{"id":"W2509938233","doi":"10.1039/9781782623632-00198","title":"Prospects of Magnetic Nanoparticles for Magnetic Field-Assisted Mixing of Fluids with Relevance to Chemical Engineering","year":2016,"lang":"en","type":"book-chapter","venue":"","topic":"Characterization and Applications of Magnetic Nanoparticles","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Magnetic field; Magnetic nanoparticles; Microfluidics; Magnetization; Nanofluid; Mixing (physics); Magnetic energy; Materials science; Magnet; Physics; Nanotechnology; Mechanics; Mechanical engineering; Nanoparticle; Engineering","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":[0.0000438383,0.0002367669,0.0003539702,0.0001055118,0.00001233302,0.00001163472,0.0001730723,0.000120432,0.0002946181],"category_scores_gemma":[0.00004498342,0.0002002473,0.00006580634,0.00006574093,0.00003721338,0.00003569442,0.00003340996,0.00006298209,0.00001298677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002637996,"about_ca_system_score_gemma":0.00001886573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.864502e-7,"about_ca_topic_score_gemma":0.000001460243,"domain_scores_codex":[0.9989598,0.000001570684,0.0004530393,0.0002240908,0.000161685,0.0001998334],"domain_scores_gemma":[0.999211,0.0001687016,0.00006308442,0.0003390662,0.0001235952,0.00009457804],"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":[0.00003712949,0.00001482642,0.000005008743,0.0003887863,0.00001638718,5.380363e-7,0.00002632116,0.0001252733,0.9579996,0.02246937,0.0002070735,0.01870964],"study_design_scores_gemma":[0.0005638976,0.0005002817,0.00007472625,0.0005961283,0.00006417748,0.000005506976,0.00000280457,0.001603914,0.9853803,0.0004292613,0.01043456,0.0003444668],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.676274,0.006099213,0.1157914,0.002426805,0.0006809339,0.009952775,0.0006048919,0.002045429,0.1861245],"genre_scores_gemma":[0.8988388,0.0001075455,0.05458359,0.0000586499,0.0001207571,0.0002884603,0.00001616405,0.0001870943,0.04579896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2225647,"threshold_uncertainty_score":0.8165848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007046575331959778,"score_gpt":0.1883128069972057,"score_spread":0.1812662316652459,"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."}}