{"id":"W4412060016","doi":"10.1002/smsc.202400643","title":"Efficient Electrochemical CO<sub>2</sub> Reduction Using AgN<sub>3</sub> Single‐Atom Sites Embedded in Free‐Standing Electrodes for Flow Cell Applications","year":2025,"lang":"en","type":"article","venue":"Small Science","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Institut National de la Recherche Scientifique; Canadian Light Source (Canada); BC Innovation Council","funders":"Office of Energy Research and Development; Natural Resources Canada; National Research Council Canada","keywords":"Faraday efficiency; Catalysis; Electrochemistry; Electrolysis; Electrocatalyst; Selectivity; Electrochemical reduction of carbon dioxide; Carbon monoxide; Carbon fibers; Density functional theory; Materials science; Chemistry; Electrode; Nanotechnology; Inorganic chemistry; Physical chemistry; Organic chemistry; Computational 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006850103,0.0002439931,0.0002511925,0.0005860292,0.0005116172,0.0001660653,0.0005498484,0.0001520517,0.000004443533],"category_scores_gemma":[0.0001210222,0.0002692427,0.0001266537,0.002689304,0.0003738283,0.0001601973,0.0001249114,0.0002503001,0.000005244341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001011507,"about_ca_system_score_gemma":0.0003758558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004460134,"about_ca_topic_score_gemma":0.00009654208,"domain_scores_codex":[0.9975467,0.00003927403,0.0004334701,0.0008423504,0.0003494825,0.0007886664],"domain_scores_gemma":[0.9988494,0.00007727522,0.0001524426,0.0005603968,0.0002335774,0.0001269127],"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":[0.00003615511,0.000186035,0.00001578478,0.00005392425,0.000005164163,5.673975e-7,0.0001166018,0.002933589,0.9811417,0.003216707,0.0001576273,0.0121361],"study_design_scores_gemma":[0.0003033411,0.00006241396,0.00002008745,0.00005970879,0.00002916244,0.00001972612,0.0001530724,0.02122064,0.9728457,0.004367403,0.0006480112,0.0002706833],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8561218,0.0004499493,0.1412957,0.0001663552,0.0001640281,0.0006170468,0.000007668205,0.0002267469,0.0009507813],"genre_scores_gemma":[0.9902075,0.00005828255,0.009140033,0.00004788354,0.0001560783,0.000287802,0.00003188099,0.00002517259,0.00004534743],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1340858,"threshold_uncertainty_score":0.999976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01520562216543071,"score_gpt":0.2615314122866578,"score_spread":0.2463257901212271,"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."}}