{"id":"W3024753562","doi":"10.1021/acs.analchem.9b05778","title":"DNA Capture by Nanopore Sensors under Flow","year":2020,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Nanopore and Nanochannel Transport Studies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Nanopore; Chemistry; Microfluidics; Laminar flow; Microchannel; Drag; Nanopore sequencing; Nanotechnology; DNA; Volumetric flow rate; Biophysics; Flow (mathematics); Brownian motion; Electrophoresis; Chemical physics; Mechanics; Chromatography; DNA sequencing; Biochemistry; Materials science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003307538,0.0003272993,0.0003946428,0.0001551398,0.0002229518,0.0004325861,0.0003429904,0.0005543356,0.0003010552],"category_scores_gemma":[0.0005004627,0.0001744144,0.0002235723,0.0001435852,0.0004440554,0.0007806616,0.0002971159,0.0004094695,0.0001384249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004583712,"about_ca_system_score_gemma":0.0003307519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001473206,"about_ca_topic_score_gemma":0.001028434,"domain_scores_codex":[0.9997179,0.00004285069,0.00001298727,0.00007544905,0.000088487,0.0000623415],"domain_scores_gemma":[0.9997841,0.0001130495,0.0000465873,0.00001072051,0.00003143722,0.00001414653],"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":[0.00003349606,0.000009776529,0.0001370353,0.00003431759,0.00000367711,0.00002345326,0.00003225863,0.0009364624,0.9975962,0.0002092037,0.00001881828,0.0009652282],"study_design_scores_gemma":[0.000007858867,0.0001360853,0.000716972,0.000004115457,0.000006384943,0.00004431383,0.00002416225,0.01578764,0.9825999,0.0001187915,0.000541952,0.0000116791],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9708977,0.0008471062,0.02728567,0.00007477172,0.00003286686,0.0000331242,0.00009399803,0.0001565386,0.0005780983],"genre_scores_gemma":[0.9874883,0.0008546873,0.0103632,0.00007897343,0.00001999333,0.00004714509,0.0001507722,0.00001739174,0.000979559],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001473206,"threshold_uncertainty_score":0.00332576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009763292069872656,"score_gpt":0.1916683373878698,"score_spread":0.1819050453179972,"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."}}