{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000488011,0.0002209815,0.00033338,0.001125751,0.00007521057,0.00001998397,0.000291995,0.0002559668,0.0000849606],"category_scores_gemma":[0.0002934134,0.0002475761,0.00007184898,0.004330862,0.00003727462,0.00009916844,0.0001810187,0.000887915,0.000004119577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000802988,"about_ca_system_score_gemma":0.00008411155,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009033874,"about_ca_topic_score_gemma":0.004456612,"domain_scores_codex":[0.9978693,0.0001614347,0.0004314949,0.0007080944,0.0005128822,0.0003168168],"domain_scores_gemma":[0.9990513,0.00001973112,0.0001285996,0.0006092209,0.0001131329,0.00007796779],"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":[0.001523054,0.00433483,0.008546194,0.0003444236,0.0003604961,0.0003499281,0.03008856,0.1092852,0.6070561,0.009586875,0.005743843,0.2227805],"study_design_scores_gemma":[0.002473292,0.0002231868,0.01764787,0.00008368951,0.0002103775,0.0001093502,0.005143454,0.003828422,0.9291925,0.00108804,0.03856857,0.001431238],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946983,0.001194091,0.000009183369,0.001894202,0.001126831,0.0004326527,0.00001305248,0.0002034003,0.0004282197],"genre_scores_gemma":[0.9974743,0.00006589082,0.0003804556,0.0000937059,0.0002271871,0.0003896081,0.0008571641,0.00004372741,0.0004680144],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3221364,"threshold_uncertainty_score":0.9999977,"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."}}