Effectiveness and Characterization of Novel Mineral Clay in Cd[sup.2+] Adsorption Process: Linear and Non-Linear Isotherm Regression Analysis
The excellent adsorption properties of clay minerals make the optimization of heavy metal removal the subject of numerous research projects. In the present study, ASLAVITAL cosmetic clay (ACC) powder was applied for the removal of Cd[sup.2+] from water. The main deposit of ACC clay is the Pădurea Cr...
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description | The excellent adsorption properties of clay minerals make the optimization of heavy metal removal the subject of numerous research projects. In the present study, ASLAVITAL cosmetic clay (ACC) powder was applied for the removal of Cd[sup.2+] from water. The main deposit of ACC clay is the Pădurea Craiului Mountains in Romania. A wide range of morpho-structural approaches (SEM, EDX, FTIR, Raman, XRD) were used to characterize the morphology and elemental composition of the adsorbent. As expected for clay minerals, Al (Wt(%) = 11.4 ± 0.9) and Si (Wt(%) = 13.7 ± 1.4) are the main constituents of ACC. After adsorption, Wt(%) = 0.2 ± 0.01 Cd[sup.2+] was detected in the sample. As proved before, the initial metal concentration is the primary influencing factor; therefore, batch adsorption of 10–160 mg/L Cd[sup.2+] was investigated. After 190 min, an efficiency of 99% was reached, and the quantity in equilibrium increased from 1–8 mg/g. The best fit in linear form was obtained for the Langmuir II. model, where R[sup.2] = 0.954 (R[sub.L] = 0.037–0.027). Based on linear isotherm models, physical bonds formed between ACC and Cd[sup.2+] during the favorable adsorption. For the non-linear fits, the Liu model proved to be the best R[sup.2] = 0.965, χ[sup.2] = 1.101. Pseudo-II-order kinetic model described the experimental data R[sup.2] = 0.988–0.999; q[sub.exp] and q[sub.calc] were almost identical (the differences ranged 0.03–0.34). |
doi_str_mv | 10.3390/w14030279 |
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In the present study, ASLAVITAL cosmetic clay (ACC) powder was applied for the removal of Cd[sup.2+] from water. The main deposit of ACC clay is the Pădurea Craiului Mountains in Romania. A wide range of morpho-structural approaches (SEM, EDX, FTIR, Raman, XRD) were used to characterize the morphology and elemental composition of the adsorbent. As expected for clay minerals, Al (Wt(%) = 11.4 ± 0.9) and Si (Wt(%) = 13.7 ± 1.4) are the main constituents of ACC. After adsorption, Wt(%) = 0.2 ± 0.01 Cd[sup.2+] was detected in the sample. As proved before, the initial metal concentration is the primary influencing factor; therefore, batch adsorption of 10–160 mg/L Cd[sup.2+] was investigated. After 190 min, an efficiency of 99% was reached, and the quantity in equilibrium increased from 1–8 mg/g. The best fit in linear form was obtained for the Langmuir II. model, where R[sup.2] = 0.954 (R[sub.L] = 0.037–0.027). Based on linear isotherm models, physical bonds formed between ACC and Cd[sup.2+] during the favorable adsorption. For the non-linear fits, the Liu model proved to be the best R[sup.2] = 0.965, χ[sup.2] = 1.101. Pseudo-II-order kinetic model described the experimental data R[sup.2] = 0.988–0.999; q[sub.exp] and q[sub.calc] were almost identical (the differences ranged 0.03–0.34).</description><identifier>ISSN: 2073-4441</identifier><identifier>EISSN: 2073-4441</identifier><identifier>DOI: 10.3390/w14030279</identifier><language>eng</language><publisher>MDPI AG</publisher><subject>Adsorption ; Analysis ; Heavy metals ; Mineral industry ; Mining industry</subject><ispartof>Water (Basel), 2022-02, Vol.14 (3)</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Tonk, Szende</creatorcontrib><creatorcontrib>Aradi, László Előd</creatorcontrib><creatorcontrib>Kovács, Gábor</creatorcontrib><creatorcontrib>Turza, Alexandru</creatorcontrib><creatorcontrib>Rápó, Eszter</creatorcontrib><title>Effectiveness and Characterization of Novel Mineral Clay in Cd[sup.2+] Adsorption Process: Linear and Non-Linear Isotherm Regression Analysis</title><title>Water (Basel)</title><description>The excellent adsorption properties of clay minerals make the optimization of heavy metal removal the subject of numerous research projects. In the present study, ASLAVITAL cosmetic clay (ACC) powder was applied for the removal of Cd[sup.2+] from water. The main deposit of ACC clay is the Pădurea Craiului Mountains in Romania. A wide range of morpho-structural approaches (SEM, EDX, FTIR, Raman, XRD) were used to characterize the morphology and elemental composition of the adsorbent. As expected for clay minerals, Al (Wt(%) = 11.4 ± 0.9) and Si (Wt(%) = 13.7 ± 1.4) are the main constituents of ACC. After adsorption, Wt(%) = 0.2 ± 0.01 Cd[sup.2+] was detected in the sample. As proved before, the initial metal concentration is the primary influencing factor; therefore, batch adsorption of 10–160 mg/L Cd[sup.2+] was investigated. After 190 min, an efficiency of 99% was reached, and the quantity in equilibrium increased from 1–8 mg/g. The best fit in linear form was obtained for the Langmuir II. model, where R[sup.2] = 0.954 (R[sub.L] = 0.037–0.027). Based on linear isotherm models, physical bonds formed between ACC and Cd[sup.2+] during the favorable adsorption. For the non-linear fits, the Liu model proved to be the best R[sup.2] = 0.965, χ[sup.2] = 1.101. Pseudo-II-order kinetic model described the experimental data R[sup.2] = 0.988–0.999; q[sub.exp] and q[sub.calc] were almost identical (the differences ranged 0.03–0.34).