{"id":"W2136933625","doi":"10.1021/ac400802g","title":"Field-Flow Fractionation and Hydrodynamic Chromatography on a Microfluidic Chip","year":2013,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Field-Flow Fractionation Techniques","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Field flow fractionation; Chemistry; Microchannel; Drag; Fractionation; Microfluidics; Mechanics; Particle image velocimetry; Gravitational field; Buoyancy; Analytical Chemistry (journal); Chromatography; Nanotechnology; Classical mechanics; Materials science; Physics","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":[0.0002059815,0.000362223,0.0003242197,0.000305934,0.0002799866,0.0002882283,0.0004595954,0.0003990097,0.0005745529],"category_scores_gemma":[0.0003281335,0.0001216399,0.0002547603,0.0002252099,0.0004333994,0.0004249191,0.0002184048,0.0002782251,0.000110585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007101819,"about_ca_system_score_gemma":0.0005283749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00267869,"about_ca_topic_score_gemma":0.001419204,"domain_scores_codex":[0.9998711,0.00001628159,0.000005045638,0.0000325576,0.0000539973,0.00002103934],"domain_scores_gemma":[0.9998913,0.0000499726,0.00001614494,0.000009974409,0.00001890986,0.00001367846],"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":[0.0001138614,0.0001398074,0.001708192,0.0001113696,0.00002565063,0.00009860745,0.00005759648,0.01851329,0.9604437,0.005114243,0.000267313,0.01340641],"study_design_scores_gemma":[0.0000379429,0.0002705269,0.00326289,0.000005402747,0.00001367599,0.0001368657,0.00002078969,0.195419,0.796891,0.001855464,0.002044914,0.00004159446],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9008446,0.001092145,0.09464774,0.0001886308,0.00005245756,0.00008653857,0.0001753888,0.0003344205,0.002578191],"genre_scores_gemma":[0.9210003,0.0005530118,0.07678338,0.0001165757,0.00003130928,0.00007640575,0.0002235028,0.00002403292,0.001191417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00267869,"threshold_uncertainty_score":0.005326211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003667970093508091,"score_gpt":0.2000044006061211,"score_spread":0.196336430512613,"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."}}