{"id":"W2034129327","doi":"10.1063/1.4722000","title":"Optimization of an electrokinetic mixer for microfluidic applications","year":2012,"lang":"en","type":"article","venue":"Biomicrofluidics","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Micromixer; Electrokinetic phenomena; Waveform; Microfluidics; Mixing (physics); Microchannel; Mechanics; Flow (mathematics); Acoustics; Amplitude; Materials science; Electronic engineering; Computer science; Physics; Engineering; Optics; Electrical engineering; Voltage; Nanotechnology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002041971,0.0002212468,0.0002431856,0.0001617859,0.0001098041,0.00001848591,0.0002655607,0.000167166,0.00006252177],"category_scores_gemma":[0.000006706883,0.0002409601,0.0001075756,0.0003984312,0.00007733069,0.0001711826,0.00001885357,0.00007969914,0.00002335895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008565402,"about_ca_system_score_gemma":0.00003435495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004015579,"about_ca_topic_score_gemma":8.049675e-8,"domain_scores_codex":[0.9987346,0.0000226333,0.0004199684,0.0002042076,0.0001103821,0.0005082084],"domain_scores_gemma":[0.9992434,0.00004323019,0.00006651249,0.0003835001,0.0000919327,0.0001714144],"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":[0.0000121443,0.0001162439,0.00005016853,0.00007621249,0.00004210601,2.53279e-8,0.00005494107,0.00004779916,0.9389637,0.002920729,0.05558218,0.00213373],"study_design_scores_gemma":[0.0002709927,0.00004366278,0.00007636283,0.000004752872,0.0000602142,0.000008890958,0.00002873333,0.0007941823,0.7165972,0.0001030946,0.2818064,0.0002055658],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08230469,0.23976,0.6756771,0.00004316634,0.0001360732,0.001205513,0.0001577167,0.0002994869,0.0004163048],"genre_scores_gemma":[0.7865279,0.1959003,0.01385443,0.0001579128,0.0007718234,0.001470103,0.0009314881,0.0002410404,0.0001449958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7042232,"threshold_uncertainty_score":0.9826068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008294539392689664,"score_gpt":0.2210067173302624,"score_spread":0.2127121779375727,"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."}}