{"id":"W3005105004","doi":"10.1021/acsenergylett.0c00018","title":"Boosting CO<sub>2</sub> Electroreduction to CH<sub>4</sub> via Tuning Neighboring Single-Copper Sites","year":2020,"lang":"en","type":"article","venue":"ACS Energy Letters","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":534,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Science and Technology Commission of Shanghai Municipality; Office of Science; Ministry of Science and Technology of the People's Republic of China; Shanghai Municipal Education Commission; National Natural Science Foundation of China","keywords":"Copper; Nitrogen; Catalysis; Density functional theory; Pyrolysis; Electrocatalyst; Carbon fibers; Chemistry; Doping; Inorganic chemistry; Dispersion (optics); Analytical Chemistry (journal); Materials science; Electrochemistry; Physical chemistry; Computational chemistry; Electrode; Composite number","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":[0.0001057517,0.0006079218,0.0002508173,0.0002240952,0.0002536219,0.0003887802,0.0005205976,0.0003059492,0.001146303],"category_scores_gemma":[0.0003216088,0.0001822879,0.0001247812,0.0001913286,0.0003581083,0.0005246091,0.0003530961,0.0003132155,0.0003894434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005307019,"about_ca_system_score_gemma":0.0001751236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001264323,"about_ca_topic_score_gemma":0.003568923,"domain_scores_codex":[0.9998674,0.0000123186,0.000006689455,0.00003729005,0.00004300658,0.00003324315],"domain_scores_gemma":[0.9998552,0.00003999376,0.00003859986,0.00001921819,0.00003237771,0.00001457905],"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":[0.0001101611,0.00005102504,0.0006031404,0.0001877025,0.00001667157,0.0001248294,0.00005023594,0.0009394739,0.9862039,0.0008399124,0.0002508814,0.01062194],"study_design_scores_gemma":[0.00000430684,0.00005641286,0.0006530749,0.000002806443,0.000007083397,0.00005868096,0.00002190087,0.003244555,0.9945149,0.00006792902,0.001363006,0.000005379855],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982462,0.001550831,0.007806704,0.0001382734,0.0000830093,0.00003343841,0.00008042652,0.0002238637,0.007621373],"genre_scores_gemma":[0.9942175,0.0004421431,0.004374388,0.00002671664,0.00001399852,0.00001755839,0.00004821826,0.00003199274,0.0008274615],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001264323,"threshold_uncertainty_score":0.003850579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01667346891143775,"score_gpt":0.2181442066576322,"score_spread":0.2014707377461945,"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."}}