{"id":"W2986477834","doi":"10.1021/acscatal.9b02987","title":"Molecular Trapping Strategy To Stabilize Subnanometric Pt Clusters for Highly Active Electrocatalysis","year":2019,"lang":"en","type":"article","venue":"ACS Catalysis","topic":"Electrocatalysts for Energy Conversion","field":"Energy","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; China Postdoctoral Science Foundation; Government of Ontario","keywords":"Catalysis; Electrocatalyst; Carbon nanotube; Methanol; Graphitic carbon nitride; Carbon nitride; Carbon fibers; Covalent bond; Materials science; Chemical engineering; Electron transfer; Nanotechnology; Chemistry; Adsorption; Photochemistry; Electrochemistry; Organic chemistry; Physical chemistry; Photocatalysis; Composite material; Electrode","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00006493999,0.0003905635,0.0001714651,0.0001528046,0.0001487376,0.0001766281,0.0004912974,0.0003636232,0.000991573],"category_scores_gemma":[0.0001547201,0.0001550739,0.0001645252,0.0001313978,0.0002014858,0.0003153801,0.0002267329,0.0004880471,0.0003281388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003967599,"about_ca_system_score_gemma":0.0001548412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004370985,"about_ca_topic_score_gemma":0.00114238,"domain_scores_codex":[0.9999129,0.000007431269,0.000005930676,0.00003384118,0.00002760269,0.00001228541],"domain_scores_gemma":[0.9999433,0.000009785112,0.00002270221,0.000008218489,0.00001017516,0.000005951861],"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":[0.00001552624,0.00001690842,0.00006903582,0.00004328474,0.000006878816,0.00002781219,0.00001156443,0.0003048962,0.9969378,0.0004724794,0.00008041327,0.002013367],"study_design_scores_gemma":[0.000006887234,0.0000831809,0.0002659984,0.000002096137,0.000009946998,0.00005937973,0.000005991024,0.002616996,0.9949473,0.00008400303,0.001913934,0.000004300637],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9482037,0.001486061,0.04366527,0.0003398528,0.0001317689,0.00009309879,0.0001962957,0.0005501492,0.005333814],"genre_scores_gemma":[0.9835277,0.0004381413,0.01412993,0.00007020808,0.00001544011,0.00004852127,0.0001044307,0.00003062496,0.001634978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000991573,"threshold_uncertainty_score":0.003317177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008135203156017309,"score_gpt":0.226376286197813,"score_spread":0.2182410830417957,"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."}}