{"id":"W4390452872","doi":"10.1016/j.susmat.2023.e00820","title":"Engineering tandem catalysts and reactors for promoting electrocatalytic CO2 reduction reaction toward multi‑carbon products","year":2023,"lang":"en","type":"article","venue":"Sustainable materials and technologies","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Tandem; Electrolysis; Electrocatalyst; Biochemical engineering; Nanotechnology; Catalysis; Chemistry; Process engineering; Combinatorial chemistry; Materials science; Engineering; Organic chemistry; Electrochemistry","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":[0.0001643607,0.0003142207,0.0002225436,0.0002831187,0.000219341,0.0006232513,0.0005865085,0.0004921082,0.000774182],"category_scores_gemma":[0.0002212579,0.0003183214,0.0002801639,0.0002701253,0.000186479,0.0005711573,0.0002949439,0.0005043175,0.0003840039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000433536,"about_ca_system_score_gemma":0.0002878041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003670898,"about_ca_topic_score_gemma":0.001386148,"domain_scores_codex":[0.9998872,0.000006091909,0.000008197247,0.00002618555,0.00004866925,0.00002363509],"domain_scores_gemma":[0.9999614,0.000008242893,0.000009491639,0.000005317957,0.000009127933,0.000006327542],"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":[0.00005463117,0.00008781406,0.0001925553,0.00008856814,0.00001477148,0.0000661678,0.00001882379,0.001520341,0.9869944,0.001708968,0.0001408127,0.00911208],"study_design_scores_gemma":[0.00001649404,0.0001281013,0.0003306316,0.000004427978,0.00002255418,0.000104187,0.00002661897,0.01033315,0.9852841,0.0003361757,0.003405994,0.000007615517],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9594124,0.002238743,0.03111761,0.0001935454,0.00009620308,0.0000519297,0.0001160413,0.0003014507,0.006472177],"genre_scores_gemma":[0.9755287,0.001141734,0.01834785,0.00003862319,0.00001695117,0.00004114324,0.0001466567,0.00004190889,0.004696468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000774182,"threshold_uncertainty_score":0.003145516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01205794228425369,"score_gpt":0.2329438754502794,"score_spread":0.2208859331660257,"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."}}