{"id":"W2185549230","doi":"10.1021/acs.analchem.5b03616","title":"Direct Interface between Digital Microfluidics and High Performance Liquid Chromatography–Mass Spectrometry","year":2015,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto; Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Research Chairs","keywords":"Chemistry; Derivatization; Microfluidics; Chromatography; Mass spectrometry; High-performance liquid chromatography; Interface (matter); Methanol; Aqueous solution; Nanotechnology; Organic chemistry","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":[0.001435356,0.0008502444,0.0005613666,0.001083986,0.0003975527,0.00101357,0.001902693,0.0008854484,0.002348167],"category_scores_gemma":[0.001469798,0.0007417278,0.0004707342,0.000388072,0.0006788904,0.0008777705,0.00193643,0.001039401,0.001640401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006510926,"about_ca_system_score_gemma":0.0007951792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000340555,"about_ca_topic_score_gemma":0.0006029575,"domain_scores_codex":[0.9980161,0.0002847508,0.0001389987,0.0005556144,0.0008579416,0.000146577],"domain_scores_gemma":[0.999338,0.0002976197,0.0001063025,0.000104647,0.0001019957,0.00005148543],"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":[0.0001171657,0.0001133744,0.0005422006,0.0002203344,0.00003179596,0.0001296424,0.00005898094,0.0002996591,0.95157,0.002407312,0.001672746,0.04283678],"study_design_scores_gemma":[0.00004611361,0.0001833125,0.0008514368,0.00002492304,0.00002756633,0.0004403522,0.00001346879,0.006569441,0.9593477,0.0009209881,0.0315312,0.00004359678],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06508401,0.004812085,0.9142117,0.0007296982,0.001061425,0.0008579897,0.0007700323,0.005884888,0.006588227],"genre_scores_gemma":[0.1891128,0.003010246,0.7959486,0.001359564,0.0006575953,0.001539299,0.0007719995,0.0002429545,0.007357017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002348167,"threshold_uncertainty_score":0.007855356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008578116644468689,"score_gpt":0.2093736157469954,"score_spread":0.2007954991025267,"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."}}