{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003174928,0.0004492411,0.0002510848,0.0002325522,0.0001757251,0.0004294346,0.0002618218,0.0003590702,0.0005168617],"category_scores_gemma":[0.0004642393,0.00019246,0.000267391,0.0001692125,0.0003057331,0.0004522152,0.0002598129,0.0003351858,0.000230804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003813944,"about_ca_system_score_gemma":0.0002354281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003384395,"about_ca_topic_score_gemma":0.0004645269,"domain_scores_codex":[0.9998696,0.00002711872,0.0000104117,0.00003532134,0.00003059403,0.00002690999],"domain_scores_gemma":[0.9998485,0.00005092204,0.00005060494,0.00001607062,0.00002111668,0.00001280421],"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":[0.00001990806,0.000008240923,0.00006532018,0.00002711284,0.00000189459,0.00002035096,0.00001449691,0.0001588093,0.9964567,0.0003332426,0.00002148711,0.002872353],"study_design_scores_gemma":[0.00000687795,0.00006647595,0.0001746779,0.000001362554,0.000003986807,0.00003024282,0.000004606079,0.002918013,0.9961604,0.0000273269,0.0006017833,0.000004244727],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9330621,0.001589116,0.06164902,0.0002379183,0.00006436792,0.0001083678,0.00005031926,0.0002884968,0.002950208],"genre_scores_gemma":[0.971576,0.0004458585,0.02676397,0.00005510137,0.00002004503,0.00004239153,0.00003563313,0.00002234997,0.001038649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005168617,"threshold_uncertainty_score":0.002767205,"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."}}