A data-driven approach for evaluation of electrolyte informatics on electrochemical performance of carbon-based electrode materials

Abstract Electrolytes are one of the most vital constituents of any electrochemical energy storage device. In this study, we investigated the effect of electrolyte parameters like bare ion size (BIS), hydrated ion size (HIS), electrolyte concentration, and operating temperature on the electrochemica...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Dubey, Richa [verfasserIn] Guruviah, Velmathi

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Artificial neural network Carbon-based electrode material Electric double-layer capacitor Electrolyte Machine learning Supercapacitor

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022

Übergeordnetes Werk:	Enthalten in: Ionics - Berlin : Springer, 1995, 28(2022), 5 vom: 16. Feb., Seite 2169-2183
Übergeordnetes Werk:	volume:28 ; year:2022 ; number:5 ; day:16 ; month:02 ; pages:2169-2183

Links:	Volltext

DOI / URN:	10.1007/s11581-022-04480-z

Katalog-ID:	SPR046756302

Internformat


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allfields	10.1007/s11581-022-04480-z doi (DE-627)SPR046756302 (SPR)s11581-022-04480-z-e DE-627 ger DE-627 rakwb eng Dubey, Richa verfasserin aut A data-driven approach for evaluation of electrolyte informatics on electrochemical performance of carbon-based electrode materials 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Electrolytes are one of the most vital constituents of any electrochemical energy storage device. In this study, we investigated the effect of electrolyte parameters like bare ion size (BIS), hydrated ion size (HIS), electrolyte concentration, and operating temperature on the electrochemical performance of the supercapacitor using a large dataset of 900 data. The impact of different types of electrolytes on the performance of energy storage devices was also investigated. Further, the significance of pore size on electrochemical performance was analyzed. Artificial neural network and Random Forest model provided better accuracy with higher correlation coefficient values (0.851 and 0.852, respectively) and lower root mean square error (RMSE) (0.98 and 1.24, respectively) in comparison to other models. The novelty of this work is the inclusion of electrolyte parameters along with structural and chemical composition and large input dataset for prediction, which enhanced the performance accuracy of designed models compared with other reported works in the literature. Artificial neural network (dpeaa)DE-He213 Carbon-based electrode material (dpeaa)DE-He213 Electric double-layer capacitor (dpeaa)DE-He213 Electrolyte (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Supercapacitor (dpeaa)DE-He213 Guruviah, Velmathi aut Enthalten in Ionics Berlin : Springer, 1995 28(2022), 5 vom: 16. Feb., Seite 2169-2183 (DE-627)509398944 (DE-600)2226746-3 1862-0760 nnns volume:28 year:2022 number:5 day:16 month:02 pages:2169-2183 https://dx.doi.org/10.1007/s11581-022-04480-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_101 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_2190 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 28 2022 5 16 02 2169-2183
spelling	10.1007/s11581-022-04480-z doi (DE-627)SPR046756302 (SPR)s11581-022-04480-z-e DE-627 ger DE-627 rakwb eng Dubey, Richa verfasserin aut A data-driven approach for evaluation of electrolyte informatics on electrochemical performance of carbon-based electrode materials 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Electrolytes are one of the most vital constituents of any electrochemical energy storage device. In this study, we investigated the effect of electrolyte parameters like bare ion size (BIS), hydrated ion size (HIS), electrolyte concentration, and operating temperature on the electrochemical performance of the supercapacitor using a large dataset of 900 data. The impact of different types of electrolytes on the performance of energy storage devices was also investigated. Further, the significance of pore size on electrochemical performance was analyzed. Artificial neural network and Random Forest model provided better accuracy with higher correlation coefficient values (0.851 and 0.852, respectively) and lower root mean square error (RMSE) (0.98 and 1.24, respectively) in comparison to other models. The novelty of this work is the inclusion of electrolyte parameters along with structural and chemical composition and large input dataset for prediction, which enhanced the performance accuracy of designed models compared with other reported works in the literature. Artificial neural network (dpeaa)DE-He213 Carbon-based electrode material (dpeaa)DE-He213 Electric double-layer capacitor (dpeaa)DE-He213 Electrolyte (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Supercapacitor (dpeaa)DE-He213 Guruviah, Velmathi aut Enthalten in Ionics Berlin : Springer, 1995 28(2022), 5 vom: 16. Feb., Seite 2169-2183 (DE-627)509398944 (DE-600)2226746-3 1862-0760 nnns volume:28 year:2022 number:5 day:16 month:02 pages:2169-2183 https://dx.doi.org/10.1007/s11581-022-04480-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_101 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_2190 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 28 2022 5 16 02 2169-2183
