{"id":"W2950206243","doi":"10.1038/s41929-019-0301-z","title":"Electrochemically converting carbon monoxide to liquid fuels by directing selectivity with electrode surface area","year":2019,"lang":"en","type":"article","venue":"Nature Catalysis","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":269,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Division of Electrical, Communications and Cyber Systems; Knut och Alice Wallenbergs Stiftelse; U.S. Department of Energy; National Science Foundation","keywords":"Selectivity; Electrode; Electrocatalyst; Materials science; Oxygenate; Surface roughness; Chemical engineering; Reversible hydrogen electrode; Catalysis; Carbon monoxide; Nanotechnology; Inorganic chemistry; Chemistry; Electrochemistry; Composite material; Working electrode; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003145005,0.0003945264,0.0005548576,0.0001537065,0.0001101406,0.00005378567,0.0003630763,0.0004485366,0.00007249007],"category_scores_gemma":[0.0001558127,0.0003570077,0.0001731481,0.001592212,0.00002530657,0.0001613365,0.00009093786,0.00106638,0.00002741759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004405173,"about_ca_system_score_gemma":0.00009783782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001429772,"about_ca_topic_score_gemma":0.0004709834,"domain_scores_codex":[0.9975045,0.00006547441,0.0003340565,0.0009104768,0.0004822699,0.0007031872],"domain_scores_gemma":[0.9984925,0.0001059595,0.0002005678,0.0007020269,0.000293156,0.0002057554],"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":[0.0003407336,0.00006079799,0.0009741505,0.00002342951,0.0002013977,0.000003213631,0.0001394656,0.000194963,0.9957452,0.00006285131,0.0007176753,0.001536051],"study_design_scores_gemma":[0.0002404891,0.0002846568,0.00005213117,0.00003423659,0.0001200694,0.00003210496,0.00005581512,0.000186641,0.9921415,0.00005154242,0.006312887,0.0004879314],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907138,0.0009026426,0.0003031133,0.0004718367,0.0000807023,0.0003304556,0.000006230064,0.0005840039,0.006607187],"genre_scores_gemma":[0.9966532,0.00002795765,0.0004732985,0.0003788328,0.00009649618,0.00004322032,0.0001634738,0.00008334916,0.002080177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005939366,"threshold_uncertainty_score":0.9998882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002425681506623762,"score_gpt":0.2068017833038641,"score_spread":0.2043761017972404,"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."}}