{"id":"W3044873987","doi":"10.1021/acs.jpclett.0c01261","title":"Highly Selective Electrocatalytic Reduction of CO<sub>2</sub> into Methane on Cu–Bi Nanoalloys","year":2020,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry Letters","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Light Source (Canada)","funders":"Office of Science; Collaborative Innovation Center of Suzhou Nano Science and Technology; Priority Academic Program Development of Jiangsu Higher Education Institutions; National Natural Science Foundation of China","keywords":"Overpotential; Selectivity; Faraday efficiency; Catalysis; Methane; Materials science; Chemical engineering; Metal; Reversible hydrogen electrode; Inorganic chemistry; Electrode; Chemistry; Electrochemistry; Metallurgy; Physical chemistry; Organic chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001832864,0.000193928,0.0003647518,0.00003408732,0.0000560539,0.00001169897,0.0003488804,0.00006025006,0.00001125685],"category_scores_gemma":[0.00007334968,0.0001431307,0.0002769769,0.0003593336,0.000146733,0.000114642,0.00003174939,0.0005818127,0.00001105485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001620547,"about_ca_system_score_gemma":0.00006061842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002781609,"about_ca_topic_score_gemma":3.585894e-7,"domain_scores_codex":[0.9987389,0.00007805297,0.0003758613,0.0001643045,0.000441278,0.0002015795],"domain_scores_gemma":[0.9989565,0.00008537191,0.0004830886,0.0002105491,0.0001367006,0.0001277706],"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":[0.000466683,0.0001078641,6.941875e-7,0.000058409,0.0001612806,0.000005106008,0.0008062191,0.0008690641,0.9919083,0.00005549519,0.00354851,0.002012342],"study_design_scores_gemma":[0.0003226313,0.0002723322,0.00001046672,0.00004096697,0.0001461504,0.00008977469,0.0001461733,0.00009987498,0.9979488,0.000453725,0.0003304036,0.0001386795],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920549,0.00003765155,0.0003059484,0.007076974,0.00003951776,0.00006298155,0.000001894263,0.00003914064,0.0003809715],"genre_scores_gemma":[0.9983235,0.00002880256,0.00003289757,0.0003841058,0.001179952,0.000003342479,0.000007655877,0.00002699574,0.00001275784],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006692869,"threshold_uncertainty_score":0.5836701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007741285567799349,"score_gpt":0.2325516695980288,"score_spread":0.2248103840302294,"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."}}