Optimization of Sodium Alginate-Graphene Nanoplate-Kaolin Bio-composite Adsorbents in Heavy Metal Adsorption by Response Surface Methodology (RSM)
Abstract In this study, graphene nanoplate-kaolin-sodium alginate (GKS) bio-composite adsorbent was prepared, and the copper removal from wastewater by the adsorption method was investigated. Characterization of adsorbent was carried out using a scanning electron microscope, Fourier transforms infra...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ünügül, Tuba [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© King Fahd University of Petroleum & Minerals 2021 |
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| Übergeordnetes Werk: |
Enthalten in: The Arabian journal for science and engineering - Berlin : Springer, 2011, 47(2021), 5 vom: 25. Juni, Seite 6001-6012 |
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| Übergeordnetes Werk: |
volume:47 ; year:2021 ; number:5 ; day:25 ; month:06 ; pages:6001-6012 |
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| DOI / URN: |
10.1007/s13369-021-05905-z |
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SPR047006447 |
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| 520 | |a Abstract In this study, graphene nanoplate-kaolin-sodium alginate (GKS) bio-composite adsorbent was prepared, and the copper removal from wastewater by the adsorption method was investigated. Characterization of adsorbent was carried out using a scanning electron microscope, Fourier transforms infrared spectroscopy, and thermo-gravimetric analysis methods. In addition to the parametric adsorption studies, experimental optimization was also applied with parameters determined by the central composite design of response surface methodology (RSM). Effects of copper concentration (from 10 to 50 ppm), pH (from 3 to 7), and adsorption dosage (from 0.05 to 0.15 g/L) were investigated to determine the optimum design points. In order to determine the efficiency of heavy metal adsorption, four experimental parameters (adsorbent dosage, metal concentration, pH, and contact time) were evaluated. As a result, the highest removal of 92.12% was obtained when the heavy metal concentration was 10 mg/L, the adsorbent dosage was 0.15 g, the solution pH was 7, and the contact time was 180 min. Adsorption isotherm studies were also carried out. The appropriate adsorption isotherm for copper removal using GKS was determined as Langmuir isotherm. According to the optimization results, the quadratic model with an R2 of 0.9946 was found to be the most suitable model. | ||
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10.1007/s13369-021-05905-z doi (DE-627)SPR047006447 (SPR)s13369-021-05905-z-e DE-627 ger DE-627 rakwb eng Ünügül, Tuba verfasserin (orcid)0000-0002-2502-1129 aut Optimization of Sodium Alginate-Graphene Nanoplate-Kaolin Bio-composite Adsorbents in Heavy Metal Adsorption by Response Surface Methodology (RSM) 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2021 Abstract In this study, graphene nanoplate-kaolin-sodium alginate (GKS) bio-composite adsorbent was prepared, and the copper removal from wastewater by the adsorption method was investigated. Characterization of adsorbent was carried out using a scanning electron microscope, Fourier transforms infrared spectroscopy, and thermo-gravimetric analysis methods. In addition to the parametric adsorption studies, experimental optimization was also applied with parameters determined by the central composite design of response surface methodology (RSM). Effects of copper concentration (from 10 to 50 ppm), pH (from 3 to 7), and adsorption dosage (from 0.05 to 0.15 g/L) were investigated to determine the optimum design points. In order to determine the efficiency of heavy metal adsorption, four experimental parameters (adsorbent dosage, metal concentration, pH, and contact time) were evaluated. As a result, the highest removal of 92.12% was obtained when the heavy metal concentration was 10 mg/L, the adsorbent dosage was 0.15 g, the solution pH was 7, and the contact time was 180 min. Adsorption isotherm studies were also carried out. The appropriate adsorption isotherm for copper removal using GKS was determined as Langmuir isotherm. According to the optimization results, the quadratic model with an R2 of 0.9946 was found to be the most suitable model. Batch adsorption (dpeaa)DE-He213 Bio-composite adsorbent (dpeaa)DE-He213 Experimental design (dpeaa)DE-He213 Heavy metal removal (dpeaa)DE-He213 Nigiz, Filiz Ugur (orcid)0000-0003-0509-8425 aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 47(2021), 5 vom: 25. Juni, Seite 6001-6012 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:47 year:2021 number:5 day:25 month:06 pages:6001-6012 https://dx.doi.org/10.1007/s13369-021-05905-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 47 2021 5 25 06 6001-6012 |
