{"id":"W3040555765","doi":"10.3390/nano10071295","title":"Synthesis and Electrochemical Study of Three-Dimensional Graphene-Based Nanomaterials for Energy Applications","year":2020,"lang":"en","type":"review","venue":"Nanomaterials","topic":"Supercapacitor Materials and Fabrication","field":"Materials Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Graphene; Supercapacitor; Materials science; Nanomaterials; Raman spectroscopy; Nanotechnology; X-ray photoelectron spectroscopy; Electrochemical energy conversion; Energy transformation; Characterization (materials science); Scanning electron microscope; Fourier transform infrared spectroscopy; Energy storage; Electrode; Electrochemistry; Chemical engineering; Composite material; Chemistry; Optics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007830344,0.0007539882,0.00343622,0.000244659,0.0002174675,0.0002244361,0.0006447978,0.0004913702,0.0003879135],"category_scores_gemma":[0.0002110385,0.0006196991,0.0003285534,0.0002909383,0.0001740989,0.0001307348,0.0001742502,0.00004778313,0.00002865063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008662947,"about_ca_system_score_gemma":0.0003486757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006083704,"about_ca_topic_score_gemma":0.00000734175,"domain_scores_codex":[0.9955037,0.000516186,0.001929607,0.001154341,0.0004090855,0.0004871254],"domain_scores_gemma":[0.9967204,0.001002274,0.00116131,0.0007282813,0.0002043511,0.0001833599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001610191,0.000450242,0.000001871323,0.006941194,0.000126302,0.000002565306,0.00002902186,2.7068e-7,0.9601292,0.000537364,0.00009225267,0.0315287],"study_design_scores_gemma":[0.0007238439,0.0005445102,0.000003117144,0.001303397,0.0017567,0.00003433722,0.00001430819,0.000007177218,0.7155559,0.0003508192,0.2788206,0.000885266],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.09037293,0.8983057,0.001162485,0.00002016805,0.00141313,0.005975569,0.002426156,0.0003085287,0.00001535901],"genre_scores_gemma":[0.1139449,0.8353302,0.00862624,0.0001006849,0.003125062,0.03709812,0.001186349,0.0005699434,0.00001845375],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.2787284,"threshold_uncertainty_score":0.9996254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03576517735139081,"score_gpt":0.2824380849445138,"score_spread":0.246672907593123,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}