{"id":"W1970548287","doi":"10.1016/j.chroma.2011.04.027","title":"On-chip solid phase extraction and enzyme digestion using cationic PolyE-323 coatings and porous polymer monoliths coupled to electrospray mass spectrometry","year":2011,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; National Institute for Nanotechnology; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Genome Prairie; University of Alberta; Genome Canada","keywords":"Monolith; Chemistry; Chromatography; Solid phase extraction; Electrospray; Polymer; Monolithic HPLC column; Extraction (chemistry); Mass spectrometry; Cationic polymerization; Methacrylate; Coating; High-performance liquid chromatography; Monomer; Polymer chemistry; 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":[0.0001779338,0.0001916465,0.0002693204,0.0005429192,0.0001339834,0.00005610371,0.00009008822,0.00009053612,0.00004061076],"category_scores_gemma":[0.00001383905,0.0001870595,0.00007894453,0.0004943806,0.00004221731,0.000201772,0.000007972101,0.0002481705,0.000002095751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007226742,"about_ca_system_score_gemma":0.00003936514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003403189,"about_ca_topic_score_gemma":0.000001561767,"domain_scores_codex":[0.9989234,0.00002524984,0.0004275525,0.0001576776,0.0001904106,0.0002757529],"domain_scores_gemma":[0.9993352,0.00004285798,0.0002055946,0.0001468562,0.0000741043,0.0001954112],"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":[0.00008056279,0.00008676931,0.0004258803,0.00002087384,0.00009259588,0.000008357628,0.0002859684,0.000009351111,0.9972686,0.0007468698,0.000558979,0.0004151637],"study_design_scores_gemma":[0.0010934,0.001018314,0.006141955,0.00008915643,0.0001580427,0.000966403,0.000116929,0.001038242,0.987484,0.001444962,0.0001372452,0.0003114018],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9336379,0.01622376,0.04967424,0.00004499839,0.00006924575,0.0001550035,0.000004918697,0.00004727598,0.0001426592],"genre_scores_gemma":[0.990987,0.007451561,0.001361261,0.00005492189,0.00009384989,0.000007565071,0.000002416839,0.00003578312,0.000005649086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0573491,"threshold_uncertainty_score":0.7628065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01038362975663793,"score_gpt":0.2516950572493188,"score_spread":0.2413114274926809,"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."}}