{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009247438,0.0002039212,0.0002635382,0.00003257373,0.00009906792,0.00004871959,0.0002820012,0.0001811487,0.007296791],"category_scores_gemma":[0.0006630644,0.0001532287,0.0002282143,0.0003225409,0.00007645049,0.00006168052,0.00004889357,0.0001088628,0.00001271772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001368758,"about_ca_system_score_gemma":0.00004838197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002442664,"about_ca_topic_score_gemma":0.000001572277,"domain_scores_codex":[0.9986273,0.000005379324,0.0003140192,0.0004738201,0.0002031568,0.0003763116],"domain_scores_gemma":[0.9986782,0.0003492031,0.0001025514,0.0005438036,0.0001667394,0.0001594978],"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":[0.00002134819,0.00007963131,0.0005992657,0.0000732409,0.00008407314,0.000001032821,0.000001552926,8.339971e-7,0.9945956,0.003399227,0.0003974394,0.0007467039],"study_design_scores_gemma":[0.0003221553,0.00001355605,0.00003030765,0.00002690257,0.0001111341,0.000004126993,0.000004497875,0.001616012,0.9799805,0.003572123,0.01408576,0.000232908],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06222603,0.0001261399,0.7677668,0.00259228,0.00001644061,0.0002531995,0.0004619071,0.000948332,0.1656089],"genre_scores_gemma":[0.987705,0.00005002096,0.005742684,0.00006275462,0.0001605851,0.0001385768,0.0001762169,0.00003757322,0.005926608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9254789,"threshold_uncertainty_score":0.9936107,"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."}}