{"id":"W4410250183","doi":"10.1002/elps.8137","title":"Electrohydrodynamic Vortex Imaging: A New Tool for Understanding Mass Transfer in Surface‐Based Biosensors","year":2025,"lang":"en","type":"article","venue":"Electrophoresis","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"École Centrale de Lyon; Institut National des Sciences Appliquées de Lyon; Centre National de la Recherche Scientifique; Fonds de recherche du Québec – Nature et technologies; Université Grenoble Alpes; Agence Nationale de la Recherche; Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke; Indian National Science Academy","keywords":"Electrohydrodynamics; Dielectrophoresis; Microfluidics; Mass transfer; Electrode; Materials science; Biosensor; Electric field; Nanotechnology; Conductivity; Particle (ecology); Vortex; Electrolyte; Mechanics; Chemistry; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001362668,0.0002686255,0.0003021999,0.000414567,0.000075591,0.00005380872,0.0002092294,0.0001609819,0.00001358959],"category_scores_gemma":[0.00004167149,0.0002740023,0.0001290667,0.0008706678,0.00005364861,0.00005139895,0.000009306644,0.0002392956,0.000003615133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007080887,"about_ca_system_score_gemma":0.0001266336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004935512,"about_ca_topic_score_gemma":0.00006938075,"domain_scores_codex":[0.9985776,0.0000206333,0.0002938616,0.0003246896,0.0001061745,0.0006770301],"domain_scores_gemma":[0.9995432,0.0001147395,0.00001278697,0.0002754858,0.00001914062,0.00003464071],"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":[0.00008136807,0.00001458926,0.0005761963,0.00006744422,0.00004422705,0.00001023785,0.00001961997,0.0007951265,0.9844016,0.003675023,0.00929892,0.001015621],"study_design_scores_gemma":[0.001240711,0.00006725919,0.000333752,0.00009565397,0.00005973345,0.00000288043,0.00006457578,0.1098751,0.8709937,0.01416178,0.00263931,0.0004654982],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4814152,0.003130017,0.5126164,0.001174618,0.0001337662,0.0004041023,0.000009553625,0.0007907883,0.0003255466],"genre_scores_gemma":[0.9954367,0.0004972619,0.003611991,0.00009728093,0.00001897587,0.00001045128,0.00001338783,0.00004719869,0.0002666969],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5140215,"threshold_uncertainty_score":0.9999712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01053414363754688,"score_gpt":0.2148209793782611,"score_spread":0.2042868357407143,"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."}}