{"id":"W2900180286","doi":"10.1002/pen.24981","title":"Mass‐produced graphene—HDPE nanocomposites: Thermal, rheological, electrical, and mechanical properties","year":2018,"lang":"en","type":"article","venue":"Polymer Engineering and Science","topic":"Polymer Nanocomposites and Properties","field":"Materials Science","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"NanoXplore (Canada); École de Technologie Supérieure; McGill University","funders":"Mitacs","keywords":"Materials science; Graphene; High-density polyethylene; Nanocomposite; Composite material; Flexural strength; Compounding; Differential scanning calorimetry; Flexural modulus; Ultimate tensile strength; Rheology; Dispersion (optics); Dynamic mechanical analysis; Scanning electron microscope; Izod impact strength test; Polymer; Polyethylene; Nanotechnology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005107695,0.0002397456,0.0002486581,0.0001650004,0.0005691204,0.0003667237,0.0004211336,0.00006958451,0.00005510058],"category_scores_gemma":[0.00007691624,0.0001633169,0.00003266924,0.0004982314,0.001110655,0.0003895691,0.0002131564,0.0001213531,0.00002877089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001753554,"about_ca_system_score_gemma":0.00005646796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007120623,"about_ca_topic_score_gemma":7.445664e-7,"domain_scores_codex":[0.9980978,0.00003579223,0.0002427063,0.0006379061,0.0003316044,0.0006541288],"domain_scores_gemma":[0.9992878,0.0000481004,0.00004900321,0.0002892777,0.00007267701,0.0002530996],"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":[0.00002795273,0.00001971765,0.0002044568,0.00001377552,0.000004237981,0.00000192625,0.0002243288,0.000007023914,0.9961002,0.001931597,0.00001442015,0.001450396],"study_design_scores_gemma":[0.0001577469,0.0003236497,0.0005038399,0.00004012061,0.00001311457,0.00004236373,0.00001497925,0.008617104,0.9897273,0.00006048687,0.0002175238,0.0002818039],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910426,0.007417834,0.0003109457,0.0002904869,0.0003646655,0.0001392374,0.000002495081,0.0002083052,0.0002234539],"genre_scores_gemma":[0.9975368,0.00008955895,0.001821407,0.0001894512,0.0001703171,0.00002270899,3.055465e-7,0.00001643596,0.0001530605],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00861008,"threshold_uncertainty_score":0.6659872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01393243183147517,"score_gpt":0.207360752542241,"score_spread":0.1934283207107658,"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."}}