{"id":"W2015373978","doi":"10.1016/j.chroma.2012.02.011","title":"Detection and characterization of silver nanoparticles in aqueous matrices using asymmetric-flow field flow fractionation with inductively coupled plasma mass spectrometry","year":2012,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Nanoparticles: synthesis and applications","field":"Materials Science","cited_by":115,"is_retracted":false,"has_abstract":false,"ca_institutions":"Trent University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Research Foundation; McMaster University","keywords":"Chemistry; Detection limit; Inductively coupled plasma mass spectrometry; Chromatography; Aqueous solution; Fractionation; Wastewater; Field flow fractionation; Mass spectrometry; Inductively coupled plasma; Nanoparticle; Silver nanoparticle; Standard solution; Analytical Chemistry (journal); Nanotechnology; Plasma; Materials science; Environmental engineering","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":[0.0005147044,0.00009956919,0.0002264128,0.0006355234,0.00008514978,0.00003770142,0.00007196891,0.00007262108,0.00003874279],"category_scores_gemma":[0.00006365783,0.00008020345,0.00004869663,0.00107074,0.00004700426,0.0008679234,0.00001156915,0.0001077548,0.000001402559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004324516,"about_ca_system_score_gemma":0.00003083104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002439214,"about_ca_topic_score_gemma":0.000005592003,"domain_scores_codex":[0.998893,0.00008078031,0.0004510758,0.0001033384,0.0002921798,0.0001796836],"domain_scores_gemma":[0.9988461,0.0001798362,0.0006743389,0.00008112036,0.0001489654,0.0000696583],"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":[0.0001162914,0.0001435435,0.03187492,0.00002570569,0.00002017994,0.000001287551,0.0001995253,0.00009016031,0.9650743,0.00002760843,0.000001034441,0.002425439],"study_design_scores_gemma":[0.0004228512,0.0001429104,0.1467452,0.00006943221,0.00004941801,0.0000677283,0.00016516,0.004842408,0.8473182,0.00007815595,0.00001582054,0.00008271998],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876961,0.0002145161,0.01176439,0.00005082326,0.0001112907,0.0001333983,0.000005239884,0.00000987269,0.00001437967],"genre_scores_gemma":[0.9827403,0.00006592859,0.01706365,0.00001251701,0.0001009907,0.000005603018,5.579077e-7,0.000009829372,6.474149e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1177561,"threshold_uncertainty_score":0.3270602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0112554040505226,"score_gpt":0.2297047007459276,"score_spread":0.218449296695405,"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."}}