{"id":"W3130366966","doi":"10.1021/acsnano.0c10069","title":"Density Matching Multi-wavelength Analytical Ultracentrifugation to Measure Drug Loading of Lipid Nanoparticle Formulations","year":2021,"lang":"en","type":"article","venue":"ACS Nano","topic":"Field-Flow Fractionation Techniques","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Lethbridge","funders":"Canadian Institutes of Health Research; Canada Foundation for Innovation; National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Measure (data warehouse); Analytical Ultracentrifugation; Nanoparticle; Materials science; Drug; Nanotechnology; Ultracentrifuge; Wavelength; Chromatography; Chemistry; Optoelectronics; Computer science; Data mining; Pharmacology; Medicine","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":[0.001164423,0.0007232736,0.0004623186,0.001579291,0.0004698443,0.0004705238,0.0005948155,0.0004919652,0.0007244854],"category_scores_gemma":[0.001375406,0.0004012641,0.0002511729,0.0008633277,0.0003151144,0.0004962518,0.0004775374,0.0009429237,0.000371583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009006268,"about_ca_system_score_gemma":0.0005336626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002029159,"about_ca_topic_score_gemma":0.002748376,"domain_scores_codex":[0.9994115,0.0001129257,0.00004573474,0.0001341041,0.0002421766,0.00005354904],"domain_scores_gemma":[0.9993138,0.0002407989,0.0001096741,0.00007904166,0.0002084235,0.00004819175],"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":[0.00005648148,0.00006267409,0.0008722398,0.00006928893,0.00001577953,0.00002251923,0.00009618788,0.0009755459,0.9848518,0.0005815506,0.0001767655,0.01221923],"study_design_scores_gemma":[0.00000500321,0.00005160431,0.001947484,0.000006802957,0.00001430912,0.00003526785,0.00001499401,0.02109267,0.9757366,0.000112162,0.0009706846,0.00001249679],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4546632,0.001745023,0.5372934,0.0001275208,0.00006923818,0.0004609665,0.0006881146,0.002370357,0.002582267],"genre_scores_gemma":[0.579474,0.001481855,0.4142712,0.0001436514,0.00002028974,0.0009156559,0.0006014331,0.0004201432,0.002671654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002029159,"threshold_uncertainty_score":0.006534517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01875734757521298,"score_gpt":0.2510245281291292,"score_spread":0.2322671805539163,"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."}}