{"id":"W4403923059","doi":"10.1021/acsanm.4c05274","title":"Dispersive Liquid–Liquid Microextraction (DLLME) Coupled with Droplet Evaporation on an Omniphobic Nano/Micro Structured Porous Microfiber Membrane for Surface-Enhanced Raman Spectroscopy","year":2024,"lang":"en","type":"article","venue":"ACS Applied Nano Materials","topic":"Gold and Silver Nanoparticles Synthesis and Applications","field":"Materials Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Microfiber; Materials science; Evaporation; Raman spectroscopy; Membrane; Nano-; Porosity; Nanotechnology; Chemical engineering; Chemistry; Composite material; Optics","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":[0.0003282223,0.0005366399,0.0004526065,0.0003574844,0.0002221883,0.0002561976,0.0005348391,0.0005434544,0.0005163378],"category_scores_gemma":[0.0003923528,0.0003583948,0.0003512953,0.0002006658,0.0002991981,0.0004982055,0.0006105714,0.0005545317,0.0005787781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003948606,"about_ca_system_score_gemma":0.0002622158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004269787,"about_ca_topic_score_gemma":0.001176063,"domain_scores_codex":[0.9995691,0.00004604223,0.00002760229,0.0000971897,0.0002168989,0.00004317001],"domain_scores_gemma":[0.9997795,0.00009476343,0.00004166035,0.00002762105,0.0000431212,0.00001328104],"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":[0.000006112655,0.000004205303,0.00003398293,0.00002143003,0.00000209654,0.000020564,0.000004685372,0.0000399395,0.9979152,0.00005871057,0.00002066672,0.001872331],"study_design_scores_gemma":[0.000002905012,0.00002878393,0.0002259095,0.000001464969,0.000003503017,0.0001113391,0.00000460629,0.00139449,0.9971032,0.00003802324,0.001079643,0.00000626671],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5917623,0.003145565,0.3991516,0.0006109215,0.0002498204,0.0002876136,0.0004714548,0.001294444,0.003026366],"genre_scores_gemma":[0.6030502,0.001732769,0.3903935,0.0003269583,0.00007352474,0.000194952,0.0002761593,0.0001008622,0.003851061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005434544,"threshold_uncertainty_score":0.002864957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009702018306238585,"score_gpt":0.248107009279093,"score_spread":0.2384049909728544,"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."}}