{"id":"W2328669682","doi":"10.1021/jp302485g","title":"Preparation of PtAu Alloy Colloids by Laser Ablation in Solution and Their Characterization","year":2012,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry C","topic":"Laser-Ablation Synthesis of Nanoparticles","field":"Engineering","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Alloy; Materials science; Laser ablation; Nanoparticle; Colloid; Fluence; Composition (language); Aqueous solution; Diffraction; Metal; Chemical engineering; Electrochemistry; Laser; Analytical Chemistry (journal); Nanotechnology; Composite material; Metallurgy; Chemistry; Optics; Electrode; Chromatography; Physical chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002611548,0.00006834014,0.0001225056,0.0000144548,0.00001776232,0.000007601191,0.0000568543,0.00003449855,0.00001007548],"category_scores_gemma":[0.0000348362,0.00004798957,0.00002662174,0.00008482795,0.00002804285,0.0003627427,0.000009620619,0.00007721093,0.000001291524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003871417,"about_ca_system_score_gemma":0.000007380856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001215406,"about_ca_topic_score_gemma":3.601749e-7,"domain_scores_codex":[0.9995148,0.00003138347,0.0002235986,0.0000296127,0.0001091953,0.00009143995],"domain_scores_gemma":[0.999618,0.00009978834,0.000135251,0.00006651288,0.00004306965,0.00003741184],"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":[0.00003009054,0.00006619125,0.0007433174,0.0000250641,0.00001270847,2.765909e-8,0.0007339442,0.002205251,0.9957124,0.000002886977,0.00006506983,0.0004030283],"study_design_scores_gemma":[0.0001747903,0.00001577231,0.007366566,0.00002513424,0.00001226204,0.000003353631,0.00005468109,0.02581686,0.9662636,0.00002718625,0.0001923071,0.00004750467],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991224,0.00005145999,0.0005801982,0.00005606248,0.00001950518,0.0000432843,0.000006213767,0.000009343164,0.0001115733],"genre_scores_gemma":[0.999777,0.00002546805,0.0000183802,0.000004211479,0.0001380629,0.000001370879,0.000006230786,0.000008109352,0.00002119377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02944884,"threshold_uncertainty_score":0.1956958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007559738039552874,"score_gpt":0.2245335470528993,"score_spread":0.2169738090133465,"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."}}