{"id":"W2903726143","doi":"10.1021/acs.macromol.8b02227","title":"Uniform Toroidal Micelles via the Solution Self-Assembly of Block Copolymer–Homopolymer Blends Using a “Frustrated Crystallization” Approach","year":2018,"lang":"en","type":"article","venue":"Macromolecules","topic":"Advanced Polymer Synthesis and Characterization","field":"Chemistry","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Engineering and Physical Sciences Research Council; Science and Technology Commission of Shanghai Municipality; University of Bristol; National Natural Science Foundation of China; Government of Canada","keywords":"Micelle; Copolymer; Toroid; Materials science; Crystallization; Polymerization; Amorphous solid; Amphiphile; Self-assembly; Nanostructure; Degree of polymerization; Chemical engineering; Template; Nanoparticle; Polymer chemistry; Nanotechnology; Chemical physics; Composite material; Chemistry; Polymer; Crystallography; Physics; Organic chemistry","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":[0.0002396501,0.0004536382,0.0003098628,0.0002930014,0.0002183014,0.0004287027,0.0002765262,0.0003142776,0.0004805762],"category_scores_gemma":[0.0002142533,0.0003200626,0.0002229801,0.000168164,0.0002849526,0.0004659882,0.0002958178,0.0004780146,0.0003790831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000435385,"about_ca_system_score_gemma":0.0003247332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005751638,"about_ca_topic_score_gemma":0.001752594,"domain_scores_codex":[0.9998191,0.00003304566,0.00001701485,0.00004745773,0.0000433915,0.00003997798],"domain_scores_gemma":[0.9998401,0.00002385742,0.00007058125,0.00001848749,0.00002192812,0.000024962],"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":[0.00002379073,0.00001369247,0.00003546509,0.00002793833,0.000004582886,0.00003648644,0.00002870925,0.0002087108,0.9977946,0.0003181216,0.00003926743,0.001468574],"study_design_scores_gemma":[0.00001225301,0.00007315265,0.0001135004,0.000001839722,0.000004786945,0.0000547168,0.000005745123,0.002476846,0.9960467,0.00002477713,0.001180457,0.000005199922],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9635773,0.001758151,0.03075662,0.0001968318,0.00004351598,0.0001542221,0.0001432755,0.0004727471,0.002897292],"genre_scores_gemma":[0.9741984,0.0009022238,0.02293084,0.00005390295,0.00001782576,0.00007152512,0.0001290674,0.00006788933,0.001628392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005751638,"threshold_uncertainty_score":0.003158927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01167163660731343,"score_gpt":0.2336696639079608,"score_spread":0.2219980273006473,"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."}}