{"id":"W2034670254","doi":"10.1016/j.eurpolymj.2014.12.004","title":"Synthesis of styrene–butadiene copolymer nanoparticles via semi-batch differential microemulsion polymerization","year":2014,"lang":"en","type":"article","venue":"European Polymer Journal","topic":"Advanced Polymer Synthesis and Characterization","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Foundation for Innovation","keywords":"Microemulsion; Pulmonary surfactant; Copolymer; Monomer; Polymerization; Chemical engineering; Styrene-butadiene; Particle size; Nanoparticle; Styrene; Polymer; Polymer chemistry; Materials science; Emulsion polymerization; Particle (ecology); Chemistry; Composite material","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002521405,0.00039015,0.0004783179,0.0001672553,0.0003516034,0.0001114628,0.0004636981,0.00008236738,0.002564703],"category_scores_gemma":[0.0001112079,0.0003525999,0.0002445519,0.0001931753,0.0001642392,0.0003315339,0.0001223977,0.000295327,0.00007756814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003922237,"about_ca_system_score_gemma":0.00003082361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001226391,"about_ca_topic_score_gemma":0.000001221173,"domain_scores_codex":[0.9972661,0.0003698335,0.0009711151,0.0003935028,0.0004654457,0.0005340251],"domain_scores_gemma":[0.9981,0.0001721899,0.0008570183,0.0004698524,0.00009992086,0.0003009992],"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":[0.0001098512,0.0001641761,0.001168626,0.00004121759,0.00007318669,0.00001004945,0.000226529,0.000007181252,0.8624704,0.00009678287,0.00005542655,0.1355766],"study_design_scores_gemma":[0.0006128098,0.00005052646,0.001428621,0.0002210977,0.0001390053,0.0001644011,0.00008308639,0.0004265045,0.9955406,0.00003153093,0.0009198694,0.0003820003],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.969732,0.001304541,0.02079279,0.0001744512,0.0004764923,0.00004186074,0.00004474507,0.00009739693,0.007335698],"genre_scores_gemma":[0.9973063,0.0001558361,0.0001405129,0.0001066441,0.0009532444,0.000004053924,0.0000347757,0.0001328102,0.001165815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1351946,"threshold_uncertainty_score":0.9998926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00544608619100129,"score_gpt":0.1984578845484405,"score_spread":0.1930117983574392,"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."}}