allfields_unstemmed	10.1007/s11581-022-04480-z doi (DE-627)SPR046756302 (SPR)s11581-022-04480-z-e DE-627 ger DE-627 rakwb eng Dubey, Richa verfasserin aut A data-driven approach for evaluation of electrolyte informatics on electrochemical performance of carbon-based electrode materials 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Electrolytes are one of the most vital constituents of any electrochemical energy storage device. In this study, we investigated the effect of electrolyte parameters like bare ion size (BIS), hydrated ion size (HIS), electrolyte concentration, and operating temperature on the electrochemical performance of the supercapacitor using a large dataset of 900 data. The impact of different types of electrolytes on the performance of energy storage devices was also investigated. Further, the significance of pore size on electrochemical performance was analyzed. Artificial neural network and Random Forest model provided better accuracy with higher correlation coefficient values (0.851 and 0.852, respectively) and lower root mean square error (RMSE) (0.98 and 1.24, respectively) in comparison to other models. The novelty of this work is the inclusion of electrolyte parameters along with structural and chemical composition and large input dataset for prediction, which enhanced the performance accuracy of designed models compared with other reported works in the literature. Artificial neural network (dpeaa)DE-He213 Carbon-based electrode material (dpeaa)DE-He213 Electric double-layer capacitor (dpeaa)DE-He213 Electrolyte (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Supercapacitor (dpeaa)DE-He213 Guruviah, Velmathi aut Enthalten in Ionics Berlin : Springer, 1995 28(2022), 5 vom: 16. Feb., Seite 2169-2183 (DE-627)509398944 (DE-600)2226746-3 1862-0760 nnns volume:28 year:2022 number:5 day:16 month:02 pages:2169-2183 https://dx.doi.org/10.1007/s11581-022-04480-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_101 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_2190 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 28 2022 5 16 02 2169-2183
allfieldsGer	10.1007/s11581-022-04480-z doi (DE-627)SPR046756302 (SPR)s11581-022-04480-z-e DE-627 ger DE-627 rakwb eng Dubey, Richa verfasserin aut A data-driven approach for evaluation of electrolyte informatics on electrochemical performance of carbon-based electrode materials 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Electrolytes are one of the most vital constituents of any electrochemical energy storage device. In this study, we investigated the effect of electrolyte parameters like bare ion size (BIS), hydrated ion size (HIS), electrolyte concentration, and operating temperature on the electrochemical performance of the supercapacitor using a large dataset of 900 data. The impact of different types of electrolytes on the performance of energy storage devices was also investigated. Further, the significance of pore size on electrochemical performance was analyzed. Artificial neural network and Random Forest model provided better accuracy with higher correlation coefficient values (0.851 and 0.852, respectively) and lower root mean square error (RMSE) (0.98 and 1.24, respectively) in comparison to other models. The novelty of this work is the inclusion of electrolyte parameters along with structural and chemical composition and large input dataset for prediction, which enhanced the performance accuracy of designed models compared with other reported works in the literature. Artificial neural network (dpeaa)DE-He213 Carbon-based electrode material (dpeaa)DE-He213 Electric double-layer capacitor (dpeaa)DE-He213 Electrolyte (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Supercapacitor (dpeaa)DE-He213 Guruviah, Velmathi aut Enthalten in Ionics Berlin : Springer, 1995 28(2022), 5 vom: 16. Feb., Seite 2169-2183 (DE-627)509398944 (DE-600)2226746-3 1862-0760 nnns volume:28 year:2022 number:5 day:16 month:02 pages:2169-2183 https://dx.doi.org/10.1007/s11581-022-04480-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_101 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_2190 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 28 2022 5 16 02 2169-2183
