{"id":"W2094364267","doi":"10.1021/ac101379g","title":"Multilayer Hybrid Microfluidics: A Digital-to-Channel Interface for Sample Processing and Separations","year":2010,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Microfluidics; Microchannel; Chemistry; Digital microfluidics; Analyte; Sample preparation; Nanotechnology; Chip; Channel (broadcasting); Sample (material); Computer hardware; Process engineering; Electrowetting; Chromatography; Computer science; Electrode; Materials science; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000519413,0.0008376046,0.00043723,0.0007429081,0.0002890801,0.0008186884,0.0007670278,0.0006312828,0.001337733],"category_scores_gemma":[0.0005219887,0.0003730511,0.0004403785,0.0004041001,0.0003200522,0.0009185366,0.0008784081,0.0004732742,0.00057093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005635139,"about_ca_system_score_gemma":0.0004724271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004859091,"about_ca_topic_score_gemma":0.0008682056,"domain_scores_codex":[0.99958,0.00005065827,0.00004306147,0.0001058901,0.0001740424,0.0000463969],"domain_scores_gemma":[0.9997962,0.00005059149,0.00004614102,0.00003359921,0.00004504195,0.00002839934],"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":[0.00009611938,0.00003926112,0.0004601813,0.0003035816,0.00003733764,0.00003882185,0.00002232505,0.000581065,0.9625617,0.001411403,0.001161918,0.03328632],"study_design_scores_gemma":[0.00004437631,0.0003811739,0.002615804,0.00003797893,0.0001091801,0.0005553525,0.00001630543,0.02140523,0.9315368,0.0008536973,0.04236732,0.00007678742],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2197862,0.01946178,0.7432802,0.001244054,0.001077139,0.0005160208,0.002447044,0.005937984,0.006249612],"genre_scores_gemma":[0.4351735,0.007368505,0.5477138,0.00101369,0.0003091643,0.0005976999,0.001523039,0.0001488088,0.006151909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001337733,"threshold_uncertainty_score":0.004475176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007914710814599622,"score_gpt":0.2488962606334983,"score_spread":0.2409815498188987,"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."}}