{"id":"W2513646043","doi":"10.1002/aic.15456","title":"Enhancing liquid micromixing using low‐frequency rotating nanoparticles","year":2016,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Micromixing; Mixing (physics); Magnetic field; Microfluidics; Magnetic nanoparticles; Rotating magnetic field; Chemistry; Mechanics; Nanoparticle; Materials science; Nanotechnology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002828631,0.0001428004,0.0001463198,0.00007708775,0.0002451406,0.0000572249,0.0001639329,0.00006113733,0.0002068117],"category_scores_gemma":[0.00003726632,0.0001077589,0.00007332423,0.0001557044,0.0000292157,0.0002211127,0.00002136401,0.0001804794,0.00006878996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001456137,"about_ca_system_score_gemma":0.00005728985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005285241,"about_ca_topic_score_gemma":0.000002331305,"domain_scores_codex":[0.9989238,0.00003472569,0.0003748434,0.0001239724,0.0001334042,0.0004092983],"domain_scores_gemma":[0.9995205,0.00005814894,0.00007270773,0.0001573579,0.00006149832,0.0001297427],"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":[0.000004890917,0.000007408865,0.0002015479,0.00001110729,0.00003043772,0.000008263181,0.0001306257,0.00002330252,0.9966918,0.0001094078,0.001156076,0.001625198],"study_design_scores_gemma":[0.0002183532,0.00003061943,0.0001275265,0.0001618295,0.00002211535,0.0003230848,0.00005657758,0.0001662656,0.9973809,0.0003791074,0.0009569806,0.0001766872],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9540893,0.01260112,0.03269004,0.0001142979,0.0001431781,0.00006063361,0.000001903936,0.00009820756,0.0002013445],"genre_scores_gemma":[0.9895334,0.007200587,0.002788649,0.00005953812,0.0003326533,0.000005180059,4.205944e-7,0.00004412856,0.00003545684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03544411,"threshold_uncertainty_score":0.439428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008405687280405062,"score_gpt":0.214956355262627,"score_spread":0.2065506679822219,"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."}}