{"id":"W4220911834","doi":"10.1016/j.isci.2022.104177","title":"Synthetic strategies for MOF-based single-atom catalysts for photo- and electro-catalytic CO2 reduction","year":2022,"lang":"en","type":"review","venue":"iScience","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Key Research and Development Program of China; Tsinghua University; National Natural Science Foundation of China","keywords":"Catalysis; Photocatalysis; Renewable energy; Electrochemical reduction of carbon dioxide; Nanotechnology; Carbon dioxide; Materials science; Selectivity; Metal-organic framework; Atom (system on chip); Photochemistry; Yield (engineering); Chemistry; Combinatorial chemistry; Organic chemistry; Computer science; Carbon monoxide; Electrical engineering","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":[0.0005507494,0.000429633,0.0008796676,0.0003643686,0.000489274,0.0002428478,0.0005510167,0.0001807798,0.00005214585],"category_scores_gemma":[0.00008054588,0.0003905534,0.000448312,0.000752295,0.0002901997,0.0002677017,0.00008739111,0.0002164414,0.000005172828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004105446,"about_ca_system_score_gemma":0.0008031297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001996796,"about_ca_topic_score_gemma":0.00002852391,"domain_scores_codex":[0.9975298,0.00006263468,0.0005501658,0.001009808,0.0003277275,0.0005198779],"domain_scores_gemma":[0.9984774,0.0002489687,0.0004256331,0.0006480601,0.00008417018,0.0001157516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000167061,0.00007655786,1.563829e-8,0.00408693,0.00003111424,0.000001464437,0.00006011088,0.00002045783,0.0005382698,0.004237706,0.001642099,0.9892886],"study_design_scores_gemma":[0.0001420887,0.0002917803,3.455863e-8,0.0006887889,0.0004081347,0.0001832116,0.0001489613,0.0002216429,0.003821949,0.001657736,0.9919823,0.0004534186],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00005860066,0.9898053,0.005434368,0.00006150301,0.0006275938,0.002260803,0.000164555,0.0003001257,0.001287184],"genre_scores_gemma":[0.004700261,0.9843966,0.002013576,0.00004101007,0.0002974734,0.006083539,0.0008989868,0.0001534962,0.001415001],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9903402,"threshold_uncertainty_score":0.9998546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05544379990900385,"score_gpt":0.318789898007108,"score_spread":0.2633460980981042,"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."}}