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10.1007/s13369-021-05905-z doi (DE-627)SPR047006447 (SPR)s13369-021-05905-z-e DE-627 ger DE-627 rakwb eng Ünügül, Tuba verfasserin (orcid)0000-0002-2502-1129 aut Optimization of Sodium Alginate-Graphene Nanoplate-Kaolin Bio-composite Adsorbents in Heavy Metal Adsorption by Response Surface Methodology (RSM) 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2021 Abstract In this study, graphene nanoplate-kaolin-sodium alginate (GKS) bio-composite adsorbent was prepared, and the copper removal from wastewater by the adsorption method was investigated. Characterization of adsorbent was carried out using a scanning electron microscope, Fourier transforms infrared spectroscopy, and thermo-gravimetric analysis methods. In addition to the parametric adsorption studies, experimental optimization was also applied with parameters determined by the central composite design of response surface methodology (RSM). Effects of copper concentration (from 10 to 50 ppm), pH (from 3 to 7), and adsorption dosage (from 0.05 to 0.15 g/L) were investigated to determine the optimum design points. In order to determine the efficiency of heavy metal adsorption, four experimental parameters (adsorbent dosage, metal concentration, pH, and contact time) were evaluated. As a result, the highest removal of 92.12% was obtained when the heavy metal concentration was 10 mg/L, the adsorbent dosage was 0.15 g, the solution pH was 7, and the contact time was 180 min. Adsorption isotherm studies were also carried out. The appropriate adsorption isotherm for copper removal using GKS was determined as Langmuir isotherm. According to the optimization results, the quadratic model with an R2 of 0.9946 was found to be the most suitable model. Batch adsorption (dpeaa)DE-He213 Bio-composite adsorbent (dpeaa)DE-He213 Experimental design (dpeaa)DE-He213 Heavy metal removal (dpeaa)DE-He213 Nigiz, Filiz Ugur (orcid)0000-0003-0509-8425 aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 47(2021), 5 vom: 25. Juni, Seite 6001-6012 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:47 year:2021 number:5 day:25 month:06 pages:6001-6012 https://dx.doi.org/10.1007/s13369-021-05905-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 47 2021 5 25 06 6001-6012 |
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10.1007/s13369-021-05905-z doi (DE-627)SPR047006447 (SPR)s13369-021-05905-z-e DE-627 ger DE-627 rakwb eng Ünügül, Tuba verfasserin (orcid)0000-0002-2502-1129 aut Optimization of Sodium Alginate-Graphene Nanoplate-Kaolin Bio-composite Adsorbents in Heavy Metal Adsorption by Response Surface Methodology (RSM) 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2021 Abstract In this study, graphene nanoplate-kaolin-sodium alginate (GKS) bio-composite adsorbent was prepared, and the copper removal from wastewater by the adsorption method was investigated. Characterization of adsorbent was carried out using a scanning electron microscope, Fourier transforms infrared spectroscopy, and thermo-gravimetric analysis methods. In addition to the parametric adsorption studies, experimental optimization was also applied with parameters determined by the central composite design of response surface methodology (RSM). Effects of copper concentration (from 10 to 50 ppm), pH (from 3 to 7), and adsorption dosage (from 0.05 to 0.15 g/L) were investigated to determine the optimum design points. In order to determine the efficiency of heavy metal adsorption, four experimental parameters (adsorbent dosage, metal concentration, pH, and contact time) were evaluated. As a result, the highest removal of 92.12% was obtained when the heavy metal concentration was 10 mg/L, the adsorbent dosage was 0.15 g, the solution pH was 7, and the contact time was 180 min. Adsorption isotherm studies were also carried out. The appropriate