir This study Adsorption Kinetics and Modeling The detailed results of the pseudo-first-order and pseudo-second-order kinetic models are presented in Tables 7 , 8 and 9 and summarized in Table 10 , Fig. 3 and Fig. 4 respectively. Table 7 Data for kinetic studies using the highest Initial concentration of 4000 mg/L Time (min) C e (mg/L) Rep 1 C e (mg/L) Rep 2 Average of Reps (mg/L) q e (mg/g) 10 3060.441 3064.541 3062.491 199.47 20 2768.448 2764.438 2766.438 262.46 30 2471.475 2469.295 2470.385 325.45 60 2331.375 2327.405 2329.385 355.45 90 2232.052 2235.042 2233.552 375.84 120 2161.644 2167.834 2164.744 390.48 150 2116.148 2111.998 2114.078 401.26 180 2069.741 2071.831 2070.791 410.47 210 2069.542 2072.032 2070.792 410.48 240 2068.781 2072.791 2070.791 410.47 Table 8 Data on pseudo-first-order Time (min) q t (mg/g) q e (mg/g) q e – q t (mg/g) log (q e – q t ) [mg/g] 10 199.47 211 2.32 20 262.46 148.01 2.17 30 325.45 85.01 1.93 60 355.45 55.02 1.74 90 375.84 34.63 1.53 120 390.48 19.99 1.30 150 401.26 9.21 0.96 180 410.47 410.47 210 410.47 240 410.47 Table 9 Data on pseudo-second-order Time (min) t/q t (min.g/mg) 10 0.050 20 0.076 30 0.092 60 0.169 90 0.239 120 0.307 150 0.374 180 0.439 210 0.512 240 0.585 Table 10 Summarized results of the kinetic models Pseudo-first-order Pseudo-second-order K p1 (L/min) q e (mg/g) R 2 K p2 (L/min) q e (mg/g) R 2 0.02 211.836 0.9789 1.91 \(\:\times\:\) 10 − 4 434.783 0.9997 Figure 3 and 4 above indicates that the generated equilibrium data obtained from the adsorption of Mn(II) onto the activated carbon fitted the pseudo-first-order and pseudo-second-order kinetic models with impressive R 2 values respectively. However, it was seen from Fig. 4 that the pseudo-second-order kinetic model showed excellent linearity with higher correlation coefficient (R 2 ) > 0.999 when compared to the pseudo-first-order model; thereby emerging as a better fit. From Table 10 , the obtained values of the adsorption capacity (qe) of the pseudo-second-order kinetic model proved its fitness to the experimental data in comparison to the pseudo-first-order model. Trace metals adsorption from aqueous solution by carbon-based adsorbents is often explained by applying pseudo-first-order and pseudo-seond-order kinetic models. However the pseudo-second-order is often applied frequently and mostly emerges as the better fit (Ho, 2006 ). This trend could be observed from Table 11 and comfirmed by the results obtained from this study. The pseudo-first-oder model assumes a reversible physisorption in the process of adsorption (Vikrant et al., 2019 ). But the pseudo-second-order is commonly used in heavy metals removal by carbon-based adsorbents, implying that the cabon-based adsorbents adsorption is an irreversible chemical adsorption process in most cases (Ma et al., 2019 ). Table 11 Reported adsorption kinetic models of activated carbon in heavy metal removal Adsorbent Heavy metal Best-fit kinetic models Reference Activated carbon Pb(II) Pseudo-second-order Nasirudden et al., (2022) Activated carbon Pb(II) Cd(II) Pseudo-second-order Salman et al., ( 2021 ) Activated carbon Ni(II) Cd(II) Co(II) Cu(II) Pseudo-second-order Amadi et al, ( 2021 ) Activated carbon Cd(II) Pb(II) Pseudo-second-order Al-onazi et al., ( 2021 ) Activated carbon Pb(II) Pseudo-second-order Abdulkarim and Al-rub, ( 2003 ) Activated carbon Cu(II) Zn(II) Pseudo-second-order Kazmierczak-razna et al., ( 2021 ) Activated carbon Pb(II) Cu(II) Zn(II) Cd(II) Cr(II) Pseudo-first-order Suo et al., ( 2020 ) Ativated carbon Pb(II) Cd(II) Cu(II) Pseudo-second-order Ali et al., ( 2020 ) Activated carbon Zn(II) Cu(II) Pb(II) Fe(II) Pseudo-second-order Gin et al., ( 2014 ) Activated carbon Mn(II) Pseudo-second-order This study Conclusion This study described the adsorption of Mn(II) ion onto activated carbon prepared from cassava stem. The activated carbon displayed promising results in adsorbing heavy metals especially Mn(II) from drinking and wastewater. According to the characterization results obtained, the cassava stem-derived activated charcoal proved to be a better adsorbent compared to other activated charcoal produced by some researchers using other precursors. This is because the obtained activated charcoal in this study was advantageous in moisture content, ash content, volatile matter, and high carbon content. The experimental equilibrium data of Mn(II) ion adsorption had a better fit with Langmuir isotherm model compared to the Freundlich isotherm model with an impressive maximum monolayer adsorption capacity (Q) of 555.56 mg/g. Moreover the R L value showed that the cassava stem-derived activated carbon was favourable for the removal of Mn(II) ion. The kinetic study also showed that the pseudo-second-order rate equation better described the adsorption process than the pseudo-first-order rate equation. These findings suggest that the prepared activated carbon has the potential to serve as a cost-effective and efficient alternative to commercial carbons, with promising applications in water purification by remediation of heavy metals [Mn(II)] from drinking and wastewater. Declarations Competing Interest The author declares no competing interest and manuscript is purely academic research and all references have been duly acknowledged. Funding The manuscript has received no funding from any source or institution and its purely an academic work. Author Contribution A.P (Alhassan Pont) gathered and analyzed the data, drafted, and prepared the manuscript for scientific clarity and coherence. A.P was responsible for editorial corrections. The author has read and agreed to the published version of the manuscript. Acknowledgement The author would like to express his appreciation to the management of the University for Development Studies, Nyankpala-Ghana, for equipping the Food Technology Laboratory and School of Engineering Laboratory for research work where this research was conducted. References Abate GY, Alene AN, Habte AT, Getahun DM (2020) Adsorptive removal of malachite green dye from aqueous solution onto activated carbon of Catha edulis stem as a low-cost bio – adsorbent. Environmental Systems Research, pp 9–29 https://doi.org/10.1186/s40068-020-00191-4 Abdulkarim M, Al-Rub FA (2003) Adsorption of Lead Ions from Aqueous Solution onto Activated Carbon and Chemically modified Activated Carbon Prepared from Date Pits. Adsorpt Sci Technol 119–134. https://doi.org/10.1260/026361704323150908 Ali AS, Hassan AMA, Nour MH (2020) Equilibrium and Kinetics studies for Removal of Heavy Metals from simulated Wastewater using Chitosan/Activated carbon composite. 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Manganese can exhibit various oxidation states ranging from \u0026minus;\u0026thinsp;3 to +\u0026thinsp;7, but in nature, the oxidation states of +\u0026thinsp;2, +3 and +\u0026thinsp;4 are the commonly occurring states with Mn\u003csup\u003e2+\u003c/sup\u003e known to be the most stable in solution (Milatovic et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Despite the numerous advantages offered by manganese such as normal body functioning, growth and development, recent research indicates that it has serious consequences on health when ingested in large amounts (Burton et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Consumption of excess manganese has been linked to low intelligence quotient especially in children, parkinsonism, abortion, stillbirth in women, reduction in hemoglobin regeneration and malfunctioning of the central nervous system (Martinez-Finley et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ke et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; O\u0026rsquo;Neal and Zheng, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHence the growing concern to manganese pollution in water has contributed to the use of the adsorption treatment method for removal of Mn(II) from aqueous solution (Baysal et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The severity of polluted water with heavy metals is now a more challenging problem because of the rapid industrial development and the high search for freshwater supplies globally (Kobielska et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Adsorption is an economical method of treating aqueous solution and it has been used to eliminate different types of heavy metals from aqueous solution due to its simplicity, efficiency and relatively low cost. The process gives a flexible operation and design that has the ability of providing treated effluent free of sludge, colour, and odour. In addition, the adsorbent used in the process of adsorption can be regenerated and reapplied (Kale et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore adsorption emerges as a favourable and economical technique for Mn(II) ion adsorption from aqueous solution. The removal