allfieldsSound	10.1007/s11581-022-04480-z doi (DE-627)SPR046756302 (SPR)s11581-022-04480-z-e DE-627 ger DE-627 rakwb eng Dubey, Richa verfasserin aut A data-driven approach for evaluation of electrolyte informatics on electrochemical performance of carbon-based electrode materials 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Electrolytes are one of the most vital constituents of any electrochemical energy storage device. In this study, we investigated the effect of electrolyte parameters like bare ion size (BIS), hydrated ion size (HIS), electrolyte concentration, and operating temperature on the electrochemical performance of the supercapacitor using a large dataset of 900 data. The impact of different types of electrolytes on the performance of energy storage devices was also investigated. Further, the significance of pore size on electrochemical performance was analyzed. Artificial neural network and Random Forest model provided better accuracy with higher correlation coefficient values (0.851 and 0.852, respectively) and lower root mean square error (RMSE) (0.98 and 1.24, respectively) in comparison to other models. The novelty of this work is the inclusion of electrolyte parameters along with structural and chemical composition and large input dataset for prediction, which enhanced the performance accuracy of designed models compared with other reported works in the literature. Artificial neural network (dpeaa)DE-He213 Carbon-based electrode material (dpeaa)DE-He213 Electric double-layer capacitor (dpeaa)DE-He213 Electrolyte (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Supercapacitor (dpeaa)DE-He213 Guruviah, Velmathi aut Enthalten in Ionics Berlin : Springer, 1995 28(2022), 5 vom: 16. Feb., Seite 2169-2183 (DE-627)509398944 (DE-600)2226746-3 1862-0760 nnns volume:28 year:2022 number:5 day:16 month:02 pages:2169-2183 https://dx.doi.org/10.1007/s11581-022-04480-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_101 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_2190 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 28 2022 5 16 02 2169-2183
language	English
source	Enthalten in Ionics 28(2022), 5 vom: 16. Feb., Seite 2169-2183 volume:28 year:2022 number:5 day:16 month:02 pages:2169-2183
sourceStr	Enthalten in Ionics 28(2022), 5 vom: 16. Feb., Seite 2169-2183 volume:28 year:2022 number:5 day:16 month:02 pages:2169-2183
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Artificial neural network Carbon-based electrode material Electric double-layer capacitor Electrolyte Machine learning Supercapacitor
isfreeaccess_bool	false
container_title	Ionics
authorswithroles_txt_mv	Dubey, Richa @@aut@@ Guruviah, Velmathi @@aut@@
publishDateDaySort_date	2022-02-16T00:00:00Z
hierarchy_top_id	509398944
id	SPR046756302
language_de	englisch
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author	Dubey, Richa
spellingShingle	Dubey, Richa misc Artificial neural network misc Carbon-based electrode material misc Electric double-layer capacitor misc Electrolyte misc Machine learning misc Supercapacitor A data-driven approach for evaluation of electrolyte informatics on electrochemical performance of carbon-based electrode materials
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topic_title	A data-driven approach for evaluation of electrolyte informatics on electrochemical performance of carbon-based electrode materials Artificial neural network (dpeaa)DE-He213 Carbon-based electrode material (dpeaa)DE-He213 Electric double-layer capacitor (dpeaa)DE-He213 Electrolyte (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Supercapacitor (dpeaa)DE-He213
topic	misc Artificial neural network misc Carbon-based electrode material misc Electric double-layer capacitor misc Electrolyte misc Machine learning misc Supercapacitor
topic_unstemmed	misc Artificial neural network misc Carbon-based electrode material misc Electric double-layer capacitor misc Electrolyte misc Machine learning misc Supercapacitor
topic_browse	misc Artificial neural network misc Carbon-based electrode material misc Electric double-layer capacitor misc Electrolyte misc Machine learning misc Supercapacitor
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title	A data-driven approach for evaluation of electrolyte informatics on electrochemical performance of carbon-based electrode materials
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title_full	A data-driven approach for evaluation of electrolyte informatics on electrochemical performance of carbon-based electrode materials
