{"id":"W2110577786","doi":"10.1016/j.nano.2013.08.003","title":"Parallel microfluidic synthesis of size-tunable polymeric nanoparticles using 3D flow focusing towards in vivo study","year":2013,"lang":"en","type":"article","venue":"Nanomedicine Nanotechnology Biology and Medicine","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":173,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Research Foundation of Korea; National Research Foundation; National Cancer Institute; Prostate Cancer Foundation; National Institutes of Health; National Science Foundation","keywords":"Microfluidics; Reproducibility; Controllability; Nanotechnology; Materials science; Nanoparticle; Biodistribution; Robustness (evolution); In vivo; PLGA; Volumetric flow rate; Flow focusing; Flow chemistry; Biomedical engineering; Continuous flow; Chemistry; Chromatography; Mathematics; Biochemical engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000700501,0.0003536071,0.0009319261,0.0008456478,0.00008667796,0.000003522163,0.0002574782,0.0004991362,0.0002552974],"category_scores_gemma":[0.0004545665,0.00027988,0.00002837556,0.001420156,0.001238022,0.0001172313,0.000108254,0.0004000761,0.000003256099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001096971,"about_ca_system_score_gemma":0.00005632468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007104443,"about_ca_topic_score_gemma":0.000004023182,"domain_scores_codex":[0.9978867,0.00007370207,0.0009656138,0.0004050713,0.0001298978,0.0005390813],"domain_scores_gemma":[0.9990494,0.0002017295,0.000163598,0.0003798513,0.0001483595,0.00005711964],"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":[0.00002718325,0.00008967727,0.008261633,0.00007361241,0.00007358458,0.00001187275,0.0003474537,0.000003587089,0.9361062,0.000397706,0.0004258141,0.05418167],"study_design_scores_gemma":[0.00242815,0.0009760706,0.003798322,0.0003433338,0.000111884,0.0001062493,0.002159082,0.001067587,0.9857579,0.00186894,0.001014444,0.0003679935],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9737499,0.009294425,0.0142597,0.00103307,0.000304832,0.0007756747,0.000007654065,0.0002946961,0.0002800582],"genre_scores_gemma":[0.995779,0.0007604394,0.002974959,0.0001903223,0.0000967736,0.0001255098,0.000005599357,0.00003652523,0.00003088376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05381367,"threshold_uncertainty_score":0.9999653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01299339572157063,"score_gpt":0.260427650639082,"score_spread":0.2474342549175114,"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."}}