{"id":"W2037674942","doi":"10.1016/j.cej.2012.05.030","title":"Reducing Taylor dispersion in capillary laminar flows using magnetically excited nanoparticles: Nanomixing mechanism for micro/nanoscale applications","year":2012,"lang":"en","type":"article","venue":"Chemical Engineering Journal","topic":"Characterization and Applications of Magnetic Nanoparticles","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Laminar flow; Nanoparticle; Taylor dispersion; Vorticity; Magnetic nanoparticles; Materials science; Chemical physics; Nanotechnology; Dispersion (optics); Nanoscopic scale; Mechanics; Chemistry; Vortex; Diffusion; Thermodynamics; Physics; Optics","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":[0.0002541181,0.0002516082,0.0002046497,0.0002047123,0.0003034472,0.0002881228,0.0002854861,0.0003429538,0.001111209],"category_scores_gemma":[0.0003251105,0.0001891515,0.0002109516,0.0001161661,0.0003450755,0.0004278402,0.0002562611,0.0004857875,0.0002682996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005125561,"about_ca_system_score_gemma":0.0002849892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007096214,"about_ca_topic_score_gemma":0.00122669,"domain_scores_codex":[0.9998686,0.00001759606,0.000007066148,0.00003658628,0.00003979402,0.00003031622],"domain_scores_gemma":[0.9998354,0.00005746906,0.00005002497,0.00001400805,0.00002509278,0.00001795754],"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":[0.00006189193,0.00002793138,0.00009292593,0.00005801679,0.000004325372,0.00003807491,0.00006037339,0.0002666022,0.9954821,0.0009460442,0.0001592995,0.002802468],"study_design_scores_gemma":[0.000008893715,0.00004044958,0.000128164,0.000001269088,0.000002862152,0.00001351165,0.000006121001,0.001976144,0.9972338,0.0000599273,0.0005253486,0.000003485813],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805699,0.0007287806,0.01512234,0.0003123826,0.00006791981,0.00006183497,0.00004374393,0.000183051,0.00291006],"genre_scores_gemma":[0.9910493,0.0002725522,0.006235022,0.0000801282,0.00002439671,0.00002976466,0.00003072693,0.00003075501,0.002247346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001111209,"threshold_uncertainty_score":0.003718853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009530452528320939,"score_gpt":0.2089568516177084,"score_spread":0.1994263990893875,"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."}}