{"id":"W3137125024","doi":"10.1016/j.powera.2021.100052","title":"Mesoporous iron-nitrogen co-doped carbon material as cathode catalyst for the anion exchange membrane fuel cell","year":2021,"lang":"en","type":"article","venue":"Journal of Power Sources Advances","topic":"Electrocatalysts for Energy Conversion","field":"Energy","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Eesti Teadusagentuur; Agence Nationale de la Recherche; European Regional Development Fund; U.S. Department of Energy","keywords":"Catalysis; Mesoporous material; Electrocatalyst; Materials science; Inorganic chemistry; Cathode; Carbon fibers; Dopant; Ion exchange; Membrane; Transition metal; Chemical engineering; Nitrogen; Doping; Chemistry; Electrode; Ion; Composite material; Electrochemistry; Organic chemistry; Composite number; Physical chemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.0006481993,0.000315657,0.0005119799,0.000138007,0.0002247235,0.000106358,0.0005608031,0.0001358904,0.0002807131],"category_scores_gemma":[0.0001354395,0.0002248325,0.000310366,0.0002488584,0.00009812514,0.0004124966,0.00008924428,0.0002545667,0.00001318249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001841051,"about_ca_system_score_gemma":0.0002024386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004276045,"about_ca_topic_score_gemma":0.0005884314,"domain_scores_codex":[0.9976853,0.000166518,0.0006470259,0.0003131962,0.000702446,0.0004855552],"domain_scores_gemma":[0.9978884,0.0002902662,0.0008060843,0.0004088562,0.0004505768,0.0001558269],"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":[0.004401417,0.00082675,0.001525817,0.001273148,0.001123082,0.001140727,0.01241762,0.01312725,0.9495572,0.001103419,0.002370289,0.0111332],"study_design_scores_gemma":[0.00123833,0.0004168669,0.00001894808,0.00003821849,0.0002393284,0.000435782,0.001991235,0.000060293,0.6795605,0.0005994649,0.3151475,0.0002535857],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9446104,0.04478141,0.0001497841,0.0006405875,0.001984297,0.0001853023,0.0000171157,0.00004567564,0.007585469],"genre_scores_gemma":[0.9929499,0.003743456,0.0005877953,0.0002405267,0.0009903011,0.00001516545,0.00005632366,0.00007488869,0.001341611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3127772,"threshold_uncertainty_score":0.9168401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007340218791889329,"score_gpt":0.2389510396971121,"score_spread":0.2316108209052228,"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."}}