{"id":"W3025417095","doi":"10.1149/ma2020-01361512mtgabs","title":"CO<sub>2</sub> Reduction at the Triphasic Interface: Enhancing Ethylene Production Using Polymers with Intrinsic Microporosity at Copper Gas Diffusion Electrodes","year":2020,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Microporous material; Formate; Electrolyte; Copper; Chemical engineering; Ethylene; Materials science; Selectivity; Carbon monoxide; Catalysis; Gaseous diffusion; Methane; Polymer; Electrode; Chemistry; Nanotechnology; Organic chemistry; Composite material; Metallurgy","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":[0.0001001711,0.0005164583,0.0001995241,0.0002326644,0.0001250475,0.0003542995,0.0003080387,0.0003489784,0.0007770191],"category_scores_gemma":[0.0002078964,0.0001929906,0.0001787372,0.0002032531,0.0002204556,0.000550015,0.0003068284,0.0004842285,0.0004781418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000301746,"about_ca_system_score_gemma":0.00008764488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003059959,"about_ca_topic_score_gemma":0.0004973538,"domain_scores_codex":[0.9998858,0.000009998565,0.000007374125,0.00002864357,0.00003287012,0.00003524392],"domain_scores_gemma":[0.9998993,0.00002156523,0.00003867356,0.000008077579,0.00001490419,0.00001757198],"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":[0.00004579018,0.00002915015,0.0001585109,0.0001249429,0.000006144534,0.0001021152,0.00002701281,0.0002668332,0.9941939,0.0002139588,0.000171435,0.004660239],"study_design_scores_gemma":[0.000004065277,0.00009521194,0.000778164,0.000004851552,0.000007445764,0.00009137591,0.000008111474,0.001845965,0.9953493,0.00001860333,0.001790736,0.000006112594],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796979,0.004587234,0.009910335,0.0002114398,0.0000729114,0.00005110562,0.0001495797,0.0003012083,0.005018356],"genre_scores_gemma":[0.9888484,0.002185925,0.006882711,0.0000756772,0.00004433272,0.00002970211,0.00007657648,0.00005137255,0.001805276],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007770191,"threshold_uncertainty_score":0.002599359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01292523195011015,"score_gpt":0.2399209738679155,"score_spread":0.2269957419178053,"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."}}