</description><subject>Adsorption</subject><subject>Analysis</subject><subject>Heavy metals</subject><subject>Mineral industry</subject><subject>Mining industry</subject><issn>2073-4441</issn><issn>2073-4441</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid/><recordid>eNpNj8FKAzEQhoMoWGoPvkHusjXZZLO73palaqFWET2JSJpM2sg2Kclaqe_gOxtrD84c5p_h-38YhM4pGTNWk8tPygkjeVkfoUFOSpZxzunxP32KRjG-k1S8rqqCDND3xBhQvd2CgxixdBq3Kxmk6iHYL9lb77A3eO630OE76yDIDred3GHrcKtf4sdmnF-84kZHHzZ7_CF4lbKu8CzhMuwz595lh3Uafb-CsMaPsAyJ-7U0Tna7aOMZOjGyizA6zCF6vp48tbfZ7P5m2jazbElJ3We1pkJqIahSC70wIKRgKi9JkZe0ULwyAoCZHKTklTBMkpozQXNQhakKudBsiMZ_uUvZwZt1xvfp5dQa1lZ5B8ame1PWLBecJ_cPDZ1rEg</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Tonk, Szende</creator><creator>Aradi, László Előd</creator><creator>Kovács, Gábor</creator><creator>Turza, Alexandru</creator><creator>Rápó, Eszter</creator><general>MDPI AG</general><scope/></search><sort><creationdate>20220201</creationdate><title>Effectiveness and Characterization of Novel Mineral Clay in Cd[sup.2+] Adsorption Process: Linear and Non-Linear Isotherm Regression Analysis</title><author>Tonk, Szende ; Aradi, László Előd ; Kovács, Gábor ; Turza, Alexandru ; Rápó, Eszter</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-g109t-9d16ad661ccbdbfe6a63c27052715c48f6ee3f2eaa486f3a0943612ec5f85abd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Adsorption</topic><topic>Analysis</topic><topic>Heavy metals</topic><topic>Mineral industry</topic><topic>Mining industry</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tonk, Szende</creatorcontrib><creatorcontrib>Aradi, László Előd</creatorcontrib><creatorcontrib>Kovács, Gábor</creatorcontrib><creatorcontrib>Turza, Alexandru</creatorcontrib><creatorcontrib>Rápó, Eszter</creatorcontrib><jtitle>Water (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tonk, Szende</au><au>Aradi, László Előd</au><au>Kovács, Gábor</au><au>Turza, Alexandru</au><au>Rápó, Eszter</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Effectiveness and Characterization of Novel Mineral Clay in Cd[sup.2+] Adsorption Process: Linear and Non-Linear Isotherm Regression Analysis</atitle><jtitle>Water (Basel)</jtitle><date>2022-02-01</date><risdate>2022</risdate><volume>14</volume><issue>3</issue><issn>2073-4441</issn><eissn>2073-4441</eissn><abstract>The excellent adsorption properties of clay minerals make the optimization of heavy metal removal the subject of numerous research projects. In the present study, ASLAVITAL cosmetic clay (ACC) powder was applied for the removal of Cd[sup.2+] from water. The main deposit of ACC clay is the Pădurea Craiului Mountains in Romania. A wide range of morpho-structural approaches (SEM, EDX, FTIR, Raman, XRD) were used to characterize the morphology and elemental composition of the adsorbent. As expected for clay minerals, Al (Wt(%) = 11.4 ± 0.9) and Si (Wt(%) = 13.7 ± 1.4) are the main constituents of ACC. After adsorption, Wt(%) = 0.2 ± 0.01 Cd[sup.2+] was detected in the sample. As proved before, the initial metal concentration is the primary influencing factor; therefore, batch adsorption of 10–160 mg/L Cd[sup.2+] was investigated. After 190 min, an efficiency of 99% was reached, and the quantity in equilibrium increased from 1–8 mg/g. The best fit in linear form was obtained for the Langmuir II. model, where R[sup.2] = 0.954 (R[sub.L] = 0.037–0.027). Based on linear isotherm models, physical bonds formed between ACC and Cd[sup.2+] during the favorable adsorption. For the non-linear fits, the Liu model proved to be the best R[sup.2] = 0.965, χ[sup.2] = 1.101. Pseudo-II-order kinetic model described the experimental data R[sup.2] = 0.988–0.999; q[sub.exp] and q[sub.calc] were almost identical (the differences ranged 0.03–0.34).</abstract><pub>MDPI AG</pub><doi>10.3390/w14030279</doi></addata></record> |
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subjects | Adsorption Analysis Heavy metals Mineral industry Mining industry |
title | Effectiveness and Characterization of Novel Mineral Clay in Cd[sup.2+] Adsorption Process: Linear and Non-Linear Isotherm Regression Analysis |
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