{"id":"W2590755368","doi":"10.1016/j.carbon.2017.02.056","title":"Heavily nitrogen-doped acetylene black as a high-performance catalyst for oxygen reduction reaction","year":2017,"lang":"en","type":"article","venue":"Carbon","topic":"Electrocatalysts for Energy Conversion","field":"Energy","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Education Department of Sichuan Province; Sichuan University of Science and Engineering; China Scholarship Council; University of Waterloo","keywords":"Acetylene; Catalysis; Carbon black; Ammonia; Nitrogen; Inorganic chemistry; Chemistry; Graphene; Oxidizing agent; Oxygen; Doping; Materials science; Organic chemistry; Nanotechnology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001621305,0.0003486498,0.0002639317,0.000379126,0.000262915,0.0003589308,0.0003882885,0.0004437031,0.0005385192],"category_scores_gemma":[0.0001337101,0.0001664306,0.0001540083,0.0002537603,0.0001824555,0.0003151159,0.0001926828,0.0003241069,0.000257427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002991576,"about_ca_system_score_gemma":0.0001921654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001142087,"about_ca_topic_score_gemma":0.003553213,"domain_scores_codex":[0.9998595,0.00001391188,0.000007772618,0.00002676021,0.00006918066,0.00002295802],"domain_scores_gemma":[0.9999558,0.000007097766,0.000006358854,0.000005647979,0.00001468417,0.00001053025],"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":[0.00006754002,0.00002521758,0.0001144935,0.0000535011,0.000008319642,0.00007067643,0.00002036823,0.0001255792,0.9956859,0.0002902801,0.0002122973,0.003325849],"study_design_scores_gemma":[0.000006406904,0.00005893686,0.0004126586,0.000002896724,0.00001370453,0.00004469374,0.0000109125,0.001413806,0.9960409,0.00004764789,0.001942131,0.000005347571],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862694,0.002631528,0.004070575,0.0002070443,0.0001569661,0.00002857215,0.0001737625,0.0001157978,0.006346347],"genre_scores_gemma":[0.9947807,0.000781858,0.001530828,0.00001584769,0.00001219723,0.000007290428,0.00009987301,0.00002065701,0.00275071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001142087,"threshold_uncertainty_score":0.002270877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0123845471808858,"score_gpt":0.2374717877010907,"score_spread":0.2250872405202049,"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."}}