{"id":"W2996601609","doi":"10.1039/c9en00637k","title":"Coupling single particle ICP-MS with field-flow fractionation for characterizing metal nanoparticles contained in nanoplastic colloids","year":2019,"lang":"en","type":"article","venue":"Environmental Science Nano","topic":"Field-Flow Fractionation Techniques","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Environmental Protection Agency","keywords":"Field flow fractionation; Nanoparticle; Particle (ecology); Materials science; Fractionation; Inductively coupled plasma mass spectrometry; Coupling (piping); Matrix (chemical analysis); Metal; Field (mathematics); Chemical engineering; Composite material; Analytical Chemistry (journal); Nanotechnology; Mass spectrometry; Chemistry; Metallurgy; Chromatography; Geology","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":[0.0005220504,0.0005748527,0.0004781797,0.0009446636,0.0003736779,0.0004475743,0.0004315791,0.0005698345,0.0005045141],"category_scores_gemma":[0.0006557682,0.0003346016,0.0002481828,0.0002764775,0.0005346916,0.0004155506,0.0002705333,0.0005264871,0.0003240677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005300397,"about_ca_system_score_gemma":0.0004697846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001458192,"about_ca_topic_score_gemma":0.002413224,"domain_scores_codex":[0.9996864,0.00004162252,0.00001682517,0.0001050832,0.0001220239,0.00002816444],"domain_scores_gemma":[0.9995773,0.000217861,0.00004818676,0.00003159839,0.00009860069,0.00002645849],"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":[0.00001531947,0.000009795097,0.00008265626,0.00001760447,0.000002875055,0.000007025433,0.000009181585,0.00007539487,0.9982638,0.0000387295,0.00001654428,0.001461052],"study_design_scores_gemma":[0.000003950611,0.0000288088,0.0003519037,0.000001353392,0.000004154785,0.00003826657,0.000007307203,0.001829928,0.9974025,0.00006409208,0.0002641133,0.000003666021],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7593498,0.001391428,0.2355018,0.0001917921,0.0001104248,0.0002442469,0.0004523406,0.0007854495,0.001972793],"genre_scores_gemma":[0.797646,0.001182881,0.1980044,0.0001537286,0.00004734362,0.000247112,0.0003432707,0.0001355302,0.002239806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001458192,"threshold_uncertainty_score":0.003845692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005843452690252957,"score_gpt":0.1948746765687807,"score_spread":0.1890312238785277,"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."}}