{"id":"W2466789682","doi":"10.1039/c6cp04030f","title":"Solving local structure around dopants in metal nanoparticles with ab initio modeling of X-ray absorption near edge structure","year":2016,"lang":"en","type":"article","venue":"Physical Chemistry Chemical Physics","topic":"Nanocluster Synthesis and Applications","field":"Materials Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saskatoon Medical Imaging; University of Saskatchewan","funders":"","keywords":"Dopant; Ab initio; Absorption (acoustics); X-ray; Metal; X-ray absorption fine structure; Materials science; Enhanced Data Rates for GSM Evolution; Nanoparticle; Molecular physics; XANES; Extended X-ray absorption fine structure; Ab initio quantum chemistry methods; Crystallography; Chemical physics; Nanotechnology; Atomic physics; Doping; Chemistry; Absorption spectroscopy; Optics; Molecule; Physics; Optoelectronics; Metallurgy; Composite material; Spectroscopy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003498633,0.0005495135,0.0005570074,0.0003516266,0.0006753571,0.0004940896,0.001025303,0.0009348026,0.0009403953],"category_scores_gemma":[0.0006582284,0.0006432422,0.0003999179,0.0003277555,0.0004757264,0.0004951983,0.0004416592,0.0005959816,0.0001836248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009439789,"about_ca_system_score_gemma":0.001225542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006160691,"about_ca_topic_score_gemma":0.005697362,"domain_scores_codex":[0.9998695,0.00002538507,0.000007146305,0.00002894695,0.0000504165,0.00001861606],"domain_scores_gemma":[0.9998164,0.00006443138,0.00002578594,0.0000433817,0.00003652984,0.00001359118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003319174,0.00003415218,0.0008518151,0.00007178891,0.00001532837,0.000112011,0.00007348369,0.9673005,0.02316852,0.005860693,0.0001040931,0.002374327],"study_design_scores_gemma":[0.000009429918,0.00001536505,0.000133573,0.000003102654,0.000003731157,0.000009954261,0.00001883233,0.9909419,0.007478505,0.001183772,0.0001972062,0.000004594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7451418,0.0002427106,0.2471141,0.0002210147,0.00002929594,0.00008809,0.0003785477,0.0008554174,0.005929046],"genre_scores_gemma":[0.8998253,0.0002120653,0.09787385,0.00004232457,0.000008308545,0.0001727917,0.0002354943,0.0002000344,0.001429926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006160691,"threshold_uncertainty_score":0.01224965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01370743882122155,"score_gpt":0.2285084910706152,"score_spread":0.2148010522493937,"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."}}