{"id":"W2954163238","doi":"10.1016/j.ces.2019.07.007","title":"Multi-dimensional analysis of micro-/nano-polymeric foams by confocal laser scanning microscopy and foam simulations","year":2019,"lang":"en","type":"article","venue":"Chemical Engineering Science","topic":"Polymer Foaming and Composites","field":"Materials Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"State Key Laboratory of Polymer Materials Engineering; National Natural Science Foundation of China; Foundation for Innovative Research Groups of the National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; Sichuan University; National Science Foundation","keywords":"Characterization (materials science); Materials science; Nano-; Supercritical fluid; Confocal laser scanning microscopy; Confocal; Foaming agent; Composite material; Nanotechnology; Chemistry; Biomedical engineering; Optics; Porosity","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":[0.0002020872,0.0003905955,0.000460326,0.0006943916,0.000547947,0.0005243202,0.0008224882,0.001177393,0.003117193],"category_scores_gemma":[0.0006873358,0.0004229764,0.0006734947,0.0004029334,0.0004752405,0.0005780556,0.0003590613,0.0004864005,0.0002550621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007223048,"about_ca_system_score_gemma":0.0009073049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008631112,"about_ca_topic_score_gemma":0.006074862,"domain_scores_codex":[0.9998977,0.00001242189,0.000006210936,0.00001696296,0.00003964669,0.00002703215],"domain_scores_gemma":[0.9996087,0.0001892006,0.00004115265,0.0000435552,0.00009006318,0.00002732605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009026285,0.0001188673,0.001967602,0.00008743988,0.00003605272,0.0001978153,0.00009305575,0.9445369,0.04427704,0.003005355,0.0003582608,0.005231323],"study_design_scores_gemma":[0.000004597797,0.000007302265,0.0003141214,0.000002374502,0.000002470762,0.00001165414,0.000008842097,0.9968787,0.002434472,0.0002197311,0.0001096657,0.000005991216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9158676,0.0002720619,0.074802,0.0002364049,0.00003546954,0.00007864114,0.001050535,0.0007345224,0.006922754],"genre_scores_gemma":[0.9766011,0.00008019729,0.02215477,0.00002268813,0.000008253153,0.00007074976,0.0002409159,0.00008204455,0.0007392994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008631112,"threshold_uncertainty_score":0.01716173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005084533844681742,"score_gpt":0.2325901916964074,"score_spread":0.2275056578517257,"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."}}