{"id":"W3204152415","doi":"10.1038/s41467-021-26053-w","title":"Efficient CO2 electroreduction on facet-selective copper films with high conversion rate","year":2021,"lang":"en","type":"article","venue":"Nature Communications","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":278,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Project 211; Natural Science Foundation of Tianjin City; National Natural Science Foundation of China","keywords":"Faraday efficiency; Materials science; Electrode; Facet (psychology); Copper; Yield (engineering); Deposition (geology); Nanotechnology; Realization (probability); Chemical engineering; Optoelectronics; Chemistry; Electrochemistry; Composite material; Metallurgy","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":[],"consensus_categories":[],"category_scores_codex":[0.0001323004,0.0001666646,0.0001715527,0.0001103974,0.0004307404,0.00004312255,0.0003976583,0.0002583896,0.0001756763],"category_scores_gemma":[0.00007514015,0.0001468261,0.00007534293,0.0007719898,0.0001207397,0.00004853185,0.0001271542,0.00102419,0.00007503009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001960176,"about_ca_system_score_gemma":0.000138759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001573419,"about_ca_topic_score_gemma":0.000306644,"domain_scores_codex":[0.9988877,0.0002190988,0.0001704088,0.0003097944,0.0002061772,0.0002068405],"domain_scores_gemma":[0.9977273,0.0000946716,0.0001069418,0.001516761,0.0004861853,0.00006819274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004686333,0.002104752,0.0002002127,0.00003667004,0.0004987569,0.0000175976,0.001051814,0.01639097,0.2711022,0.5400335,0.159263,0.008831888],"study_design_scores_gemma":[0.0009473737,0.0003266851,0.003638725,0.00008214215,0.0001156598,0.0001076666,0.0007827092,0.001761639,0.7566688,0.0005915265,0.234499,0.0004780156],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8987857,0.005000975,0.0005210452,0.0228829,0.0008994683,0.0008787756,0.00007445952,0.001157101,0.06979962],"genre_scores_gemma":[0.9942887,0.0003921895,0.002374313,0.0005215096,0.00004532227,0.0000848339,0.0005325422,0.00002863268,0.001731924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.539442,"threshold_uncertainty_score":0.5987393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009189819730792784,"score_gpt":0.2595252527507022,"score_spread":0.2503354330199093,"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."}}