{"id":"W2293457115","doi":"10.1007/s00216-016-9451-x","title":"Separation, detection and characterization of nanomaterials in municipal wastewaters using hydrodynamic chromatography coupled to ICPMS and single particle ICPMS","year":2016,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Nanoparticles: synthesis and applications","field":"Materials Science","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Detection limit; Wastewater; Mass spectrometry; Inductively coupled plasma mass spectrometry; Chemistry; Effluent; Chromatography; Environmental chemistry; Environmental 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006102351,0.0003401998,0.0004293529,0.0008124483,0.0005678104,0.0004435164,0.0003709382,0.0004563983,0.0005928896],"category_scores_gemma":[0.000790239,0.0003348422,0.0003753174,0.0005034812,0.0004096739,0.0003425587,0.000294055,0.000359708,0.0003585998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006761012,"about_ca_system_score_gemma":0.0008425986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003944481,"about_ca_topic_score_gemma":0.006298271,"domain_scores_codex":[0.9992223,0.00009955448,0.00006587835,0.0001581669,0.0003811494,0.00007290622],"domain_scores_gemma":[0.9995998,0.0001186239,0.00004249497,0.00003887572,0.0001758053,0.00002446708],"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":[0.00003402109,0.00001595547,0.0004818807,0.00003835242,0.000005046417,0.00001481082,0.00002719417,0.0001582446,0.9956291,0.00005436732,0.00005297481,0.003487964],"study_design_scores_gemma":[0.000003631477,0.00004481158,0.002633651,0.000002739973,0.000005489034,0.00007174061,0.00002489245,0.001950192,0.9942852,0.00008807306,0.0008821352,0.000007438293],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8936433,0.001158502,0.1002949,0.0002056978,0.00005099678,0.0001866652,0.0009337774,0.0007453737,0.002780778],"genre_scores_gemma":[0.8960359,0.001019231,0.09610161,0.0001944571,0.00002285404,0.0002314255,0.0007941803,0.0001394971,0.005460883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003944481,"threshold_uncertainty_score":0.007843077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01496160568161085,"score_gpt":0.2495812760807723,"score_spread":0.2346196703991615,"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."}}