adsorption isotherm for copper removal using GKS was determined as Langmuir isotherm. According to the optimization results, the quadratic model with an R2 of 0.9946 was found to be the most suitable model. Batch adsorption (dpeaa)DE-He213 Bio-composite adsorbent (dpeaa)DE-He213 Experimental design (dpeaa)DE-He213 Heavy metal removal (dpeaa)DE-He213 Nigiz, Filiz Ugur (orcid)0000-0003-0509-8425 aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 47(2021), 5 vom: 25. Juni, Seite 6001-6012 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:47 year:2021 number:5 day:25 month:06 pages:6001-6012 https://dx.doi.org/10.1007/s13369-021-05905-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 47 2021 5 25 06 6001-6012 |
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10.1007/s13369-021-05905-z doi (DE-627)SPR047006447 (SPR)s13369-021-05905-z-e DE-627 ger DE-627 rakwb eng Ünügül, Tuba verfasserin (orcid)0000-0002-2502-1129 aut Optimization of Sodium Alginate-Graphene Nanoplate-Kaolin Bio-composite Adsorbents in Heavy Metal Adsorption by Response Surface Methodology (RSM) 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2021 Abstract In this study, graphene nanoplate-kaolin-sodium alginate (GKS) bio-composite adsorbent was prepared, and the copper removal from wastewater by the adsorption method was investigated. Characterization of adsorbent was carried out using a scanning electron microscope, Fourier transforms infrared spectroscopy, and thermo-gravimetric analysis methods. In addition to the parametric adsorption studies, experimental optimization was also applied with parameters determined by the central composite design of response surface methodology (RSM). Effects of copper concentration (from 10 to 50 ppm), pH (from 3 to 7), and adsorption dosage (from 0.05 to 0.15 g/L) were investigated to determine the optimum design points. In order to determine the efficiency of heavy metal adsorption, four experimental parameters (adsorbent dosage, metal concentration, pH, and contact time) were evaluated. As a result, the highest removal of 92.12% was obtained when the heavy metal concentration was 10 mg/L, the adsorbent dosage was 0.15 g, the solution pH was 7, and the contact time was 180 min. Adsorption isotherm studies were also carried out. The appropriate adsorption isotherm for copper removal using GKS was determined as Langmuir isotherm. According to the optimization results, the quadratic model with an R2 of 0.9946 was found to be the most suitable model. Batch adsorption (dpeaa)DE-He213 Bio-composite adsorbent (dpeaa)DE-He213 Experimental design (dpeaa)DE-He213 Heavy metal removal (dpeaa)DE-He213 Nigiz, Filiz Ugur (orcid)0000-0003-0509-8425 aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 47(2021), 5 vom: 25. Juni, Seite 6001-6012 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:47 year:2021 number:5 day:25 month:06 pages:6001-6012 https://dx.doi.org/10.1007/s13369-021-05905-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 47 2021 5 25 06 6001-6012 |
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10.1007/s13369-021-05905-z doi (DE-627)SPR047006447 (SPR)s13369-021-05905-z-e DE-627 ger DE-627 rakwb eng Ünügül, Tuba verfasserin (orcid)0000-0002-2502-1129 aut Optimization of Sodium Alginate-Graphene Nanoplate-Kaolin Bio-composite Adsorbents in Heavy Metal Adsorption by Response Surface Methodology (RSM) 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2021 Abstract In this study, graphene nanoplate-kaolin-sodium alginate (GKS) bio-composite adsorbent was prepared, and the copper removal from wastewater by the adsorption method was investigated. Characterization of adsorbent was carried out using a scanning electron microscope, Fourier transforms infrared spectroscopy, and thermo-gravimetric analysis methods. In addition to the parametric adsorption studies, experimental optimization was also applied with parameters determined by the central composite design of response surface methodology (RSM). Effects of copper concentration (from 10 to 50 ppm), pH (from 3 to 7), and adsorption dosage (from 0.05 to 0.15 g/L) were investigated to determine the optimum design points. In order to determine the efficiency of heavy metal adsorption, four experimental parameters (adsorbent dosage, metal concentration, pH, and contact time) were evaluated. As a result, the highest removal