process takes place when a liquid or gas molecule is adsorbed onto the surface of the adsorbent and generates atomic or a molecular film (Lakherwal, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This occurs because of the existence of residual unbalanced forces on the surface of a solid or liquid media. The residual unbalanced forces keep on attracting and retaining the species of the molecules as they get to the adsorbent surface. The adsorbent adsorbs the adsorbate due to the attraction between them with bonding forces such as covalent bond and Van der Waals forces (Kale et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdsorption can be classified into two forms: physical and chemical adsorption. Physical adsorption: It occurs when the adsorbent and adsorbate interact through weak Van der Waals forces, hydrogen bonding and dipole-dipole interactions. Metal ions are adsorbed electrostatically by this physical process across the materials surface. In addition, the process occurs at almost equal or at a lower temperature of the adsorbed component. Physisorption has the capacity to form a multilayer adsorption process that gives high adsorption ability (Kale et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Chemisorption: In this process, there exist a chemical reaction between the adsorbent surface and the adsorbate which forms new chemical bonds at the surface of the adsorbent. It is an irreversible reaction also called activated adsorption that requires high activation energy. Unlike the physisorption, chemisorption is a permanent reaction (Liu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Chemical adsorption can only form monolayer adsorption and remove trace materials selectively from aqueous solution. Therefore, there is difficulty in the regeneration and reusability of the adsorbent because it is an irreversible reaction (Amin et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eModels of Adsorption isotherms are represented by describing the relationship between the amount of adsorbate in fluid and quantity distributed onto the adsorbent phase as a function of concentration and pressure at equilibrium at a fixed temperature. Adsorption studies of heavy metals onto activated carbon and other carbon materials showed that Langmuir isotherm and Freundlich isotherm are often applied (Sadegh et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). But between the two, the Langmuir isotherm is more frequently applied, which is an indication of a chemical monolayer adsorption and a uniform adsorption performance of the surface of the adsorbent (Xu et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wen et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e. However, some carbonaceous materials do not adhere to that ideal uniform condition and as such Freundlich isotherm is applied as an empirical formular for multilayer adsorption (Zhang et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe adsorptive capacity of adsorbent, elucidate adsorption kinetics. The kinetic models commonly applied in adsorption are the pseudo-first-order, pseudo-second-order, Weber Morris models and Elovich kinetic models (Vikrant et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). But the heavy metals adsorption behavior in water by carbon-containing adsorbents is often elucidated by applying the pseudo-first-order and pseudo-second-order kinetic models, while the pseudo-second-order is often used frequently (Ho, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Generally, the pseudo-first-order model assumes that in the adsorption process, there exist a reversible physical adsorption but the pseudo-second-order occurs as an irreversible adsorption (Ma et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe alarming rate of Mn(II) pollution in water has led to the production of different adsorbents for adsorption of Mn(II) from aqueous solution (Baysal et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Activated carbon\u0026rsquo;s high adsorption capacity as a result of its free active valence, small particle size and maximum internal suface area makes it a unique and advanced adsorption adsorbent for adsorption of Mn(II) from aqueous solution (Palodkar et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Angin and Sarikulce., 2017). Below are some researchers who have assessed the adsorption capacities of activated carbon for Mn(II) onto its surface.\u003c/p\u003e \u003cp\u003eNiksirat et al. prepared activated carbon for removal of Mn(II) from aqueous solution. The results obtained from isotherm models showed that the Mn(II) adsorption onto the activated carbon was more compatible with the Freundlich model. Also, Langmuir adsorption capacity of 120 mg/g was recorded. Again from the results, the process of adsorption suited well with the pseudo-second-order kinetics model (Niksirat et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOmri and Benzina in their research used activated carbon to adsorb Mn(II) ion from aqueous solutions. The maximum adsorption capacity around 172 mg/g of Mn(II) ion was calculated from the Langmuir isotherm model (Omri and Benzina, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRachel et al. used granular activated carbon and activated carbon modified with iron oxide to remove Mn(II) ion from aqueous solution. The adsorption equilibrium was more compatible to the Freunlich isotherm model. A recorded Langmuir adsorption capacities of 14.49 and 6.94 mg/g for modified activated carbon and granular activated carbon respectively were obtained (Rachel et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEl-sherif et al. carried out, adsorption and precipitation methods to eliminate manganese from aqueous solution. Analyzes of the adsorption data equilibrium employing the Langmuir and Freundlich isotherm models showed that the manganese adsorption behaviour was more compatible to the Langmuir isotherm model with the monolayer adsorption capacity of 4.72 mg/g Mn(II) ion (El-sherif et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWanjara also used activated carbon to adsorbed Mn(II) ion from aqueous solution. The pseudo-second-order was the best for succesful description of the adsorption process. In conclusion, the activated carbon used for the study proved to be a good economical carbon to remove Mn(II) ion from aqueous solution (Wanjari, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe aim of this study is to investigate the use of cassava stem-derived activated carbon for removal of Mn(II) from aqueous solutions through equilibrium adsorption isotherms and kinetic models.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMaterials\u003c/h2\u003e \u003cp\u003eCassava stems, the primary raw material for the synthesis were obtained from an agricultural farm from Bole in the Savannah Region of northern Ghana. Crude potash, the material used as an inexpensive activating agent, was obtained from Bekwai in the Ashanti Region of Ghana. The other chemicals and reagents used were of analytical grade. Solutions were prepared using distilled water.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAdsorbent preparation\u003c/h3\u003e\n\u003cp\u003eThe impregnation of the biomass precursor was carried out by mixing about 20 g of the powdered cassava stem with crude potash in a beaker, varying the impregnation ratio (activating agent/biomass precursor) from 0.5 to 2.0. Thereafter, 100 mL of distilled water was added, and the resulting mixture was continuously stirred at room temperature for 24 h to achieve homogeneity. The resulting mixture was oven-dried at a temperature of 105 \u003csup\u003e0\u003c/sup\u003eC for 24 h.\u003c/p\u003e \u003cp\u003eThe simultaneous carbonization and activation of the impregnated precursor was carried out from 600 to 900 ℃ and activation time ranged from 0.5 to 2 h at a heating rate of 25 ℃/min in a muffle furnace. The resulting material was cooled down in a desiccator, washed with distilled water and 0.1 M HCl until the pH of the washed solution was approximately 7. It was dried in an oven at 105 ℃ for 24 h, allowed to cool in a desiccator and kept in an air-tight container.\u003c/p\u003e\n\u003ch3\u003eCharacterization\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003epH\u003c/h2\u003e \u003cp\u003eThe pH of the activated charcoal was determined using the standard method ASTM D 3838-80. 1 g of powdered activated charcoal was put in a beaker and 100 mL of deionized water was added to it. The mixture was stirred and allowed to stand for about 30 min. The pH reading was then taken using the pH meter. The readings were done in triplicate.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMoisture Content\u003c/h3\u003e\n\u003cp\u003eThe moisture content of the obtained powdered activated charcoal was determined following ASTM D 121. An empty crucible with its lid was heated at 105 \u003csup\u003e0\u003c/sup\u003eC for about 1 h and cooled in a desiccator. 