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author_browse	Dubey, Richa Guruviah, Velmathi
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doi_str_mv	10.1007/s11581-022-04480-z
title_sort	data-driven approach for evaluation of electrolyte informatics on electrochemical performance of carbon-based electrode materials
title_auth	A data-driven approach for evaluation of electrolyte informatics on electrochemical performance of carbon-based electrode materials
abstract	Abstract Electrolytes are one of the most vital constituents of any electrochemical energy storage device. In this study, we investigated the effect of electrolyte parameters like bare ion size (BIS), hydrated ion size (HIS), electrolyte concentration, and operating temperature on the electrochemical performance of the supercapacitor using a large dataset of 900 data. The impact of different types of electrolytes on the performance of energy storage devices was also investigated. Further, the significance of pore size on electrochemical performance was analyzed. Artificial neural network and Random Forest model provided better accuracy with higher correlation coefficient values (0.851 and 0.852, respectively) and lower root mean square error (RMSE) (0.98 and 1.24, respectively) in comparison to other models. The novelty of this work is the inclusion of electrolyte parameters along with structural and chemical composition and large input dataset for prediction, which enhanced the performance accuracy of designed models compared with other reported works in the literature. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
abstractGer	Abstract Electrolytes are one of the most vital constituents of any electrochemical energy storage device. In this study, we investigated the effect of electrolyte parameters like bare ion size (BIS), hydrated ion size (HIS), electrolyte concentration, and operating temperature on the electrochemical performance of the supercapacitor using a large dataset of 900 data. The impact of different types of electrolytes on the performance of energy storage devices was also investigated. Further, the significance of pore size on electrochemical performance was analyzed. Artificial neural network and Random Forest model provided better accuracy with higher correlation coefficient values (0.851 and 0.852, respectively) and lower root mean square error (RMSE) (0.98 and 1.24, respectively) in comparison to other models. The novelty of this work is the inclusion of electrolyte parameters along with structural and chemical composition and large input dataset for prediction, which enhanced the performance accuracy of designed models compared with other reported works in the literature. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
abstract_unstemmed	Abstract Electrolytes are one of the most vital constituents of any electrochemical energy storage device. In this study, we investigated the effect of electrolyte parameters like bare ion size (BIS), hydrated ion size (HIS), electrolyte concentration, and operating temperature on the electrochemical performance of the supercapacitor using a large dataset of 900 data. The impact of different types of electrolytes on the performance of energy storage devices was also investigated. Further, the significance of pore size on electrochemical performance was analyzed. Artificial neural network and Random Forest model provided better accuracy with higher correlation coefficient values (0.851 and 0.852, respectively) and lower root mean square error (RMSE) (0.98 and 1.24, respectively) in comparison to other models. The novelty of this work is the inclusion of electrolyte parameters along with structural and chemical composition and large input dataset for prediction, which enhanced the performance accuracy of designed models compared with other reported works in the literature. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
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container_issue	5
title_short	A data-driven approach for evaluation of electrolyte informatics on electrochemical performance of carbon-based electrode materials
url	https://dx.doi.org/10.1007/s11581-022-04480-z
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author2	Guruviah, Velmathi
author2Str	Guruviah, Velmathi
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doi_str	10.1007/s11581-022-04480-z
up_date	2024-07-04T00:15:00.635Z
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A data-driven approach for evaluation of electrolyte informatics on electrochemical performance of carbon-based electrode materials

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