{"id":"W3106892890","doi":"10.1002/smll.202004158","title":"Nanostructured Cobalt‐Based Electrocatalysts for CO<sub>2</sub>Reduction: Recent Progress, Challenges, and Perspectives","year":2020,"lang":"en","type":"review","venue":"Small","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Institut national de la recherche scientifique","keywords":"Overpotential; Catalysis; Materials science; Cobalt oxide; Faraday efficiency; Nanotechnology; Cobalt; Density functional theory; Electrochemistry; Oxide; Rational design; Transition metal; Nanostructure; Chemical engineering; Chemistry; Organic chemistry; Computational chemistry; Metallurgy; Physical chemistry; Electrode","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001936217,0.0007759099,0.001691088,0.0002426242,0.0001869637,0.00008829758,0.0003654687,0.0005966112,0.00002900866],"category_scores_gemma":[0.00007431106,0.000703567,0.0005916369,0.0003934388,0.00021291,0.00007857433,0.00008016246,0.0005054835,0.00002567351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004117763,"about_ca_system_score_gemma":0.0005449075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008290677,"about_ca_topic_score_gemma":0.0000462354,"domain_scores_codex":[0.9970973,0.0001665046,0.0006479641,0.001277565,0.0002657019,0.0005449397],"domain_scores_gemma":[0.9983011,0.00005159838,0.0005198771,0.0006183409,0.000246355,0.0002627131],"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":[0.00005084971,0.00006773515,2.574637e-8,0.006463727,0.0003180451,0.000009668172,0.0001888783,1.335158e-7,0.000124807,0.00423616,0.002038113,0.9865019],"study_design_scores_gemma":[0.000365345,0.0003076847,8.806971e-7,0.001396985,0.0007566605,0.0002889383,0.0001352532,0.000004009419,0.004860794,0.000610643,0.9905717,0.0007010967],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000008311579,0.9950938,0.00006186773,0.000999356,0.0004725953,0.002060851,0.00009309031,0.0005462275,0.0006639579],"genre_scores_gemma":[0.0001876481,0.9952239,0.0006751206,0.00002510691,0.001113204,0.001393549,0.00110112,0.0001776988,0.0001026798],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9885336,"threshold_uncertainty_score":0.9995415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05953451211316696,"score_gpt":0.2999769783871195,"score_spread":0.2404424662739525,"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."}}