2.07 g of the powdered activated charcoal in a crucible was put in an oven at 105 \u003csup\u003e0\u003c/sup\u003eC for 12 h and afterwards cooled in a desiccator. The experiment was conducted in triplicate. The moisture content was determined using Eq.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eMC = [(W\u003csub\u003e0\u003c/sub\u003e \u0026ndash; W\u003csub\u003e1\u003c/sub\u003e)/W\u003csub\u003e0\u003c/sub\u003e] \u0026times; 100% (1),\u003c/p\u003e \u003cp\u003ewhere W\u003csub\u003e0\u003c/sub\u003e is the initial weight sample (powdered activated charcoal) (g) and W\u003csub\u003e1\u003c/sub\u003e is the weight of the oven dried activated charcoal (g).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVolatile Matter\u003c/h2\u003e \u003cp\u003eThe volatile matter of the activated charcoal was obtained following ASTM D 3175-02. About 2.06 g of activated charcoal was weighed and transferred into a crucible, covered with its lid, and later placed in a muffle furnace at 930 \u003csup\u003e0\u003c/sup\u003eC for 10 mins. The crucible and its content were cooled in a desiccator and the volatile component determined using Eq.\u0026nbsp;2. The experiment was conducted in triplicate.\u003c/p\u003e \u003cp\u003eVC = [(W\u003csub\u003e2\u003c/sub\u003e \u0026ndash; W\u003csub\u003e3\u003c/sub\u003e)/(W\u003csub\u003e2\u003c/sub\u003e \u0026ndash; W\u003csub\u003e1\u003c/sub\u003e)] \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e 100% (2).\u003c/p\u003e \u003cp\u003eRemarks: W\u003csub\u003e1\u003c/sub\u003e is the mass of empty crucible\u0026thinsp;+\u0026thinsp;lid (g), W\u003csub\u003e2\u003c/sub\u003e is the mass of empty crucible\u0026thinsp;+\u0026thinsp;sample (powdered activated charcoal)\u0026thinsp;+\u0026thinsp;lid (g) and W\u003csub\u003e3\u003c/sub\u003e is the weight of empty crucible\u0026thinsp;+\u0026thinsp;residue\u0026thinsp;+\u0026thinsp;lid (g). \u003cb\u003eAsh Content\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe ash content of the prepared activated charcoal was determined following ASTM D 2866-94. 2.06 g of the powdered activated charcoal weighed into a crucible was transferred into a furnace and heated at 750 \u003csup\u003e0\u003c/sup\u003eC for 6 h. The experiment was carried out in triplicate. It was thereafter cooled in a desiccator and the ash content was determined using Eq.\u0026nbsp;3.\u003c/p\u003e \u003cp\u003eAC = (ash weight/ initial weight) \u0026times; 100% (3)\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFixed Carbon Content\u003c/h3\u003e\n\u003cp\u003eThe fixed carbon content was determined using Eq.\u0026nbsp;4, where the experiment was conducted in triplicate.\u003c/p\u003e \u003cp\u003e% Fixed carbon\u0026thinsp;=\u0026thinsp;100 \u0026ndash; (moisture content\u0026thinsp;+\u0026thinsp;volatile content\u0026thinsp;+\u0026thinsp;ash content) (4)\u003c/p\u003e\n\u003ch3\u003eBulk density\u003c/h3\u003e\n\u003cp\u003eThe bulk density was obtained through a tapping method described by Nyamful \u003cem\u003eet al.\u003c/em\u003e (Nyamful et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A weighed amount of 5.00 g of powdered activated charcoal was put into a 100 mL graduated cylinder. The cylinder was tapped constantly until there was no volume change and the bulk density was calculated using Eq.\u0026nbsp;5.\u003c/p\u003e \u003cp\u003eBulk density (g/mL) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{weight\\:of\\:activated\\:charcoal\\:\\left(g\\right)}{volume\\:of\\:packed\\:activated\\:charcoal\\:\\left(mL\\right)}\\)\u003c/span\u003e\u003c/span\u003e (5)\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIodine number\u003c/h2\u003e \u003cp\u003eThe iodine number is the milligrams of elemental iodine removed by a gram of dry activated charcoal (mg/g) when it is in equilibrium with a 0.010 M solution of I\u003csub\u003e2\u003c/sub\u003e. The iodine number was determined following the ASTM D4607-94 (2006) method. The activated charcoal samples were mixed with 10 mL of 5% HCl and heated for 30 s and then cooled. About 100 mL of the 0.1 N iodine solution was then added to the mixture and stirred for 30 s. The obtained solution was filtered, and 50 mL of the filtrate was titrated against 0.1 N Na\u003csub\u003e2\u003c/sub\u003eS\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, using starch as an indicator. The iodine amount removed per gram of activated charcoal (X/M) was calculated using Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u0026ndash;9).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\:\\frac{X}{\\:M}=\\:\\frac{[A-\\left(DF\\right)\\left(B\\right)\\left(S\\right)]}{M}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cp\u003eA = (N\u003csub\u003e1\u003c/sub\u003e) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e (12693) (7)\u003c/p\u003e\u003cp\u003eB = (N\u003csub\u003e2\u003c/sub\u003e) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e (126.93) (8)\u003c/p\u003e \u003cp\u003eDF = (1\u0026thinsp;+\u0026thinsp;H) / F (9),\u003c/p\u003e \u003cp\u003ewhere the iodine adsorbed per gram of activated charcoal is represented as X/M (mg/g), DF represents the dilution factor, S is the volume of the sodium thiosulfate used (mL), M represents the quantity of activated charcoal used (g), N\u003csub\u003e1\u003c/sub\u003e is the normality of iodine (N), N\u003csub\u003e2\u003c/sub\u003e represents the normality of sodium thiosulphate (N), I is the iodine volume used (mL), H is the 5% HCl utilized (mL) and F represents the filtrate (mL).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAttrition measurement\u003c/h2\u003e \u003cp\u003eThe hardness of the activated charcoal was determined following the wet attrition test as described by Toles \u003cem\u003eet al.\u003c/em\u003e (Toles et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). 1 g of powdered cassava stem-derived activated charcoal was weighed and transferred into 100 mL of 0.07 M sodium acetate 0.03 M acetic acid buffer solution of pH 4.8. The mixture was subsequently stirred at 500 rpm for 24 h at 25 \u003csup\u003e0\u003c/sup\u003eC. Thereafter the mixture was placed on a 50-mesh sieve and using distilled water, it was washed. The samples were then dried by heating to 105 \u003csup\u003e0\u003c/sup\u003eC for 2 h and finally allowed to cool and the final weight determined. Calculation of the final attrition was carried out using the following Eq.\u0026nbsp;10.\u003c/p\u003e \u003cp\u003e% attrition = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{initial\\:weight\\:\\left(g\\right)\\:-final\\:weight\\:\\left(g\\right)\\:}{initial\\:weight\\:\\left(g\\right)\\:}\\)\u003c/span\u003e\u003c/span\u003e \u0026times; 100 (10)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSurface area determination\u003c/h2\u003e \u003cp\u003eThe surface area of the cassava stem-derived activated charcoal was determined following Sear\u0026rsquo;s approximation method with slight modification (Abate et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). 1 g of the powdered activated charcoal was mixed with 20 g NaCl, and the mixture dissolved with 100 mL of distilled water in a 250 mL conical flask. The mixture was constantly stirred for five minutes after the dissolution. Afterwards, the solution pH was adjusted to 4, and the obtained solution was titrated against 0.1 M NaOH until the pH finally reached 9. The NaOH volumes needed to change the pH values from 4 to 9 were recorded. The sample specific surface area was hence obtained using Eq.\u0026nbsp;11.\u003c/p\u003e \u003cp\u003eSpecific surface area (m\u003csup\u003e2\u003c/sup\u003e/g) = 32V \u0026ndash; 25 (11),\u003c/p\u003e \u003cp\u003ewhere V represents volume of 0.1 M NaOH needed to raise the pH from 4.0 to 9.0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStandard Mn(II) solution\u003c/h2\u003e \u003cp\u003eMn(II) stock solution of 1000 mg/L was prepared by dissolving 3.1393 g of Manganese sulphate monohydrate (MnSO\u003csub\u003e4\u003c/sub\u003e.H\u003csub\u003e2\u003c/sub\u003eO) in 250 mL of distilled water and the obtained solution was diluted to 1,000 mL mark of volumetric flask using distilled water. The stock solution was diluted with distilled water to prepare working concentrations. Concentrations prior as well as subsequent adsorption were estimated using UV \u0026ndash; Vis spectrophotometer at a wavelength of 525 nm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAdsorption Isotherm, Experiments and Modeling\u003c/h2\u003e \u003cp\u003eThe isotherms equilibrium experiments were conducted using conical flasks containing 0.47 g/L of activated carbon (optimized dose) and 100 mL of Mn(II) ion solution with initial Mn(II) ion concentrations (500, 1000, 1500, 2000, 2500, 3000, 3500 and 4000 mg/L) under an optimized temperature (45 \u003csup\u003e0\u003c/sup\u003eC). The solutions pH was adjusted to 9.0 (optimized pH) using 0.1M NaOH or 0.1M HCl. The mixture in each flask was agitated at a constant speed of 100 rpm for equilibrium time of 200 min. Each flask content was filtered and the residual Mn(II) ions in the filtrate were analyzed at a wavelength of 525 nm using UV \u0026ndash; Vis spectrophotometer. The quantity of Mn(II) ion removed at equilibrium was obtained using Eq.