{"id":"W3216850832","doi":"10.1002/advs.202104237","title":"Synergistic Binary Fe–Co Nanocluster Supported on Defective Tungsten Oxide as Efficient Oxygen Reduction Electrocatalyst in Zinc‐Air Battery","year":2021,"lang":"en","type":"article","venue":"Advanced Science","topic":"Electrocatalysts for Energy Conversion","field":"Energy","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Electrocatalyst; Nanoclusters; Catalysis; Materials science; Oxide; Chemical engineering; Tungsten; Metal; Inorganic chemistry; Nanotechnology; Chemistry; Electrode; Physical chemistry; Metallurgy; Electrochemistry; Organic 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005392386,0.0002212925,0.0002802003,0.0001550827,0.00009086606,0.0002250141,0.0002992811,0.000247335,0.0002572582],"category_scores_gemma":[0.0000739432,0.0001403391,0.0001977798,0.0001434099,0.0001146687,0.0001851325,0.0002191625,0.0001794794,0.0001227913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001505479,"about_ca_system_score_gemma":0.0001202431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000656742,"about_ca_topic_score_gemma":0.001618722,"domain_scores_codex":[0.9999316,0.000004051784,0.000006482865,0.0000192344,0.00002386984,0.00001482715],"domain_scores_gemma":[0.9999794,0.000001939932,0.000005285482,0.000003025666,0.000005049169,0.000005361226],"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":[0.00003528698,0.00001359734,0.0001055153,0.00005204559,0.000006092106,0.00005393965,0.000007785876,0.0001950419,0.9977096,0.0001049635,0.00005480904,0.001661296],"study_design_scores_gemma":[0.0000107311,0.00006650465,0.000630947,0.000003086302,0.00001586051,0.00006954669,0.000008876788,0.004186328,0.9939633,0.00002714363,0.00101201,0.000005609621],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937463,0.0009722329,0.003723606,0.00006165639,0.00003200752,0.00002016708,0.0000871417,0.0001114146,0.001245585],"genre_scores_gemma":[0.9964013,0.0003404588,0.002377566,0.00001786715,0.000003276243,0.00001296687,0.00006238435,0.00001015637,0.0007739316],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000656742,"threshold_uncertainty_score":0.001305819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006453322145329733,"score_gpt":0.2467439490195421,"score_spread":0.2402906268742123,"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."}}