{"id":"W4412884634","doi":"10.1021/acs.nanolett.5c02232","title":"Multiparametric Characterization of Individual Suspended Nanoparticles Using Confocal Fluorescence and Interferometric Scattering Microscopy with Microfluidic Confinement","year":2025,"lang":"en","type":"article","venue":"Nano Letters","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Canadian Institutes of Health Research; NanoMedicines Innovation Network; Mitacs; Vetenskapsrådet; Killam Trusts","keywords":"Characterization (materials science); Confocal microscopy; Microfluidics; Materials science; Microscopy; Interferometry; Fluorescence; Confocal; Fluorescence microscope; Nanotechnology; Nanoparticle; Scattering; Optics; Physics","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":[0.000735151,0.0004321381,0.0002893923,0.0004977372,0.0003266319,0.0004273948,0.0005639077,0.0004017934,0.0003391091],"category_scores_gemma":[0.0008084929,0.0001863636,0.0002708417,0.000240309,0.0005724879,0.0004620173,0.0004687817,0.0004186452,0.0001951068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009324881,"about_ca_system_score_gemma":0.0008220609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002490823,"about_ca_topic_score_gemma":0.00396489,"domain_scores_codex":[0.9995253,0.00005506211,0.00003214288,0.000134881,0.0001979483,0.00005466931],"domain_scores_gemma":[0.9993279,0.0002445071,0.0001406837,0.00007556382,0.0001683776,0.00004308212],"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":[0.00001876071,0.00001180113,0.0003072188,0.00001720829,0.000003346096,0.00001605417,0.00003400328,0.0003218606,0.9973309,0.0001961094,0.00002098275,0.001721675],"study_design_scores_gemma":[0.000004459596,0.00005504569,0.001068468,0.000002482906,0.000005342724,0.00005407861,0.00002148765,0.01292788,0.9853338,0.0001092027,0.0004048814,0.00001293826],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7831606,0.0005985537,0.2138066,0.0001569046,0.00002495828,0.0001559177,0.0003078899,0.0004202377,0.001368349],"genre_scores_gemma":[0.8402221,0.0004411198,0.1574513,0.0001009497,0.00001761802,0.0002751768,0.0002562971,0.00007414353,0.001161256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002490823,"threshold_uncertainty_score":0.006765664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01197793640119927,"score_gpt":0.2226444704806378,"score_spread":0.2106665340794385,"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."}}