{"id":"W2015980588","doi":"10.1021/ma020053w","title":"Phase Segregation in SAN/PMMA Blends Probed by Rheology, Microscopy, and Inverse Gas Chromatography Techniques","year":2002,"lang":"en","type":"article","venue":"Macromolecules","topic":"Polymer crystallization and properties","field":"Materials Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Inverse gas chromatography; Viscoelasticity; Lower critical solution temperature; Rheology; Materials science; Dynamic mechanical analysis; Optical microscope; Polymer chemistry; Methyl methacrylate; Poly(methyl methacrylate); Time–temperature superposition; Phase (matter); Polymer blend; Microscopy; Relaxation (psychology); Polymer; Thermodynamics; Composite material; Copolymer; Scanning electron microscope; Chemistry; Optics; Organic chemistry","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":[0.0001225907,0.0002075203,0.0001289579,0.0003781442,0.0001463742,0.000175699,0.00008870498,0.0001300074,0.0004728617],"category_scores_gemma":[0.0002276983,0.0001365506,0.00008256327,0.0001993171,0.0002083578,0.0002525527,0.0001051425,0.0002883759,0.0001030209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001939017,"about_ca_system_score_gemma":0.00008563199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004645459,"about_ca_topic_score_gemma":0.0005421193,"domain_scores_codex":[0.9999321,0.000008074756,0.00000426175,0.00001675957,0.00002534188,0.00001338799],"domain_scores_gemma":[0.9998815,0.00002970275,0.00004801804,0.000007532324,0.00001768504,0.00001557572],"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":[0.00002025551,0.000004861156,0.0001703012,0.000004899334,6.532975e-7,0.000008727364,0.000009462827,0.0000305234,0.9993343,0.00002184032,0.00000293561,0.0003912988],"study_design_scores_gemma":[0.000005493366,0.00005087183,0.002787835,0.000002318439,0.000005371463,0.00004851808,0.00001290644,0.001420932,0.995425,0.00003007851,0.0002084832,0.000002217856],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980101,0.0002548386,0.001361136,0.00001473805,0.000002876044,0.000005962354,0.00004212851,0.00002208105,0.0002861402],"genre_scores_gemma":[0.9968829,0.0002564809,0.002148363,0.00001077774,0.000003536727,0.00001368857,0.00008469621,0.00001594904,0.0005835921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004728617,"threshold_uncertainty_score":0.001581907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01244912897291891,"score_gpt":0.2473381258620019,"score_spread":0.234888996889083,"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."}}