{"id":"W4389542579","doi":"10.1039/d3qi01769a","title":"Optimizing Bi active sites by Ce doping for boosting formate production in a wide potential window","year":2023,"lang":"en","type":"article","venue":"Inorganic Chemistry Frontiers","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Light Source (Canada); University of Alberta","funders":"Canada First Research Excellence Fund; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Formate; Doping; Faraday efficiency; Pyrolysis; Materials science; Catalysis; Boosting (machine learning); Inorganic chemistry; Chemical engineering; Chemistry; Optoelectronics; Physical chemistry; Electrochemistry; Electrode; Computer science; Organic chemistry","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":[0.0002406163,0.0005246936,0.0003251443,0.0002948368,0.0002752552,0.0007495442,0.0005227555,0.0005123609,0.001694966],"category_scores_gemma":[0.0003637022,0.0002980008,0.0001410546,0.0002831568,0.0002289389,0.0006581226,0.0003512228,0.0004458132,0.0007268016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002710352,"about_ca_system_score_gemma":0.0001732931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006364599,"about_ca_topic_score_gemma":0.001549484,"domain_scores_codex":[0.999864,0.00001116129,0.00001113855,0.00003450221,0.00005042978,0.00002884956],"domain_scores_gemma":[0.9999076,0.00002473533,0.00001437565,0.000009318753,0.00003020624,0.0000137218],"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":[0.00006513907,0.00001421741,0.00006098347,0.00004559034,0.000002566611,0.00003608323,0.0000155504,0.000101299,0.9975964,0.0001702885,0.00005828502,0.001833379],"study_design_scores_gemma":[0.00000838741,0.00006691489,0.0002000037,0.000003938425,0.000004629044,0.00005653126,0.00001587888,0.001030574,0.9975228,0.00004545717,0.001039838,0.000005029584],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9762055,0.002447235,0.01195174,0.0002961362,0.00006052051,0.00005398119,0.0002482536,0.0005076084,0.008229102],"genre_scores_gemma":[0.9915203,0.0008305142,0.005407033,0.00005732396,0.000007587591,0.00003485652,0.0001100147,0.00005779351,0.001974639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001694966,"threshold_uncertainty_score":0.005670249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008283273239948066,"score_gpt":0.2233152940636471,"score_spread":0.215032020823699,"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."}}