{"id":"W3005104573","doi":"10.31635/ccschem.020.201900031","title":"Defect-Rich, Candied Haws-Shaped AuPtNi Alloy Nanostructures for Highly Efficient Electrocatalysis","year":2020,"lang":"en","type":"article","venue":"CCS Chemistry","topic":"Electrocatalysts for Energy Conversion","field":"Energy","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Brookhaven National Laboratory; Argonne National Laboratory; Ministry of Education, India; National Natural Science Foundation of China; U.S. Department of Energy; Nanyang Technological University; Office of Science; Agency for Science, Technology and Research; Canadian Light Source; Beijing Synchrotron Radiation Facility; City University of Hong Kong","keywords":"Electrocatalyst; Alloy; Nanostructure; Materials science; Nanotechnology; Metallurgy; Chemistry; Electrochemistry; Electrode; Physical chemistry","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":[0.00008142105,0.0001794647,0.0001570593,0.0001033827,0.0001201208,0.0002468419,0.0002220447,0.0002067751,0.0004816947],"category_scores_gemma":[0.0001667181,0.0001261241,0.0001185682,0.00009897098,0.0001780622,0.0002550706,0.0002247271,0.0002852048,0.0002443274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002958589,"about_ca_system_score_gemma":0.000172136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003377121,"about_ca_topic_score_gemma":0.001361865,"domain_scores_codex":[0.9999349,0.000004347206,0.000006492019,0.00002016908,0.00002594755,0.000008141044],"domain_scores_gemma":[0.9999406,0.000008364553,0.00001383587,0.0000125314,0.00001349452,0.00001117146],"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":[0.00001477272,0.000007805596,0.0001888929,0.00005315581,0.000004029919,0.00005525327,0.00002044289,0.0004199445,0.995025,0.0006501474,0.0001156898,0.003444776],"study_design_scores_gemma":[0.000003169025,0.00004041867,0.0004693236,0.000002560663,0.000003823398,0.0001344188,0.00001492701,0.004619838,0.9924564,0.000131564,0.002119505,0.000004021449],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9670879,0.001168311,0.02465514,0.0001404659,0.00006826397,0.00005516935,0.000245339,0.0003234151,0.006256109],"genre_scores_gemma":[0.977375,0.0003786171,0.02039152,0.00002512915,0.000006895442,0.00002308184,0.000137645,0.00002880198,0.001633237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004816947,"threshold_uncertainty_score":0.002146661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007828986486793,"score_gpt":0.2065941825371257,"score_spread":0.1987651960503327,"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."}}