{"id":"W2037666888","doi":"10.1039/b513183a","title":"DC-dielectrophoretic separation of microparticles using an oil droplet obstacle","year":2005,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Sandia National Laboratories; Natural Sciences and Engineering Research Council of Canada","keywords":"Dielectrophoresis; Electric field; Microfluidics; Materials science; Voltage; Particle (ecology); Electrophoresis; Electrode; Range (aeronautics); Field strength; Field (mathematics); Particle size; Analytical Chemistry (journal); Nanotechnology; Chromatography; Chemistry; Electrical engineering; Composite material; Magnetic field; 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":[],"consensus_categories":[],"category_scores_codex":[0.00005641611,0.0001055047,0.0001280665,0.00006347737,0.00003765257,0.00001704168,0.00009058084,0.00007937504,0.00002123147],"category_scores_gemma":[0.00001396591,0.00009744408,0.00002901313,0.0001340228,0.00005293118,0.00005841615,0.00001434537,0.00009621384,0.00002302215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005180796,"about_ca_system_score_gemma":0.00000957605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001296975,"about_ca_topic_score_gemma":0.00001351624,"domain_scores_codex":[0.9994354,0.00001574895,0.0001588174,0.0001183234,0.00007878465,0.0001929761],"domain_scores_gemma":[0.9996974,0.00001239177,0.00002610482,0.0002165651,0.00001986765,0.00002766528],"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":[0.00001095447,0.0000457903,0.00007148563,0.00001693246,0.000008004346,0.00000180072,0.0001436636,0.001145991,0.9854563,0.0005141135,0.0002580176,0.01232695],"study_design_scores_gemma":[0.0001572322,0.00008794443,0.0001698332,0.00002505335,0.00001046279,0.00001117425,0.00004017133,0.02979968,0.967508,0.0001161554,0.001956123,0.0001181741],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979728,0.0006745042,0.0004831173,0.00007983132,0.00005281413,0.00003219402,0.000005572061,0.0003556205,0.0003435452],"genre_scores_gemma":[0.996067,0.0001325326,0.003633362,0.00004656607,0.00005056784,0.000001086426,0.000004620862,0.00001895563,0.0000452734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02865369,"threshold_uncertainty_score":0.3973655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01902543284948262,"score_gpt":0.2462628633586947,"score_spread":0.2272374305092121,"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."}}