{"id":"W3044873987","doi":"10.1021/acs.jpclett.0c01261","title":"Highly Selective Electrocatalytic Reduction of CO<sub>2</sub> into Methane on Cu–Bi Nanoalloys","year":2020,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry Letters","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Light Source (Canada)","funders":"Office of Science; Collaborative Innovation Center of Suzhou Nano Science and Technology; Priority Academic Program Development of Jiangsu Higher Education Institutions; National Natural Science Foundation of China","keywords":"Overpotential; Selectivity; Faraday efficiency; Catalysis; Methane; Materials science; Chemical engineering; Metal; Reversible hydrogen electrode; Inorganic chemistry; Electrode; Chemistry; Electrochemistry; Metallurgy; Physical chemistry; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001129405,0.0003332667,0.0003689241,0.0002396119,0.0002595131,0.000307639,0.0003903841,0.0002751721,0.001315743],"category_scores_gemma":[0.0002086974,0.0002141616,0.0001471384,0.0001865465,0.00016753,0.0002712004,0.0002661402,0.0002765199,0.0006152071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004376136,"about_ca_system_score_gemma":0.0001240422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002603133,"about_ca_topic_score_gemma":0.005599387,"domain_scores_codex":[0.9998478,0.00001288608,0.000009875824,0.00003370351,0.00006524107,0.00003052557],"domain_scores_gemma":[0.9999189,0.00001138917,0.00001041704,0.000009192302,0.00003657748,0.00001345132],"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":[0.00007102644,0.000006784688,0.00008896393,0.00004487504,0.000004385327,0.00002887547,0.00001772626,0.00005240314,0.9976458,0.00005250454,0.000106956,0.001879768],"study_design_scores_gemma":[0.000004566466,0.00004389859,0.001077886,0.000002102963,0.000005202838,0.00003536597,0.00002129535,0.001592543,0.996152,0.00001173435,0.001049154,0.000004342613],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936208,0.0009437221,0.002025069,0.0000907931,0.00005902906,0.00001507145,0.0002434548,0.0002800524,0.002722125],"genre_scores_gemma":[0.9955549,0.0002368515,0.001712195,0.00001858458,0.000007517647,0.00001228921,0.0001778185,0.00002849278,0.002251416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002603133,"threshold_uncertainty_score":0.005175948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007741285567799349,"score_gpt":0.2325516695980288,"score_spread":0.2248103840302294,"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."}}