{"id":"W2934117711","doi":"10.1016/j.aca.2019.03.049","title":"Magnetic nanoparticles speed up mechanochemical solid phase extraction with enhanced enrichment capability for organochlorines in plants","year":2019,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Chemistry; Solid phase extraction; Sorbent; Chromatography; Extraction (chemistry); Analyte; Sample preparation; Magnetic nanoparticles; Detection limit; Gas chromatography; Mass spectrometry; Solid-phase microextraction; Pesticide; Clean-up; Nanoparticle; Gas chromatography–mass spectrometry; Nanotechnology; Adsorption; Materials science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001069173,0.0002436983,0.0001444692,0.0001815647,0.0001484852,0.0001799805,0.0001796401,0.0002831651,0.0009760523],"category_scores_gemma":[0.0001465574,0.0001756161,0.0001930765,0.0001177637,0.0001435496,0.0002369326,0.0002626635,0.0003576025,0.0004385785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002458556,"about_ca_system_score_gemma":0.0001766136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007708472,"about_ca_topic_score_gemma":0.002008043,"domain_scores_codex":[0.9999107,0.000009659516,0.000004424076,0.00002221954,0.0000333159,0.00001973582],"domain_scores_gemma":[0.9999416,0.00001639633,0.00001216461,0.000005047033,0.0000184471,0.000006382007],"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":[0.00002707047,0.000007232859,0.00004765309,0.00002820301,0.00000251982,0.00001301453,0.000007964632,0.00007645257,0.9972,0.0000569775,0.00007915591,0.002453701],"study_design_scores_gemma":[0.000004448988,0.00003784689,0.0004118427,0.000001773668,0.000005777385,0.00003264688,0.000007257931,0.001081297,0.9964628,0.0000242429,0.001926631,0.000003593314],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9653918,0.002072214,0.02590131,0.0003595163,0.00009654874,0.00006436514,0.000201025,0.0002905217,0.005622695],"genre_scores_gemma":[0.9849221,0.0006371441,0.008264034,0.0001076146,0.00002346294,0.00002959947,0.0001192106,0.00003616941,0.005860748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009760523,"threshold_uncertainty_score":0.003265262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.019692266516225,"score_gpt":0.3167894450381998,"score_spread":0.2970971785219748,"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."}}