\u0026nbsp;12.\u003c/p\u003e \u003cp\u003e \u003cem\u003eq\u003c/em\u003e \u003csub\u003e \u003cem\u003et\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e=\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{(C0-Ct)}{m}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eV\u003c/em\u003e (12)\u003c/p\u003e \u003cp\u003eThe obtained experimental equilibrium data were applied to analyze Langmuir and Freundlich models and the linear regressions obtained were used to determine the model with a better fit.\u003c/p\u003e \u003cp\u003eBelow is Langmuir isotherm model equation:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{Ce}{qe}\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e=\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{qmKL}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{Ce}{qm}\\)\u003c/span\u003e\u003c/span\u003e (13)\u003c/p\u003e \u003cp\u003eThe constants K\u003csub\u003eL\u003c/sub\u003e and q\u003csub\u003em\u003c/sub\u003e were derived from Eq.\u0026nbsp;13 by plotting \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{C}\\text{e}}{\\text{q}\\text{e}}\\)\u003c/span\u003e\u003c/span\u003e against \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e where the slope is represented as\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\frac{Ce}{qm}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\frac{1}{qmKL}\\)\u003c/span\u003e\u003c/span\u003erepresenting the intercept. The adsorption process fitness to Langmuir isotherm model was examined using Eq.\u0026nbsp;14. The obtained value of R\u003csub\u003eL\u003c/sub\u003e was applied to determine the isotherm model\u0026rsquo;s shape (Chakravarty et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eR\u003c/em\u003e \u003csub\u003e \u003cem\u003eL\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e=\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{1+KLCo}\\)\u003c/span\u003e\u003c/span\u003e (14)\u003c/p\u003e \u003cp\u003ewhere, R\u003csub\u003eL\u003c/sub\u003e represents separation factor, C\u003csub\u003e0\u003c/sub\u003e represents the initial Mn(II) ion concentration and K\u003csub\u003eL\u003c/sub\u003e is the Langmuir constant (L/mg) associated to the adsorption energy through the Arrhenius equation.\u003c/p\u003e \u003cp\u003eEq.\u0026nbsp;15 represents Freundlich isotherm model.\u003c/p\u003e \u003cp\u003elog q\u003csub\u003ee\u003c/sub\u003e = log K\u003csub\u003eF\u003c/sub\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{\\text{n}}\\)\u003c/span\u003e\u003c/span\u003e log C\u003csub\u003ee\u003c/sub\u003e (15)\u003c/p\u003e \u003cp\u003ewhere, q\u003csub\u003ee\u003c/sub\u003e represents the removal capacity, C\u003csub\u003ee\u003c/sub\u003e represents the adsorbent concentration in the solution at equilibrium, K\u003csub\u003eF\u003c/sub\u003e is a constant representing the adsorption capacity of the activated carbon and n represents the constant of the adsorption intensity. Using the plot of log q\u003csub\u003ee\u003c/sub\u003e against log C\u003csub\u003ee\u003c/sub\u003e in Eq.\u0026nbsp;15, the constant K\u003csub\u003eF\u003c/sub\u003e was derived from the intercept of log K\u003csub\u003eF\u003c/sub\u003e and the constant n was obtained from the slope of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{n}\\)\u003c/span\u003e\u003c/span\u003e. The surface heterogeneity or adsorption intensity of the activated carbon was measured from the slope ranging from 0 to 1. Proximity of the value to zero, makes the surface of the adsorbent more heterogeneous. Also, an obtained value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{n}\\)\u003c/span\u003e\u003c/span\u003e less than 1 demonstrated a chemisorption process, but an obtained value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{n}\\)\u003c/span\u003e\u003c/span\u003e greater than 1 usually indicates cooperative adsorption (Brasquet et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Haghseresht and Lu, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The linear plots of Langmuir and Freundlich isotherm models are represented in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e respectively whereas the obtained results of the constants and correlation coefficients (R\u003csup\u003e2\u003c/sup\u003e) of the two models are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAdsorption Kinetic, Experiments and Modeling\u003c/h2\u003e \u003cp\u003eThe adsorption kinetics experiment was conducted using conical flasks of 250 mL capacity containing a mixture of 0.47 g/L of activated carbon (optimized dose) and 100 mL of Mn(II) ion solutions at specific times (10, 20, 30, 60, 90, 120, 150, 180, 210 and 240 min) respectively using the highest initial Mn(II) ion concentration of 4000 mg/L at an optimized temperature of 45 \u003csup\u003e0\u003c/sup\u003eC. The solutions pH was adjusted to an optimized pH of 9.0 using 0.1 M HCl or 0.1 M NaOH. The activated carbon-Mn(II) ion solutions were agitated at a constant speed of 100 rpm to achieve equilibrium time (10, 20, 30, 60, 90, 120, 150, 180, 210 and 240 min) respectively. Each flask content was filtered with Whatman filter paper at the stop of each adsorption time and the residual Mn(II) ions in the filtrate were analyzed at a wavelength of 525 nm using UV \u0026ndash; Vis spectrophotometer. The amount of Mn(II) ion adsorbed at a particular period (t) was obtained using Eq.\u0026nbsp;12 above.\u003c/p\u003e \u003cp\u003eUsing the obtained experimental data from the kinetic experiment, the adsorption mechanism of Mn(II) ion onto the activated carbon was determined by testing the pseudo-first-order and the pseudo-second-order.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCharacterization\u003c/h2\u003e \u003cp\u003eThe results obtained from the characterization of the powdered activated charcoal are found 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\u003eCharacterization of the prepared activated charcoal\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObtained values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolatile matter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsh content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed carbon content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulk density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 g/mL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIodine number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1080.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 mg/g\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurface area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1101\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20 m\u003csup\u003e2\u003c/sup\u003e/g\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12%\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=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003epH\u003c/h2\u003e \u003cp\u003epH is a strong indicator of the efficacy of adsorption process, hence adsorption using activated charcoal is affected by pH. At neutral and moderate (within the lower alkaline region) pH, activated charcoals are more effective in adsorption than at lower and higher pH (Evbuomwan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The pH value of the activated charcoal obtained in this work from Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e falls within the pH range of most commercial activated charcoal (pH 7 to pH 9) (Erhayem et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMoisture Content\u003c/h2\u003e \u003cp\u003eAn excellent adsorbent such as activated charcoal should be low in moisture content (Ekpete et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The moisture content recorded in this work was 4.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29% which was lower than work conducted by Ulfah \u003cem\u003eet al.\u003c/em\u003e whose moisture content was 5.42% (Ulfah et al., 2016). The obtained result in this study suggest that the prepared activated charcoal will make a better adsorbent since it has lower moisture content. Relative to the moisture content obtained by Ulfah \u003cem\u003eet al.\u003c/em\u003e and Devi \u003cem\u003eet al.