{"id":"W4289817125","doi":"10.1038/s41929-022-00788-1","title":"High carbon utilization in CO2 reduction to multi-carbon products in acidic media","year":2022,"lang":"en","type":"article","venue":"Nature Catalysis","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":610,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Research Grants Council, University Grants Committee; Canada Foundation for Innovation; Ontario Research Foundation","keywords":"Chemistry; Carbonate; Bicarbonate; Carbon fibers; Catalysis; Electrolyte; Electrocatalyst; Inorganic chemistry; Total inorganic carbon; Electrolysis; Faraday efficiency; Adsorption; Carbonization; Chemical engineering; Carbon dioxide; Materials science; Electrochemistry; Organic chemistry; Electrode; Composite number","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":[0.0004059693,0.0005070219,0.0003755915,0.0006434467,0.0004324486,0.00106871,0.0006369387,0.0005419618,0.001614359],"category_scores_gemma":[0.0004445818,0.0002236229,0.0002108708,0.0004747692,0.0003968766,0.001088193,0.000490576,0.000684933,0.0007124147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004272246,"about_ca_system_score_gemma":0.0002740246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00145992,"about_ca_topic_score_gemma":0.002287691,"domain_scores_codex":[0.99963,0.00005771685,0.00002098534,0.00007923484,0.0001297761,0.0000823279],"domain_scores_gemma":[0.9998013,0.00007324083,0.00001669607,0.00001902684,0.00005218133,0.00003755868],"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":[0.0004011452,0.00007197248,0.0002419865,0.000107471,0.000008790432,0.0001159826,0.00004275961,0.0001944438,0.9941815,0.000473726,0.0001343468,0.004025952],"study_design_scores_gemma":[0.000005303644,0.00009879654,0.0003822506,0.000002406292,0.000004766163,0.00005093065,0.00001869925,0.0007598777,0.9977865,0.00003798324,0.0008477904,0.000004615763],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845737,0.002869213,0.003917788,0.0001689306,0.00007314161,0.00002658819,0.0001732191,0.0001426936,0.008054806],"genre_scores_gemma":[0.9966121,0.0005401678,0.0009900113,0.00001760282,0.00001761707,0.000007111967,0.0001206279,0.00003216715,0.001662713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001614359,"threshold_uncertainty_score":0.005400598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0159948773955314,"score_gpt":0.257662144485905,"score_spread":0.2416672670903736,"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."}}