{"id":"W3036669261","doi":"10.1002/aenm.202001275","title":"Graphene Quantum Dots‐Based Advanced Electrode Materials: Design, Synthesis and Their Applications in Electrochemical Energy Storage and Electrocatalysis","year":2020,"lang":"en","type":"article","venue":"Advanced Energy Materials","topic":"Carbon and Quantum Dots Applications","field":"Materials Science","cited_by":203,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Electrocatalyst; Nanomaterials; Materials science; Nanotechnology; Graphene; Supercapacitor; Quantum dot; Electrochemical energy conversion; Energy storage; Heteroatom; Electrochemical energy storage; Electrochemistry; Electrode; Chemistry","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":[0.0003359988,0.0005453274,0.0008933819,0.0001974855,0.0002199979,0.0001850981,0.0003889489,0.0001838726,0.0001907876],"category_scores_gemma":[0.0001031292,0.0004895181,0.00006334698,0.0006325216,0.0002035271,0.000311935,0.0001283772,0.00009173174,0.000006550291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008550398,"about_ca_system_score_gemma":0.00009558621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001764472,"about_ca_topic_score_gemma":0.00005544942,"domain_scores_codex":[0.9968547,0.0003007171,0.0007565584,0.001116959,0.0002123975,0.0007586407],"domain_scores_gemma":[0.9983805,0.0004048838,0.0003108604,0.0005322564,0.00007864666,0.0002928346],"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":[0.0004160831,0.00007781701,0.000003038322,0.000044325,0.00001900811,0.000002040291,0.00007026769,0.0001851323,0.9764946,0.02135245,0.00001272979,0.001322498],"study_design_scores_gemma":[0.0006167999,0.0001366309,0.00004951326,0.00002701032,0.00004649224,0.000009077516,0.0000718151,0.0005832491,0.9904956,0.005298024,0.002126975,0.0005388581],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8690165,0.001270981,0.1282349,0.0005971617,0.00006294425,0.0003670019,0.0001326943,0.0002837545,0.00003410133],"genre_scores_gemma":[0.9925197,0.0009937956,0.003464154,0.0007297645,0.0001082156,0.001918904,0.0001657318,0.00008877014,0.00001098121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1247707,"threshold_uncertainty_score":0.9997556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01017706065916478,"score_gpt":0.2223081102047856,"score_spread":0.2121310495456208,"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."}}