{"id":"W2436645641","doi":"10.1021/jacs.6b02878","title":"A Molecular Surface Functionalization Approach to Tuning Nanoparticle Electrocatalysts for Carbon Dioxide Reduction","year":2016,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":425,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Basic Energy Sciences; Lawrence Berkeley National Laboratory; Canadian Institute for Advanced Research; U.S. Department of Energy; Office of Science; Samsung; National Science Foundation","keywords":"Overpotential; Surface modification; Tafel equation; Chemistry; Catalysis; Nanoparticle; Carbene; Nanomaterials; Electrochemical reduction of carbon dioxide; Chemical engineering; Nanotechnology; Carbon dioxide; Inorganic chemistry; Combinatorial chemistry; Electrochemistry; Organic chemistry; Physical chemistry; Materials science; Carbon monoxide; Electrode","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001483174,0.0003642244,0.00017704,0.000205205,0.0001884974,0.0002625448,0.000337491,0.0003937664,0.0007549206],"category_scores_gemma":[0.0001984101,0.0001855997,0.0002449898,0.0001329847,0.000252487,0.0002481496,0.0001600897,0.000526222,0.0002625526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004504847,"about_ca_system_score_gemma":0.0001871157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006874615,"about_ca_topic_score_gemma":0.001480856,"domain_scores_codex":[0.9998409,0.00001828927,0.00001042218,0.00004366602,0.00006636819,0.00002041744],"domain_scores_gemma":[0.9999429,0.00001454864,0.00001015007,0.00000967656,0.00001762931,0.000005083016],"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":[0.000009366929,0.00002163291,0.00003885336,0.00003581695,0.000004720987,0.00001772212,0.000008948663,0.0001806083,0.9962363,0.0003912265,0.00005666127,0.002998092],"study_design_scores_gemma":[0.000002791239,0.00005751237,0.0001516264,0.000001116145,0.000004781446,0.00004539994,0.000003374868,0.001100281,0.9968542,0.00003601477,0.001740051,0.000002923065],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8795299,0.004469025,0.1008782,0.0009358354,0.0002828635,0.000226751,0.0002240139,0.0006370266,0.01281631],"genre_scores_gemma":[0.9459373,0.001848562,0.04558515,0.0002060682,0.000036644,0.00008847773,0.000157838,0.00005029117,0.006089595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007549206,"threshold_uncertainty_score":0.00326854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00948633898800578,"score_gpt":0.2366584167118935,"score_spread":0.2271720777238878,"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."}}