{"id":"W2562521469","doi":"10.1021/acs.analchem.6b02571","title":"Molecularly Imprinted Membrane Electrospray Ionization for Direct Sample Analyses","year":2016,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China; Beijing Institute of Technology; Natural Science Foundation of Beijing Municipality; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Chemistry; Electrospray ionization; Chromatography; Analyte; Molecularly imprinted polymer; Detection limit; Mass spectrometry; Electrospray; Molecular imprinting; Membrane; Sample preparation; Analytical Chemistry (journal); Ambient ionization; Ionization; Chemical ionization; Selectivity; Organic chemistry; Ion","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001570871,0.001283736,0.0007497897,0.00105647,0.0002812771,0.0005170819,0.0008901402,0.000850336,0.006130054],"category_scores_gemma":[0.001165993,0.0005700241,0.0004621456,0.0007886987,0.0003866217,0.0008849023,0.000632752,0.001249207,0.004514717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003265354,"about_ca_system_score_gemma":0.0004566521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001840718,"about_ca_topic_score_gemma":0.0004440772,"domain_scores_codex":[0.9983658,0.0003664271,0.00009742931,0.0003297214,0.0007699942,0.00007051105],"domain_scores_gemma":[0.9995117,0.0002161957,0.00006759551,0.00004925296,0.0001289471,0.00002635303],"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":[0.00008633143,0.0000504091,0.0002467325,0.0004296505,0.00005101425,0.0001508171,0.00002732412,0.0002234921,0.9434796,0.0006387053,0.001275501,0.05334037],"study_design_scores_gemma":[0.00002789832,0.0002595653,0.00120046,0.00004492448,0.00005176272,0.0008802042,0.00001608586,0.005920638,0.9553986,0.0005666403,0.03560076,0.00003251088],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08096918,0.04532307,0.8385004,0.001091978,0.001183266,0.0007800607,0.001979277,0.005777291,0.02439542],"genre_scores_gemma":[0.2977297,0.03140029,0.6445472,0.001716237,0.0004938816,0.001302321,0.00234737,0.0004523894,0.02001071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006130054,"threshold_uncertainty_score":0.02050704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02360141490987817,"score_gpt":0.3180506497349971,"score_spread":0.2944492348251189,"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."}}