{"id":"W4403735048","doi":"10.1021/acsanm.4c03547","title":"Hybrid 3D Vertical Graphene Nanoflake and Aligned Carbon Nanotube Architectures for High-Energy-Density Lithium-Ion Batteries","year":2024,"lang":"en","type":"article","venue":"ACS Applied Nano Materials","topic":"Advancements in Battery Materials","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Graphene; Materials science; Lithium (medication); Carbon nanotube; Ion; Nanotechnology; Nanotube; Energy density; Carbon fibers; Composite number; Engineering physics; Chemistry; Composite material; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004747063,0.0003314445,0.0001431931,0.0003185072,0.0001603238,0.0002939854,0.0002669119,0.0005640402,0.0005054257],"category_scores_gemma":[0.00006678482,0.0001709921,0.000312301,0.0001935255,0.0001182046,0.0003664227,0.000201814,0.0002392663,0.0002600907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002665188,"about_ca_system_score_gemma":0.0001239318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004764882,"about_ca_topic_score_gemma":0.002107609,"domain_scores_codex":[0.9999542,0.000003115903,0.000003315319,0.000007981164,0.00001995777,0.00001144769],"domain_scores_gemma":[0.9999763,0.000002669524,0.000005248306,0.000002987801,0.000006856842,0.000005949737],"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":[0.00002482119,0.00002524003,0.0001286268,0.00009980443,0.00001161353,0.0001576005,0.00002111161,0.001948627,0.9917606,0.0007105287,0.0001715103,0.004939915],"study_design_scores_gemma":[0.0000306603,0.0002287918,0.001444717,0.00001492694,0.00002848548,0.0003286198,0.00003509325,0.02583223,0.9614503,0.0005929076,0.009960191,0.00005293897],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9661039,0.003583981,0.02369284,0.0002593223,0.0002161607,0.00006113698,0.0004709525,0.0004702028,0.005141631],"genre_scores_gemma":[0.9781117,0.00101195,0.01906049,0.00004750851,0.00001433712,0.000046171,0.0001941347,0.00002919922,0.001484474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005640402,"threshold_uncertainty_score":0.001933753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00670137125688512,"score_gpt":0.2083864500580065,"score_spread":0.2016850788011214,"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."}}