{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001008782,0.0002239992,0.0002842922,0.00006347846,0.00003643614,0.00007847144,0.0002198667,0.0002005504,0.00001114844],"category_scores_gemma":[0.00008816228,0.0002198941,0.00005355314,0.0003384621,0.0001748601,0.0001284482,0.00007940394,0.0003371923,0.00002182669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007915364,"about_ca_system_score_gemma":0.00002177134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003456986,"about_ca_topic_score_gemma":3.112265e-8,"domain_scores_codex":[0.9989764,0.000003661869,0.0002197135,0.0002547269,0.0001458589,0.0003996038],"domain_scores_gemma":[0.9994506,0.00005109798,0.00002284477,0.0002683539,0.00003513708,0.000172015],"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":[0.00001886203,0.00001929253,0.008264167,0.0001746982,0.0002004073,0.00001240891,0.0000356383,0.000009890293,0.9811787,0.00001654189,0.00747931,0.002590058],"study_design_scores_gemma":[0.0002159415,0.00009191332,0.0002862283,0.00004350851,0.00004207165,0.00002259982,0.00005494593,0.0001848132,0.995169,0.00006288676,0.003557117,0.0002689133],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844354,0.004297944,0.0014408,0.00008976048,0.00003350624,0.00003540582,0.00002387471,0.0008850924,0.008758241],"genre_scores_gemma":[0.9987895,0.000514932,0.000254895,0.000008256264,0.0001081942,0.000002859033,0.00002122702,0.00003370834,0.0002664149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01435414,"threshold_uncertainty_score":0.8967021,"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."}}