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Destination: mk:title Children: Item: Title: 1 Introduction Destination: mk:H1_1 Item: Title: 2 Materials and methods Destination: mk:H1_2 Children: Item: Title: 2.1 Preparation of GO/Co electrodes Destination: mk:H2_1 Item: Title: 2.2 Characterization Destination: mk:H2_2 Children: Item: Title: 2.2.1 RF-assisted XPS data collection Destination: mk:H3_3 Item: Title: 2.2.2 RF-assisted XPS model establishment Destination: mk:H3_4 Item: Title: 2.2.3 Other material characterization Destination: mk:H3_5 Item: Title: 2.3 Electrochemical measurement Destination: mk:H2_6 Item: Title: 2.4 RF capacitance model Destination: mk:H2_7 Children: Item: Title: 2.4.1 RF capacitance model data collection Destination: mk:H3_8 Item: Title: 2.4.2 RF capacitance model establishment Destination: mk:H3_9 Item: Title: 3 Results and discussion Destination: mk:H1_3 Children: Item: Title: 3.1 Stage1 ML-assisted XPS spectral analysis Destination: mk:H2_10 Children: Item: Title: 3.1.1 Model of RF-assisted XPS Destination: mk:H3_11 Item: Title: 3.1.2 Analysis of chemical compositions and structure Destination: mk:H3_12 Item: Title: 3.2 Stage 2 capacitance model establishment and validation Destination: mk:H2_13 Item: Title: 4 Conclusion Destination: mk:H1_4 Item: Title: CRediT authorship contribution statement Destination: mk:H1_5 Item: Title: Declaration of competing interest Destination: mk:HH1_3 Item: Title: Acknowledgements Destination: mk:HH1_4 Item: Title: Appendix A Supplementary data Destination: mk:H1_6 Item: Title: References Destination: mk:HH2_1 Info: Title: Machine learning assisted chemical characterization to investigate the temperature-dependent supercapacitance using Co-rGO electrodes Author: Xiaoyu Liu Subject: Carbon, 214 (2023) 118342. doi:10.1016/j.carbon.2023.118342 Keywords: Graphene oxide,Supercapacitance,Random forest,Machine learning Creator: Elsevier Producer: Acrobat Distiller 8.1.0 (Windows) CreationDate: Thu Aug 31 04:43:57 CEST 2023 ModDate: Thu Aug 31 07:49:37 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EKMFHJ+CharisSIL-Bold Flags: Nonsymbolic, ForceBold FontBBox: -720, -533, 1759, 1320 FontFile2: true XMP: application/pdf 10.1016/j.carbon.2023.118342 Elsevier Ltd Carbon, 214 (2023) 118342. doi:10.1016/j.carbon.2023.118342 Graphene oxide Supercapacitance Random forest Machine learning Machine learning assisted chemical characterization to investigate the temperature-dependent supercapacitance using Co-rGO electrodes Xiaoyu Liu Dali Ji Xiaoheng Jin Vanesa Quintano Rakesh Joshi journal Carbon © 2023 The Authors. Published by Elsevier Ltd. 0008-6223 214 1 October 2023 2023-10-01 118342 118342 10.1016/j.carbon.2023.118342 https://doi.org/10.1016/j.carbon.2023.118342 2010-04-23 true 10.1016/j.carbon.2023.118342 elsevier.com sciencedirect.com http://creativecommons.org/licenses/by-nc-nd/4.0/ VoR 10.1016/j.carbon.2023.118342 noindex 2010-04-23 true 31st August 2023 sciencedirect.com elsevier.com Elsevier 2023-08-31T05:49:37 2023-08-31T02:43:57 2023-08-31T05:49:37 True 31st August 2023 Acrobat Distiller 8.1.0 (Windows) Graphene oxide,Supercapacitance,Random forest,Machine learning uuid:1b499bed-4ce8-4c0c-b682-0d58baae1cbe uuid:b6ed5266-03cb-4855-90c0-636283357f42 Pages: Page: Label: 1 Annotations: Annotation: Subtype: Link Rect: 90, 576, 96, 587 Flags: Print Destination: 542 Annotation: Subtype: Link Rect: 97, 571, 140, 582 Flags: Print Destination: 542 Annotation: Subtype: Link Rect: 140, 576, 147, 587 Flags: Print Destination: 542 Annotation: Subtype: Link Rect: 147, 579, 151, 584 Flags: Print 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