\u003c/em\u003e it could be concluded that the activated charcoal prepared is of appreciable quality and thus, indicates that cassava stem is a suitable precursor for preparing high performance activated charcoal (Ulfah et al., 2016; Devi et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eVolatile Content\u003c/h2\u003e \u003cp\u003eA low volatile matter of 23.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was obtained for the activated charcoal. The value was lower compared to work conducted by Noor \u003cem\u003eet al.\u003c/em\u003e who obtained 81.51% volatile matter using cassava stem (Noor et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The low percentage of volatile matter of the activated charcoal in this work might be because of the dehydrating effect of the crude potash (salt), the chemical activating agent which could have caused the biomass to vaporize and strengthen the covalent bond in the carbon matrix leaving no chance for the loss of carbon (Noor et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eAsh Content\u003c/h2\u003e \u003cp\u003eThe obtained ash content of the activated charcoal was 3.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Ash content is the actual characteristic of activated charcoals. Higher ash content decreases the efficacy of activated charcoal. The value of the ash content obtained in this study was lower compared to works carried out by Noor \u003cem\u003eet al.\u003c/em\u003e(Noor et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The lower percentage of ash content derived in this study indicates that the activated charcoal produced is of remarkable quality and hence will make a better adsorbent.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eFixed Carbon Content\u003c/h2\u003e \u003cp\u003eCarbon content refers to the percentage of carbon that remains after the volatiles are eliminated [41]. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows a high carbon content of 69.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09% which is an indication that the prepared activated charcoal has appreciable quality compared to work done by Noor \u003cem\u003eet al.\u003c/em\u003e who obtained 16.07% using cassava stem as a precursor (Noor et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It also indicates that the activated charcoal would have a high adsorption capacity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eBulk density\u003c/h2\u003e \u003cp\u003eBulk density refers to the mass of carbon contained in a filter of a given solid and treated amount of liquid that a filter cake can retain. The filterability of the activated charcoal becomes better with higher density (Nyamful et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A bulk density of 0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 g/mL (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was recorded for the cassava stem-derived activated charcoal. The material hardness could have contributed to this result. The value of bulk density in this study agreed closely to work done by Ekpete \u003cem\u003eet al.\u003c/em\u003e (Ekpete et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eIodine number\u003c/h2\u003e \u003cp\u003eThe iodine number is usually used for rough estimation of surface area of activated charcoal at conditions of room temperature. It represents the relative indicator of the porosity and adsorbent capacity in an activated charcoal. Higher iodine numbers of activated charcoal are usually attributed to the availability of micropore structure and to the great approximation of the activated charcoal to possess a large surface area because of the enlargement of their pore structures (Ekpete and Harcourt, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The iodine number obtained in this study was 1080.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 mg/g (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The value obtained is relatively close to work conducted by Li \u003cem\u003eet al.\u003c/em\u003e who reported a maximum iodine number of 1085 mg/g (Li et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eSurface area\u003c/h2\u003e \u003cp\u003eThe prepared activated charcoal\u0026rsquo;s specific surface area based on Sear method of analysis obtained in this study was 1101\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20 m\u003csup\u003e2\u003c/sup\u003e/g (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Based on the value obtained for surface area in this work, it can be concluded that the obtained activated charcoal has a very high surface area, which might have arisen from the crude potash activation. The result obtained in this research agreed closely to the work done by Xin-hui \u003cem\u003eet al.\u003c/em\u003e and Abate \u003cem\u003eet al.\u003c/em\u003e (Xin-hui et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Abate et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eAttrition measurement\u003c/h2\u003e \u003cp\u003eAnother important physical property of any activated charcoal is its attrition or hardness, especially granular activated charcoals which due to their nature of applications may encounter intraparticle abrasion. The relatively low density of the cassava stem precursor might have caused the relatively high attrition value observed in this study. The value obtained (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) is in concordance with work done by Toles \u003cem\u003eet al\u003c/em\u003e. (Toles et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAdsorption Isotherms and Modeling\u003c/h3\u003e\n\u003cp\u003eThe detailed results of the Langmuir and Freundlich isotherm models are found in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\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\u003eData on Langmuir Isotherm\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003csub\u003e0\u003c/sub\u003e (mg/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003ee\u003c/sub\u003e (mg/L)\u003c/p\u003e \u003cp\u003eRep 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003csub\u003ee\u003c/sub\u003e (mg/L)\u003c/p\u003e \u003cp\u003eRep 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage reps of\u003c/p\u003e \u003cp\u003eC\u003csub\u003ee\u003c/sub\u003e(mg/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eq\u003csub\u003ee\u003c/sub\u003e (mg/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC\u003csub\u003ee\u003c/sub\u003e/q\u003csub\u003ee\u003c/sub\u003e (g/L)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e147.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e349.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e347.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e348.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e138.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.510\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e585.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e582.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e584.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e183.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e847.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e849.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e848.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e245.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1138.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1136.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1137.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e289.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1462.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1458.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1460.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e327.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1848.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1851.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e351.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2241.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2238.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e374.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.982\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 \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\u003eData on Freundlich Isotherm\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003csub\u003e0\u003c/sub\u003e (mg/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003ee\u003c/sub\u003e (mg/L)\u003c/p\u003e \u003cp\u003eRep1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003csub\u003ee\u003c/sub\u003e (mg/L)\u003c/p\u003e \u003cp\u003eRep 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003cp\u003eC\u003csub\u003ee\u003c/sub\u003e (mg/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eq\u003csub\u003ee\u003c/sub\u003e (mg/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003elog C\u003csub\u003ee\u003c/sub\u003e (mg/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003elog q\u003csub\u003ee\u003c/sub\u003e (mg/g)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e147.