{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001413113,0.0004262962,0.0001448954,0.0004829271,0.0001136461,0.0001762342,0.0001509248,0.0002811599,0.0007724995],"category_scores_gemma":[0.000159119,0.000148464,0.0001682347,0.000294739,0.0001330327,0.0002595517,0.000140822,0.0003269322,0.0001868679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001714669,"about_ca_system_score_gemma":0.00007231562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001864164,"about_ca_topic_score_gemma":0.0006732653,"domain_scores_codex":[0.9999177,0.000007533071,0.000005983763,0.00001877774,0.00003823184,0.00001165668],"domain_scores_gemma":[0.9999194,0.00001618015,0.00002641023,0.000008130165,0.00001437948,0.00001539179],"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":[0.00002731964,0.00001493975,0.0000768055,0.00002408617,0.000002739654,0.00002347185,0.000006451873,0.000106738,0.9985356,0.00002983984,0.00001180092,0.00114012],"study_design_scores_gemma":[0.000003251306,0.00007431842,0.001166841,0.000001906493,0.000006942956,0.00004800797,0.000005015505,0.0004488752,0.997689,0.00001443619,0.0005393683,0.00000218634],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925453,0.0006418031,0.004823447,0.00003595302,0.00001447773,0.00003533949,0.0003310636,0.00008272945,0.001489948],"genre_scores_gemma":[0.9920278,0.0002847729,0.006106852,0.00001268857,0.000004581786,0.00001792718,0.000219111,0.00002600039,0.001300259],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007724995,"threshold_uncertainty_score":0.002584279,"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."}}