of 92.12% was obtained when the heavy metal concentration was 10 mg/L, the adsorbent dosage was 0.15 g, the solution pH was 7, and the contact time was 180 min. Adsorption isotherm studies were also carried out. The appropriate adsorption isotherm for copper removal using GKS was determined as Langmuir isotherm. According to the optimization results, the quadratic model with an R2 of 0.9946 was found to be the most suitable model. Batch adsorption (dpeaa)DE-He213 Bio-composite adsorbent (dpeaa)DE-He213 Experimental design (dpeaa)DE-He213 Heavy metal removal (dpeaa)DE-He213 Nigiz, Filiz Ugur (orcid)0000-0003-0509-8425 aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 47(2021), 5 vom: 25. Juni, Seite 6001-6012 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:47 year:2021 number:5 day:25 month:06 pages:6001-6012 https://dx.doi.org/10.1007/s13369-021-05905-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 47 2021 5 25 06 6001-6012 |
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Characterization of adsorbent was carried out using a scanning electron microscope, Fourier transforms infrared spectroscopy, and thermo-gravimetric analysis methods. In addition to the parametric adsorption studies, experimental optimization was also applied with parameters determined by the central composite design of response surface methodology (RSM). Effects of copper concentration (from 10 to 50 ppm), pH (from 3 to 7), and adsorption dosage (from 0.05 to 0.15 g/L) were investigated to determine the optimum design points. In order to determine the efficiency of heavy metal adsorption, four experimental parameters (adsorbent dosage, metal concentration, pH, and contact time) were evaluated. As a result, the highest removal of 92.12% was obtained when the heavy metal concentration was 10 mg/L, the adsorbent dosage was 0.15 g, the solution pH was 7, and the contact time was 180 min. Adsorption isotherm studies were also carried out. The appropriate adsorption isotherm for copper removal using GKS was determined as Langmuir isotherm. 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Ünügül, Tuba |
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Ünügül, Tuba misc Batch adsorption misc Bio-composite adsorbent misc Experimental design misc Heavy metal removal Optimization of Sodium Alginate-Graphene Nanoplate-Kaolin Bio-composite Adsorbents in Heavy Metal Adsorption by Response Surface Methodology (RSM) |
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Optimization of Sodium Alginate-Graphene Nanoplate-Kaolin Bio-composite Adsorbents in Heavy Metal Adsorption by Response Surface Methodology (RSM) Batch adsorption (dpeaa)DE-He213 Bio-composite adsorbent (dpeaa)DE-He213 Experimental design (dpeaa)DE-He213 Heavy metal removal (dpeaa)DE-He213 |
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misc Batch adsorption misc Bio-composite adsorbent misc Experimental design misc Heavy metal removal |
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Optimization of Sodium Alginate-Graphene Nanoplate-Kaolin Bio-composite Adsorbents in Heavy Metal Adsorption by Response Surface Methodology (RSM) |
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Optimization of Sodium Alginate-Graphene Nanoplate-Kaolin Bio-composite Adsorbents in Heavy Metal Adsorption by Response Surface Methodology (RSM) |
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Ünügül, Tuba |
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Ünügül, Tuba Nigiz, Filiz Ugur |
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optimization of sodium alginate-graphene nanoplate-kaolin bio-composite adsorbents in heavy metal adsorption by response surface methodology (rsm) |
| title_auth |
Optimization of Sodium Alginate-Graphene Nanoplate-Kaolin Bio-composite Adsorbents in Heavy Metal Adsorption by Response Surface Methodology (RSM) |
| abstract |