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e349.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e347.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e348.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e138.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e585.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e582.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e584.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e183.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e847.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e849.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e848.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e245.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1138.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1136.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1137.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e289.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1462.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1458.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1460.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e327.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1848.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1851.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e351.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2241.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2238.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e374.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.573\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 \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\u003eSummarized results of the isotherm models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eFreundlich\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eq\u003csub\u003em\u003c/sub\u003e(mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eK\u003csub\u003eL\u003c/sub\u003e (L/mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csub\u003eL\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eK\u003csub\u003eF\u003c/sub\u003e (mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1/n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e555.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.596 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.9881\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\u003eFrom Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e) of 0.9931 of the Langmuir isotherm models showed its fitness to the equilibrium data better than the Freundlich model. This fitness of the Langmuir model could be an indication of monolayer adsorption by the cassava stem-crude potash activated carbon surface possessing a fixed number of adsorption sites.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicates that the R\u003csub\u003eL\u003c/sub\u003e value was between 0 and 1, which demonstrated that under the studied conditions, the adsorption process was favourable. The monolayer adsorption capacity (q\u003csub\u003em\u003c/sub\u003e) value was found to be 555.56 mg/g indicating the high removal capacity of the cassava stem-crude potash activated carbon. The value of K\u003csub\u003eL\u003c/sub\u003e was relatively high indicating that the surface of the activated carbon possessed high energy and consequently high bonding between Mn(II) ions and the activated carbon.\u003c/p\u003e \u003cp\u003eResearchers have earlier investigated the adsorption capacities of various activated carbons on adsorption of Mn(II) onto the surfaces of these activated carbons. The reported maximum monolayer adsorption capacities of Mn(II) onto various activated carbons are listed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. However, the adsorption capacity of cassava stem-derived activated carbon (555.56 mg/g) obtained in this study was a very large value compared to the values obtained by other researchers as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. This implies that the large value obtained in this study makes the cassava stem activated carbon to be a promising inexpensive adsorbent for the adsorption of Mn(II) ion from aqueous solution.\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\u003eMaximum monolayer capacities of activated carbons for removal of Mn(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=\"char\" char=\".\" 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\u003eAdsorbent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdsorption Capacity (mg/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\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\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiksirat et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e172.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOmri and Benzina, (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e149.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRachel et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEl-sherif et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWanjari, (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanola flower, activated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFeizi and Jalali, (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotato, activated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFeizi and Jalali, (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSunflower, activated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFeizi and Jalali, (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCassava stem activated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e555.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis study\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\u003eSimilarly, the Freundlich model isotherm (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) also had a fit to the experimental data. But the level of fitness was low compared to the fitness of the Langmuir model as indicated by the Freundlich correlation coefficient of 0.9881. From Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e above, the value of n was obtained as 1.654 proving that the activated carbon had a heterogenous surface since the obtained value falls within the heterogeneity range of 1\u0026thinsp;\u0026lt;\u0026thinsp;n\u0026thinsp;\u0026lt;\u0026thinsp;10. Again, the result of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{n}\\)\u003c/span\u003e\u003c/span\u003e was less than 1, indicating a chemical adsorption process. The value of K\u003csub\u003eF\u003c/sub\u003e (3.867 mg/g) was not very high showing that there was low adsorption of the Mn(II) ions onto the activated carbon surface.\u003c/p\u003e \u003cp\u003eThe investigated models when compared, the Langmuir model demonstrated a better fitness to the equilibrium data compared to the Freundlich model, thereby indicating a monolayer adsorption. Studies from literature has shown that the Langmuir isotherm model is more frequently used than the Freundlich model and other isotherm models in the adsorption studies of heavy metals onto activated carbon as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and confirmed in this study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReported adsorption isotherm models of activated carbon in heavy metal removal\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\u003eAdsorbent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeavy metal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBest-fit isotherm model\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\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCu(II) Zn(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKazmierczak-razna et al., (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMn(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLagmuir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOmri and Benzina, (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCu(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFreundlich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMuslim and Said, (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMn(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFreundlich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMengistie et al., (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorn straw porous carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCr (VI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMa et al., (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRice husk carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCr(VI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKhan