{"created":"2025-03-03T06:33:48.542152+00:00","id":2012780,"links":{},"metadata":{"_buckets":{"deposit":"a51d3d04-2788-449e-a4b8-e176ac086a1f"},"_deposit":{"created_by":121,"id":"2012780","owner":"121","owners":[121],"pid":{"revision_id":0,"type":"depid","value":"2012780"},"status":"published"},"_oai":{"id":"oai:tokushima-u.repo.nii.ac.jp:02012780","sets":["1713853213384:1713853295607"]},"author_link":["275"],"control_number":"2012780","item_10001_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2024-12-05","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicPageEnd":"139","bibliographicPageStart":"119","bibliographicVolumeNumber":"4","bibliographic_titles":[{"bibliographic_title":"Energy Advances","bibliographic_titleLang":"en"}]}]},"item_10001_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"This work utilizes a novel approach leveraging the machine learning (ML) technique to predict the electrochemical supercapacitor performance of graphene oxide nano-rings (GONs) as electrode nanomaterials. Initially, the experimental procedure was carried out to synthesize GO via a modified Hummers method, followed by GONs preparation using the water-in-oil (W/O) emulsion technique. High-resolution transmission electron microscopy (HRTEM) analysis reveals the formation of a typical two-dimensional GO nanosheet and multilayer-GO nano-rings. The X-ray diffraction (XRD), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and Brunauer–Emmett–Teller (BET) analysis results show that the GONs possess similar structural and surface chemistry properties as of GO, with a slight reduction in oxygenous functionalities, enhancing the capacitive behaviours through facile electron migration at the electrode surface. The electrochemical assessment of GO and GONs samples indicates outstanding specific capacitances of 164 F g−1 and 294 F g−1 at 1 mV s−1, showcasing capacitive retention of up to 63% and 60% after 2500 cycles. In addition, four different machine learning models were tested to estimate the role of electrochemical parameters in determining the specific capacitance of GONs.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_10001_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"The Royal Society of Chemistry","subitem_publisher_language":"en"}]},"item_10001_rights_15":{"attribute_name":"権利情報","attribute_value_mlt":[{"subitem_rights":"This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.","subitem_rights_language":"en"}]},"item_10001_source_id_9":{"attribute_name":"収録物ID","attribute_value_mlt":[{"subitem_source_identifier":"27531457","subitem_source_identifier_type":"EISSN"}]},"item_10001_version_type_20":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_1715043197608":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_abf2"}]},"item_1722929371688":{"attribute_name":"出版社版DOI","attribute_value_mlt":[{"subitem_relation_name":[{"subitem_relation_name_language":"ja","subitem_relation_name_text":"10.1039/d4ya00577e"}],"subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.1039/d4ya00577e","subitem_relation_type_select":"DOI"}}]},"item_1723180141928":{"attribute_name":"EID","attribute_value_mlt":[{"subitem_identifier_type":"URI","subitem_identifier_uri":"415394"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yogesh, Gaurav Kumar","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Nandi, Debabrata","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Yeetsorn, Rungsima","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Wanchan, Waritnan","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Devi, Chandni","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Singh, Ravi Pratap","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Vasistha, Aditya","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Kumar, Mukesh","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"コインカー, パンカジ","creatorNameLang":"ja"},{"creatorName":"コインカー, パンカジ","creatorNameLang":"ja-Kana"},{"creatorName":"Koinkar, Pankaj","creatorNameLang":"en"}],"familyNames":[{"familyName":"コインカー","familyNameLang":"ja"},{"familyName":"コインカー","familyNameLang":"ja-Kana"},{"familyName":"Koinkar","familyNameLang":"en"}],"givenNames":[{"givenName":"パンカジ","givenNameLang":"ja"},{"givenName":"パンカジ","givenNameLang":"ja-Kana"},{"givenName":"Pankaj","givenNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"275","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"164626/profile-ja.html","nameIdentifierScheme":"徳島大学 教育研究者総覧","nameIdentifierURI":"http://pub2.db.tokushima-u.ac.jp/ERD/person/164626/profile-ja.html"}]},{"creatorNames":[{"creatorName":"Yadav, Kamlesh","creatorNameLang":"en"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2025-03-26"}],"displaytype":"detail","filename":"energyad_4_1_119.pdf","filesize":[{"value":"4.6 MB"}],"format":"application/pdf","licensetype":"license_9","mimetype":"application/pdf","url":{"objectType":"fulltext","url":"https://tokushima-u.repo.nii.ac.jp/record/2012780/files/energyad_4_1_119.pdf"},"version_id":"28515b3a-959a-4a46-ad06-95637f4299ff"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"A machine learning approach for estimating supercapacitor performance of graphene oxide nano-ring based electrode materials","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"A machine learning approach for estimating supercapacitor performance of graphene oxide nano-ring based electrode materials","subitem_title_language":"en"}]},"item_type_id":"40001","owner":"121","path":["1713853295607"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2025-03-26"},"publish_date":"2025-03-26","publish_status":"0","recid":"2012780","relation_version_is_last":true,"title":["A machine learning approach for estimating supercapacitor performance of graphene oxide nano-ring based electrode materials"],"weko_creator_id":"121","weko_shared_id":-1},"updated":"2025-03-26T02:36:58.849028+00:00"}