Abstract In this study, graphene nanoplate-kaolin-sodium alginate (GKS) bio-composite adsorbent was prepared, and the copper removal from wastewater by the adsorption method was investigated. Characterization of adsorbent was carried out using a scanning electron microscope, Fourier transforms infrared spectroscopy, and thermo-gravimetric analysis methods. In addition to the parametric adsorption studies, experimental optimization was also applied with parameters determined by the central composite design of response surface methodology (RSM). Effects of copper concentration (from 10 to 50 ppm), pH (from 3 to 7), and adsorption dosage (from 0.05 to 0.15 g/L) were investigated to determine the optimum design points. In order to determine the efficiency of heavy metal adsorption, four experimental parameters (adsorbent dosage, metal concentration, pH, and contact time) were evaluated. As a result, the highest removal of 92.12% was obtained when the heavy metal concentration was 10 mg/L, the adsorbent dosage was 0.15 g, the solution pH was 7, and the contact time was 180 min. Adsorption isotherm studies were also carried out. The appropriate adsorption isotherm for copper removal using GKS was determined as Langmuir isotherm. According to the optimization results, the quadratic model with an R2 of 0.9946 was found to be the most suitable model. © King Fahd University of Petroleum & Minerals 2021 |
| abstractGer |
Abstract In this study, graphene nanoplate-kaolin-sodium alginate (GKS) bio-composite adsorbent was prepared, and the copper removal from wastewater by the adsorption method was investigated. Characterization of adsorbent was carried out using a scanning electron microscope, Fourier transforms infrared spectroscopy, and thermo-gravimetric analysis methods. In addition to the parametric adsorption studies, experimental optimization was also applied with parameters determined by the central composite design of response surface methodology (RSM). Effects of copper concentration (from 10 to 50 ppm), pH (from 3 to 7), and adsorption dosage (from 0.05 to 0.15 g/L) were investigated to determine the optimum design points. In order to determine the efficiency of heavy metal adsorption, four experimental parameters (adsorbent dosage, metal concentration, pH, and contact time) were evaluated. As a result, the highest removal of 92.12% was obtained when the heavy metal concentration was 10 mg/L, the adsorbent dosage was 0.15 g, the solution pH was 7, and the contact time was 180 min. Adsorption isotherm studies were also carried out. The appropriate adsorption isotherm for copper removal using GKS was determined as Langmuir isotherm. According to the optimization results, the quadratic model with an R2 of 0.9946 was found to be the most suitable model. © King Fahd University of Petroleum & Minerals 2021 |
| abstract_unstemmed |
Abstract In this study, graphene nanoplate-kaolin-sodium alginate (GKS) bio-composite adsorbent was prepared, and the copper removal from wastewater by the adsorption method was investigated. Characterization of adsorbent was carried out using a scanning electron microscope, Fourier transforms infrared spectroscopy, and thermo-gravimetric analysis methods. In addition to the parametric adsorption studies, experimental optimization was also applied with parameters determined by the central composite design of response surface methodology (RSM). Effects of copper concentration (from 10 to 50 ppm), pH (from 3 to 7), and adsorption dosage (from 0.05 to 0.15 g/L) were investigated to determine the optimum design points. In order to determine the efficiency of heavy metal adsorption, four experimental parameters (adsorbent dosage, metal concentration, pH, and contact time) were evaluated. As a result, the highest removal of 92.12% was obtained when the heavy metal concentration was 10 mg/L, the adsorbent dosage was 0.15 g, the solution pH was 7, and the contact time was 180 min. Adsorption isotherm studies were also carried out. The appropriate adsorption isotherm for copper removal using GKS was determined as Langmuir isotherm. According to the optimization results, the quadratic model with an R2 of 0.9946 was found to be the most suitable model. © King Fahd University of Petroleum & Minerals 2021 |
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Optimization of Sodium Alginate-Graphene Nanoplate-Kaolin Bio-composite Adsorbents in Heavy Metal Adsorption by Response Surface Methodology (RSM) |
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https://dx.doi.org/10.1007/s13369-021-05905-z |
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Nigiz, Filiz Ugur |
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10.1007/s13369-021-05905-z |
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2024-07-04T01:26:08.014Z |
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