{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003615568,0.0003930851,0.0003831785,0.00067983,0.0001731659,0.0006525758,0.0004906273,0.0005729992,0.0005178585],"category_scores_gemma":[0.0003974208,0.0002684212,0.0001994654,0.0003487946,0.0006197451,0.001186033,0.00044498,0.0007169689,0.0002423326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003905236,"about_ca_system_score_gemma":0.0002962965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002237338,"about_ca_topic_score_gemma":0.0002389601,"domain_scores_codex":[0.999848,0.00003304716,0.000008535379,0.00002947845,0.00006547545,0.00001541636],"domain_scores_gemma":[0.9998703,0.00006292038,0.00002686534,0.00001115833,0.00001807276,0.00001071051],"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":[0.00004268086,0.00004067105,0.0003540996,0.0001970269,0.000009138284,0.00006762047,0.00007228112,0.004170853,0.9512982,0.01166577,0.0003233249,0.03175844],"study_design_scores_gemma":[0.00002481449,0.0001547109,0.00115185,0.00004883996,0.00001537549,0.00033615,0.00006932528,0.1913773,0.7815621,0.01004402,0.01517306,0.00004252733],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06498709,0.006126754,0.925276,0.0005126117,0.0001191191,0.00005443028,0.000103726,0.0005379171,0.00228236],"genre_scores_gemma":[0.5185786,0.006803941,0.4710082,0.0002274025,0.0001435841,0.0001561036,0.0001219369,0.0001134938,0.002846583],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00067983,"threshold_uncertainty_score":0.002833486,"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."}}