{"id":"W2784943348","doi":"10.1021/acsnano.7b08721","title":"Carbon Nanosheets Containing Discrete Co-N<sub><i>x</i></sub>-B<sub><i>y</i></sub>-C Active Sites for Efficient Oxygen Electrocatalysis and Rechargeable Zn–Air Batteries","year":2018,"lang":"en","type":"article","venue":"ACS Nano","topic":"Electrocatalysts for Energy Conversion","field":"Energy","cited_by":483,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Light Source (Canada)","funders":"State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, Jilin University; National Natural Science Foundation of China","keywords":"Catalysis; Electrocatalyst; Electron transfer; Materials science; Carbon fibers; Oxygen evolution; Adsorption; Oxygen; Chemical engineering; Limiting current; Inorganic chemistry; Chemistry; Electrochemistry; Nanotechnology; Physical chemistry; Electrode; Organic chemistry; Composite number","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006420134,0.000812839,0.0009199387,0.0005022948,0.0008626836,0.0001349358,0.0005623489,0.0004529698,0.00001587947],"category_scores_gemma":[0.000368679,0.0008124288,0.0002897473,0.0008865672,0.0004196062,0.0004601221,0.0002822205,0.0003506372,0.00009324433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00057346,"about_ca_system_score_gemma":0.0002515753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004171566,"about_ca_topic_score_gemma":0.001677197,"domain_scores_codex":[0.995378,0.0001929013,0.00070496,0.00140646,0.0007203054,0.001597358],"domain_scores_gemma":[0.9971959,0.0004816905,0.0004640298,0.0009564862,0.0005589493,0.0003429486],"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":[0.001063559,0.00008027294,0.0002046697,0.00007641746,0.0004652594,0.00001266726,0.0009970909,0.0001083138,0.9877875,0.0006620616,0.001005402,0.007536767],"study_design_scores_gemma":[0.001767716,0.001196886,0.0003085574,0.0001016693,0.000370492,0.00004019839,0.0003523819,0.0009268714,0.9874089,0.000429252,0.006137849,0.0009592556],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948727,0.0004753583,0.0003198391,0.0004379294,0.0003340602,0.0006279979,0.00004214701,0.0003875798,0.002502331],"genre_scores_gemma":[0.9970919,0.000235005,0.0001671431,0.0006950833,0.0004578679,0.0002898459,0.0006845642,0.0002098152,0.0001687535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006577511,"threshold_uncertainty_score":0.9994327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008444256078743679,"score_gpt":0.2230863571685672,"score_spread":0.2146421010898235,"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."}}