{"id":"W2613951757","doi":"10.1002/anie.201703720","title":"Efficient Electrocatalytic Reduction of CO<sub>2</sub> by Nitrogen‐Doped Nanoporous Carbon/Carbon Nanotube Membranes: A Step Towards the Electrochemical CO<sub>2</sub> Refinery","year":2017,"lang":"en","type":"article","venue":"Angewandte Chemie International Edition","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":304,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Carbon nanotube; Materials science; Electrochemistry; Faraday efficiency; Nanoporous; Carbon fibers; Formate; Chemical engineering; Electrocatalyst; Nanotechnology; Membrane; Electrode; Inorganic chemistry; Chemistry; Catalysis; Organic chemistry; Composite number; Composite material","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001557085,0.000284045,0.0001798192,0.0001626717,0.0001951042,0.0002508087,0.0002500091,0.000430365,0.0003983634],"category_scores_gemma":[0.0001927894,0.0001496935,0.0001710943,0.0001013201,0.0002185348,0.0003836124,0.0002425298,0.0002715344,0.0002465846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004100714,"about_ca_system_score_gemma":0.0003045088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000947448,"about_ca_topic_score_gemma":0.00255045,"domain_scores_codex":[0.99986,0.00001741869,0.000009005245,0.00002692996,0.00006580382,0.00002075663],"domain_scores_gemma":[0.9999152,0.00001486889,0.00001677121,0.00001065537,0.00003363328,0.000008753273],"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":[0.00001036951,0.000006930912,0.00005627254,0.00002997515,0.000002593214,0.0000319544,0.000007531387,0.0001244098,0.9981167,0.000119317,0.0000448711,0.001449066],"study_design_scores_gemma":[0.000001620337,0.0000140699,0.0002253076,0.000001684684,0.00000254202,0.00003948086,0.000005975653,0.0008967139,0.9979095,0.00002262929,0.0008782666,0.000002232734],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9629589,0.002252852,0.02825198,0.0004254644,0.000134645,0.00007188661,0.0002348836,0.0002347624,0.005434538],"genre_scores_gemma":[0.9800518,0.0007897397,0.0166016,0.00005373826,0.00001226323,0.0000202346,0.00008283661,0.0000216266,0.002366175],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000947448,"threshold_uncertainty_score":0.002975225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01081225288214463,"score_gpt":0.2451746945114,"score_spread":0.2343624416292554,"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."}}