{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001678569,0.0002854262,0.0001696231,0.0001860566,0.0001445816,0.0002450788,0.0002007228,0.0004365778,0.0004731017],"category_scores_gemma":[0.0002380771,0.000157396,0.000193945,0.0001123961,0.0001966392,0.0005990426,0.0002696074,0.000355834,0.0002817035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001821687,"about_ca_system_score_gemma":0.00009397898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001160791,"about_ca_topic_score_gemma":0.0003153296,"domain_scores_codex":[0.9999211,0.00001218905,0.000007120258,0.00002077912,0.00002262956,0.00001610165],"domain_scores_gemma":[0.9998664,0.00003216212,0.00003952317,0.000015269,0.00001770864,0.00002884391],"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":[0.00001482641,0.000004304018,0.00002279069,0.00002378867,0.000002164497,0.00002336599,0.000006804042,0.00005637315,0.9989356,0.00008396636,0.00001417167,0.0008117416],"study_design_scores_gemma":[0.000005620871,0.00003530459,0.0002883095,0.000001854765,0.000003905103,0.0001007058,0.000004266117,0.0005901416,0.9980543,0.00003287196,0.0008789332,0.000003883584],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9667955,0.001818791,0.02809839,0.0002304801,0.0000522169,0.00008783233,0.0002405228,0.0002376114,0.002438563],"genre_scores_gemma":[0.9756281,0.001321849,0.02103138,0.00007590854,0.00002608689,0.0000716717,0.0001611971,0.00005419457,0.001629636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004731017,"threshold_uncertainty_score":0.001582742,"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."}}