et al., (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWalnut shell activated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCr(VI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNethaji and Sivasamy, (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMn(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFreundlich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNiksirat et al., (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMesoporous carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMn(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnbia and Amirmahmoodi, (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePb(II) Ni(II) Cd(II) Zn(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFreundlich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKarnib et al., (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMn(II) Fe(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFowler - Guggenheim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKoubaissy et al., (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMn(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLangmuir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eAdsorption Kinetics and Modeling\u003c/h2\u003e \u003cp\u003eThe detailed results of the pseudo-first-order and pseudo-second-order kinetic models are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and summarized in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData for kinetic studies using the highest Initial concentration of 4000 mg/L\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003ee\u003c/sub\u003e (mg/L)\u003c/p\u003e \u003cp\u003eRep 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003csub\u003ee\u003c/sub\u003e (mg/L)\u003c/p\u003e \u003cp\u003eRep 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage of Reps (mg/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eq\u003csub\u003ee\u003c/sub\u003e (mg/g)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3060.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3064.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3062.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e199.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2768.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2764.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2766.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e262.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2471.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2469.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2470.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e325.45\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2331.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2327.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2329.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e355.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2232.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2235.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2233.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e375.84\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2161.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2167.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2164.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e390.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2116.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2111.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2114.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e401.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2069.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2071.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2070.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e410.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2069.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2072.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2070.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e410.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2068.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2072.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2070.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e410.47\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData on pseudo-first-order\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eq\u003csub\u003et\u003c/sub\u003e (mg/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eq\u003csub\u003ee\u003c/sub\u003e (mg/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eq\u003csub\u003ee \u0026ndash;\u003c/sub\u003e q\u003csub\u003et\u003c/sub\u003e (mg/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003elog (q\u003csub\u003ee\u003c/sub\u003e \u0026ndash; q\u003csub\u003et\u003c/sub\u003e) [mg/g]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e199.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e262.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e325.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.93\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e355.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e375.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.53\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e390.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e401.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e410.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e410.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e410.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e410.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData on pseudo-second-order\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003et/q\u003csub\u003et\u003c/sub\u003e (min.g/mg)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.092\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.239\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.585\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummarized results of the kinetic models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePseudo-first-order\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003ePseudo-second-order\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003csub\u003ep1\u003c/sub\u003e(L/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eq\u003csub\u003ee\u003c/sub\u003e (mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK\u003csub\u003ep2\u003c/sub\u003e(L/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eq\u003csub\u003ee\u003c/sub\u003e (mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.91 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e434.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9997\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 \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e above indicates that the generated equilibrium data obtained from the adsorption of Mn(II) onto the activated carbon fitted the pseudo-first-order and pseudo-second-order kinetic models with impressive R\u003csup\u003e2\u003c/sup\u003e values respectively. However, it was seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e that the pseudo-second-order kinetic model showed excellent linearity with higher correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;0.999 when compared to the pseudo-first-order model; thereby emerging as a better fit. From Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, the obtained values of the adsorption capacity (qe) of the pseudo-second-order kinetic model proved its fitness to the experimental data in comparison to the pseudo-first-order model.