{"id":"W3009202962","doi":"10.1007/s00542-020-04787-9","title":"Design and development of an efficient fluid mixing for 3D printed lab-on-a-chip","year":2020,"lang":"en","type":"article","venue":"Microsystem Technologies","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mixing (physics); Micromixer; Reynolds number; Péclet number; Waviness; Microfluidics; Materials science; Volumetric flow rate; Chip; Inlet; Flow (mathematics); Mechanics; Microchannel; Mechanical engineering; Electrical engineering; Composite material; Engineering; Nanotechnology; Physics; Turbulence","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":[0.0001377317,0.0001492082,0.0002096774,0.00007411467,0.00009139653,0.00001939605,0.0002182951,0.0001259053,0.000001761811],"category_scores_gemma":[0.00002723402,0.0001365408,0.00002059708,0.0001569696,0.00004294941,0.00001714788,0.00005126727,0.0000854235,0.000006605146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004404934,"about_ca_system_score_gemma":0.00002541834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.18879e-7,"about_ca_topic_score_gemma":3.399974e-7,"domain_scores_codex":[0.9992059,0.0000101256,0.0002805649,0.0002284508,0.00007230587,0.0002026422],"domain_scores_gemma":[0.9996202,0.00005444111,0.0000415528,0.0002128978,0.00004092646,0.00003000814],"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":[0.00001732251,0.00001522147,0.000006418762,0.0002943182,0.00002982234,5.022751e-7,0.0004743053,0.0005581735,0.9883078,0.0003798361,0.0007938442,0.009122405],"study_design_scores_gemma":[0.000205161,0.00009739991,0.00001919296,0.00005334794,0.00000755566,0.000003931411,0.0007721474,0.01063528,0.9748946,0.00001800479,0.01314666,0.0001466946],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6457221,0.008121535,0.3443558,0.00008628603,0.00001967335,0.0006516907,0.0000116364,0.001012006,0.00001933122],"genre_scores_gemma":[0.9421102,0.0006676629,0.05695696,0.00001548403,0.000007742776,0.0001988762,0.000009493746,0.00002969163,0.000003913373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2963881,"threshold_uncertainty_score":0.5567972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01817400485158067,"score_gpt":0.2116357034991755,"score_spread":0.1934616986475949,"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."}}