{"id":"W4392168938","doi":"10.1038/s41467-024-45538-y","title":"In situ copper faceting enables efficient CO2/CO electrolysis","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; Marsden Fund; Ontario Research Foundation; National Natural Science Foundation of China; Canada Research Chairs; Royal Society Te Apārangi; Shanghai Jiao Tong University; Paul Scherrer Institut","keywords":"Faraday efficiency; Copper; Faceting; Electrochemistry; Catalysis; Electrolysis; Carbon fibers; Materials science; Electrocatalyst; Adsorption; Chemical engineering; Hydroxide; Inorganic chemistry; Chemistry; Electrode; Crystallography; Physical chemistry; Metallurgy","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":[0.00009533279,0.0003074774,0.000237604,0.0002077559,0.0001976586,0.0005072764,0.000326349,0.0002509759,0.001349138],"category_scores_gemma":[0.0002464694,0.0001702342,0.0001478283,0.0002241008,0.0002596819,0.0002264493,0.000261345,0.0002759073,0.0003594555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005085733,"about_ca_system_score_gemma":0.000205591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002319273,"about_ca_topic_score_gemma":0.003653947,"domain_scores_codex":[0.9998326,0.00001078251,0.000009816854,0.0000458873,0.00005357503,0.00004738691],"domain_scores_gemma":[0.9998792,0.00002460113,0.0000263802,0.00002631386,0.00002785998,0.00001556953],"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":[0.00003162745,0.00001070402,0.000127493,0.00004834334,0.000004101643,0.00008609358,0.00002618397,0.0002220619,0.9947774,0.0002448769,0.000297112,0.004123926],"study_design_scores_gemma":[0.000001486341,0.00001690768,0.0002413724,9.058262e-7,0.000001805688,0.00004168798,0.000008149366,0.001065416,0.9977165,0.00001660537,0.0008867879,0.000002405977],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9709966,0.0008212437,0.01697991,0.000177112,0.00008871242,0.00002957849,0.0003807077,0.0008657855,0.009660539],"genre_scores_gemma":[0.9931554,0.0002270378,0.004878052,0.00002000065,0.000008688659,0.000007580639,0.0001034068,0.00004949944,0.001550392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002319273,"threshold_uncertainty_score":0.004611552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01220921353337431,"score_gpt":0.3070829351170388,"score_spread":0.2948737215836645,"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."}}