\u003c/p\u003e \u003cp\u003eTrace metals adsorption from aqueous solution by carbon-based adsorbents is often explained by applying pseudo-first-order and pseudo-seond-order kinetic models. However the pseudo-second-order is often applied frequently and mostly emerges as the better fit (Ho, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This trend could be observed from Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e and comfirmed by the results obtained from this study. The pseudo-first-oder model assumes a reversible physisorption in the process of adsorption (Vikrant et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). But the pseudo-second-order is commonly used in heavy metals removal by carbon-based adsorbents, implying that the cabon-based adsorbents adsorption is an irreversible chemical adsorption process in most cases (Ma et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReported adsorption kinetic models of activated carbon in heavy metal removal\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\u003eAdsorbent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeavy metal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBest-fit kinetic models\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePb(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePseudo-second-order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNasirudden et al., (2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePb(II) Cd(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePseudo-second-order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSalman et al., (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNi(II) Cd(II) Co(II) Cu(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePseudo-second-order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAmadi et al, (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCd(II) Pb(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePseudo-second-order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAl-onazi et al., (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePb(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePseudo-second-order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbdulkarim and Al-rub, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCu(II) Zn(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePseudo-second-order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKazmierczak-razna et al., (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePb(II) Cu(II) Zn(II) Cd(II) Cr(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePseudo-first-order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSuo et al., (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePb(II) Cd(II) Cu(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePseudo-second-order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAli et al., (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZn(II) Cu(II) Pb(II) Fe(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePseudo-second-order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGin et al., (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMn(II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePseudo-second-order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThis study\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"},{"header":"Conclusion","content":"\u003cp\u003eThis study described the adsorption of Mn(II) ion onto activated carbon prepared from cassava stem. The activated carbon displayed promising results in adsorbing heavy metals especially Mn(II) from drinking and wastewater. According to the characterization results obtained, the cassava stem-derived activated charcoal proved to be a better adsorbent compared to other activated charcoal produced by some researchers using other precursors. This is because the obtained activated charcoal in this study was advantageous in moisture content, ash content, volatile matter, and high carbon content. The experimental equilibrium data of Mn(II) ion adsorption had a better fit with Langmuir isotherm model compared to the Freundlich isotherm model with an impressive maximum monolayer adsorption capacity (Q) of 555.56 mg/g. Moreover the R\u003csub\u003eL\u003c/sub\u003e value showed that the cassava stem-derived activated carbon was favourable for the removal of Mn(II) ion. The kinetic study also showed that the pseudo-second-order rate equation better described the adsorption process than the pseudo-first-order rate equation. These findings suggest that the prepared activated carbon has the potential to serve as a cost-effective and efficient alternative to commercial carbons, with promising applications in water purification by remediation of heavy metals [Mn(II)] from drinking and wastewater.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interest\u003c/h2\u003e \u003cp\u003eThe author declares no competing interest and manuscript is purely academic research and all references have been duly acknowledged.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe manuscript has received no funding from any source or institution and its purely an academic work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e \u003cp\u003eA.P (Alhassan Pont) gathered and analyzed the data, drafted, and prepared the manuscript for scientific clarity and coherence. A.P was responsible for editorial corrections. The author has read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThe author would like to express his appreciation to the management of the University for Development Studies, Nyankpala-Ghana, for equipping the Food Technology Laboratory and School of Engineering Laboratory for research work where this research was conducted.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbate GY, Alene AN, Habte AT, Getahun DM (2020) Adsorptive removal of malachite green dye from aqueous solution onto activated carbon of \u003cem\u003eCatha edulis\u003c/em\u003e stem as a low-cost bio \u0026ndash; adsorbent. 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BioMed Research International\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1155/2014/973095\u003c/span\u003e\u003cspan address=\"10.1155/2014/973095\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Y, Lu J, Zhou Y, Liu Y (2019) Recent advances for dyes removal using novel adsorbents: A review Environmental Pollution. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envpol.2019.05.072\u003c/span\u003e\u003cspan address=\"10.1016/j.envpol.2019.05.072\" 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":true,"hideJournal":true,"highlight":"","institution":"University for Development Studies","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":"Adsorption, Manganese, Langmuir model, cassava stem and isotherm","lastPublishedDoi":"10.21203/rs.3.rs-9301112/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9301112/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe high cost of commercial activated carbon due to its high production expenses, has driven increasing demand in lignocellulosic biomass-derived activated carbon as low-cost adsorbent for adsorbing emerging contaminants from drinking and wastewater due to its high surface area and high adsorption capacity. In this study, the mechanism of equilibrium adsorption and kinetics of Mn(II) ion in drinking and wastewater onto cassava stem-crude potash derived activated carbon were studied. The cassava stem was carbonized at 818.68 \u003csup\u003e0\u003c/sup\u003eC for 1.45 h at impregnation ratio of 2:1. The chemically activated cassava stem charcoal was characterized for pH of 7.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10, moisture content of 4.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29%, volatile matter of 23.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20%, ash content of 3.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12%, fixed carbon content of 69.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09%, bulk density of 0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 g/mL, iodine number of 1080.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 mg/g, surface area of 1101\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20 m\u003csup\u003e2\u003c/sup\u003e/g and attrition value of 62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12%. Data from equilibrium adsorption were analyzed with two model equations which were Langmuir and Freundlich models. The Langmuir (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9931) model correlated the experimental data better than the Freundlich (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9881) isotherm model. The maximum adsorption capacity (q\u003csub\u003em\u003c/sub\u003e), intensity of adsorption (K\u003csub\u003eL\u003c/sub\u003e) and separation factor (R\u003csub\u003eL\u003c/sub\u003e) were obtained from Langmuir plot. Data from adsorption kinetics were analyzed with pseudo-first-order and pseudo-second-order models. The results showed that the pseudo-second-order (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9997) was a better model, describing the adsorption kinetics data compared to the pseudo-first-order model (R\u0026thinsp;=\u0026thinsp;0.9789). The maximum monolayer adsorption capacity of the activated carbon was evaluated as 555.56 mg/g.\u003c/p\u003e","manuscriptTitle":"Removal of Mn(II) ion from aqueous solution onto cassava stem-derived activated carbon: Adsorption isotherms and kinetics studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 07:06:06","doi":"10.21203/rs.3.rs-9301112/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":"8b8e77a5-ec50-497e-91df-6edd605ca936","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65602965,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2026-04-03T07:06:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 07:06:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9301112","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9301112","identity":"rs-9301112","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}