{"id":"W2956114943","doi":"10.1109/tuffc.2019.2926211","title":"Thin Film PZT-Based PMUT Arrays for Deterministic Particle Manipulation","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"FujiFilm VisualSonics (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering; National Defense Science and Engineering Graduate; Sunnybrook Research Institute; National Science Foundation","keywords":"Materials science; PMUT; Lead zirconate titanate; Piezoelectricity; Ultrasonic sensor; Acoustics; Diaphragm (acoustics); Waveform; Microfluidics; Transducer; Sound pressure; Particle (ecology); Levitation; Electromechanical coupling coefficient; Voltage; Optoelectronics; Dielectric; Electrical engineering; Composite material; Nanotechnology; Ferroelectricity; Vibration; Engineering","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":[0.0001500689,0.0002260411,0.0002512122,0.000124729,0.0001500955,0.00006135945,0.000117221,0.0002083282,0.00002732294],"category_scores_gemma":[0.00002224093,0.0002186745,0.0001067261,0.000233797,0.00004051779,0.00008584794,2.593614e-7,0.0002610652,0.00001903483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009535163,"about_ca_system_score_gemma":0.00004308544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001362283,"about_ca_topic_score_gemma":0.00002107812,"domain_scores_codex":[0.9989179,0.00002000957,0.0002763632,0.0002686957,0.0001285892,0.0003884623],"domain_scores_gemma":[0.9992431,0.0003205889,0.00004320719,0.0002634649,0.0000626386,0.00006697972],"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":[0.0001909855,0.0002038243,0.0003802781,0.0002271608,0.0001632276,0.000006648335,0.000123481,0.1780333,0.7684682,0.002825042,0.0003268273,0.04905104],"study_design_scores_gemma":[0.00147856,0.0003741783,0.0000810892,0.00002586841,0.00008383504,0.00000754719,0.00001841151,0.879987,0.1165162,0.0009218812,0.0002312224,0.0002742246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.175298,0.0005063325,0.8224455,0.0001345191,0.0004012041,0.0005527725,0.00007963939,0.0004683151,0.0001137],"genre_scores_gemma":[0.9974013,0.000196498,0.002072513,0.0001369707,0.00002100468,0.00006109677,0.000008145998,0.0000408975,0.00006160208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8221033,"threshold_uncertainty_score":0.8917288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01155493331430625,"score_gpt":0.2040343192162407,"score_spread":0.1924793859019344,"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."}}