{"id":"W4285115777","doi":"10.1039/d2ta01566h","title":"Tailoring trimetallic CoNiFe oxide nanostructured catalysts for the efficient electrochemical conversion of methane to methanol","year":2022,"lang":"en","type":"article","venue":"Journal of Materials Chemistry A","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Methane; Oxygenate; Methanol; Electrochemistry; Catalysis; Oxide; Materials science; Chemical engineering; Inorganic chemistry; Chemistry; Metallurgy; Electrode; Organic 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006087408,0.0001728044,0.0001358238,0.0001464249,0.0001196185,0.0003235364,0.0004196735,0.0003471958,0.001108796],"category_scores_gemma":[0.0002451838,0.0001306617,0.0000886152,0.0001400559,0.0001136335,0.000256693,0.0002029247,0.0003591404,0.0002965519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000260523,"about_ca_system_score_gemma":0.0001103386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004490061,"about_ca_topic_score_gemma":0.002386039,"domain_scores_codex":[0.9999347,0.000003627747,0.000004139971,0.00001320709,0.00002617321,0.00001810543],"domain_scores_gemma":[0.9999676,0.000007504677,0.000007004588,0.000003696485,0.000007634863,0.000006553454],"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":[0.00009648658,0.00004962778,0.0002911134,0.0001342209,0.00001094821,0.0001026523,0.0000306039,0.0005623219,0.9832106,0.001049253,0.0004801578,0.01398223],"study_design_scores_gemma":[0.00001635956,0.0001465167,0.0008648388,0.00001060595,0.000008760903,0.00009884227,0.00003752778,0.00670751,0.9875966,0.0001978829,0.004306385,0.000008228733],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9836984,0.001481233,0.005693208,0.0001270628,0.0001142871,0.00003335906,0.0001702829,0.0001740237,0.008508125],"genre_scores_gemma":[0.993795,0.0005318655,0.003453238,0.00006087565,0.00001030307,0.00001855091,0.0001309534,0.00002737663,0.001971799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001108796,"threshold_uncertainty_score":0.003709316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01152473938717891,"score_gpt":0.2482528886822566,"score_spread":0.2367281492950777,"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."}}