{"id":"W3019646180","doi":"10.1039/d0cc01934h","title":"Smart azobenzene-containing tubular polymersomes: fabrication and multiple morphological tuning","year":2020,"lang":"en","type":"article","venue":"Chemical Communications","topic":"Photochromic and Fluorescence Chemistry","field":"Materials Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Institute for Nanotechnology; University of Waterloo","funders":"Collaborative Innovation Center of Suzhou Nano Science and Technology; Priority Academic Program Development of Jiangsu Higher Education Institutions; Soochow University; National Natural Science Foundation of China","keywords":"Polymersome; Azobenzene; Fabrication; Materials science; Nanotechnology; Smart material; Chemical engineering; Copolymer; Polymer; Amphiphile; Composite material; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.000195154,0.0001361842,0.0001928263,0.00001497418,0.0002200071,0.00005977656,0.0008468087,0.0001115257,0.0001184611],"category_scores_gemma":[0.0006485622,0.000132309,0.00004470147,0.0001433466,0.0003593913,0.0001371261,0.0006335274,0.0002676185,0.00007032073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004078853,"about_ca_system_score_gemma":0.00003430087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000330029,"about_ca_topic_score_gemma":4.806658e-7,"domain_scores_codex":[0.9989983,0.00008143505,0.0002601933,0.0003107372,0.0001242989,0.0002250127],"domain_scores_gemma":[0.9986456,0.0002700888,0.00009050663,0.00071226,0.00005734584,0.0002242034],"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":[0.0000106786,0.00003317129,0.002549502,0.00001244267,0.000004057534,0.000001621353,0.000517613,7.33356e-7,0.996226,0.00009958268,0.0001674634,0.0003771096],"study_design_scores_gemma":[0.0003279019,0.00001963091,0.0005473406,0.00002309514,0.00001623851,0.0000154838,0.0005170879,0.006257839,0.9912254,0.0001307895,0.0007408129,0.0001784323],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927849,0.0007713047,0.002190359,0.002597174,0.00002068361,0.0001424321,0.00001977892,0.0001769499,0.001296466],"genre_scores_gemma":[0.9906892,0.00007638209,0.008264596,0.0007774866,0.00005496759,0.0000333076,0.00008057025,0.00001360441,0.000009853443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006257106,"threshold_uncertainty_score":0.5395406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05150154387020243,"score_gpt":0.2637899465364433,"score_spread":0.2122884026662409,"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."}}