{"id":"W4414024042","doi":"10.1021/acsnano.5c09554","title":"Steering CO<sub>2</sub> Electroreduction Pathway via Tuning Microenvironment of Cobalt Center in Molecular Catalysts","year":2025,"lang":"en","type":"article","venue":"ACS Nano","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Canadian Light Source (Canada); McGill University; Institut National de la Recherche Scientifique","funders":"Science and Technology Department of Hubei Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Foundation for Innovation","keywords":"Cobalt; Catalysis; Materials science; Center (category theory); Chemistry; Nanotechnology; Inorganic chemistry; Crystallography; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001382861,0.0001655595,0.0002315064,0.0002634545,0.00004400492,0.00001369625,0.0001611021,0.000106165,0.00001108156],"category_scores_gemma":[0.00001090002,0.000184161,0.00009057711,0.0003764978,0.00004941036,0.00009380766,0.00007839193,0.0001420474,0.00001261019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002748906,"about_ca_system_score_gemma":0.00003796595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003133052,"about_ca_topic_score_gemma":0.0000822175,"domain_scores_codex":[0.9988282,0.0000495719,0.0003733786,0.0003041379,0.0001649901,0.0002797044],"domain_scores_gemma":[0.9994507,0.000009830187,0.0001092559,0.0003636609,0.00002759751,0.00003898522],"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":[0.00002600131,0.0001082299,0.0002923841,0.00003952905,0.00003797605,0.000008584638,0.00008911457,0.0001897617,0.9650076,0.0008781059,0.0002884454,0.03303427],"study_design_scores_gemma":[0.0003471174,0.00004255032,0.0004781782,0.00008717423,0.00001593371,0.00001759677,0.00006109054,0.00003000509,0.9906055,0.0002421088,0.007933003,0.000139793],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944882,0.0004450007,0.002860678,0.0001193092,0.0002089711,0.0001880271,0.00000569191,0.00007590083,0.001608208],"genre_scores_gemma":[0.9992363,0.0001653714,0.0001617411,0.00006824661,0.00003002114,0.00004580493,0.00008763409,0.00002238659,0.0001824863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03289448,"threshold_uncertainty_score":0.7509868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004115555948191193,"score_gpt":0.2098111464259546,"score_spread":0.2056955904777634,"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."}}