{"id":"W4401099064","doi":"10.1002/aenm.202402278","title":"Promises of MOF‐Based and MOF‐Derived Materials for Electrocatalytic CO<sub>2</sub> Reduction","year":2024,"lang":"en","type":"article","venue":"Advanced Energy Materials","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Metal-organic framework; Materials science; Nanotechnology; Catalysis; Renewable energy; Fossil fuel; Scalability; Process engineering; Computer science; Waste management; Chemistry; Engineering","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":[0.0002852328,0.0003506033,0.0002397019,0.0003624379,0.0001306594,0.0005887608,0.0002244431,0.0008821599,0.001054171],"category_scores_gemma":[0.0002726161,0.0001865508,0.0002961886,0.0002459839,0.0001767768,0.0007754475,0.0002438466,0.0005350332,0.0004487382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004661299,"about_ca_system_score_gemma":0.0003471114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004927555,"about_ca_topic_score_gemma":0.0009359314,"domain_scores_codex":[0.9999191,0.00001622136,0.000004609383,0.00001175962,0.00003007207,0.00001823879],"domain_scores_gemma":[0.9999244,0.00002495523,0.00001202384,0.000004473223,0.0000268366,0.000007269003],"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":[0.0002992894,0.0002138221,0.0008757482,0.006007051,0.0001394151,0.00071694,0.000157324,0.005403464,0.5757785,0.05361407,0.01951495,0.3372795],"study_design_scores_gemma":[0.00005534312,0.0007144472,0.002764083,0.0005142884,0.0001793384,0.0007906053,0.0001799128,0.008921989,0.4254339,0.01156729,0.5488104,0.00006843889],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.1166854,0.8335329,0.01645252,0.00594387,0.00097522,0.00005404064,0.0002985568,0.0003279249,0.02572947],"genre_scores_gemma":[0.5966346,0.3748094,0.0154312,0.001725393,0.0004662852,0.00008664661,0.0003635213,0.00004556925,0.01043744],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001054171,"threshold_uncertainty_score":0.003526568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009212438888784905,"score_gpt":0.2546724864846809,"score_spread":0.245460047595896,"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."}}