{"id":"W2591880525","doi":"10.1002/cjce.22825","title":"Large scale preparation of microbubbles by multi‐channel ceramic membranes: Hydrodynamics and mass transfer characteristics","year":2017,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fluid Dynamics and Mixing","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Technology Support Program of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; National Science Foundation","keywords":"Microbubbles; Mass transfer; Mass transfer coefficient; Sparging; Bubble; Ceramic membrane; Materials science; Membrane; Filtration (mathematics); Ceramic; Superficial velocity; Mechanics; Volumetric flow rate; Flow (mathematics); Analytical Chemistry (journal); Chemical engineering; Chemistry; Chromatography; Composite material; Acoustics; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001833612,0.0001357909,0.0002306185,0.00006811473,0.00009060367,0.00008533707,0.0002665462,0.00009596958,0.000003517943],"category_scores_gemma":[0.00004806666,0.0001209516,0.0000624903,0.00003074861,0.00005662233,0.0001377949,0.000008807127,0.0002473078,3.87332e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008881024,"about_ca_system_score_gemma":0.00005033656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001411683,"about_ca_topic_score_gemma":0.0003310793,"domain_scores_codex":[0.9992947,0.000004863541,0.0003035188,0.00007000695,0.00008273073,0.0002442084],"domain_scores_gemma":[0.9994804,0.00002548326,0.00005616247,0.0001798681,0.00005571374,0.0002024126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007137523,0.000005687057,0.0001055387,0.0001905063,0.0000650518,0.000007750587,0.0008604815,0.0555962,0.9429532,0.00007097023,0.00004407975,0.00009335954],"study_design_scores_gemma":[0.0003538514,0.00001083491,0.0001212906,0.0001208995,0.00003454068,0.00004319262,0.0000156543,0.9279233,0.07096873,0.00002128841,0.000245131,0.0001413137],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9713764,0.0007473755,0.02737259,0.00006256523,0.0002418016,0.00006399248,0.00009547778,0.00001153362,0.00002825962],"genre_scores_gemma":[0.9994038,0.00007692263,0.0003917746,0.000007686695,0.00006431266,0.000001236342,0.000008231172,0.0000328496,0.00001320302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8723271,"threshold_uncertainty_score":0.4932263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004573117252613553,"score_gpt":0.18407985641398,"score_spread":0.1795067391613665,"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."}}