{"id":"W4404186501","doi":"10.1016/j.polymer.2024.127809","title":"A novel melt extrusion method for efficient and large-scale in-situ exfoliation of boron nitride to prepare high performance thermal conductive polymer composite","year":2024,"lang":"en","type":"article","venue":"Polymer","topic":"Thermal properties of materials","field":"Materials Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Boron nitride; Exfoliation joint; Materials science; Extrusion; Composite number; In situ; Composite material; Electrical conductor; Thermal; Polymer; Boron; Nanotechnology; Chemistry; Graphene","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":[0.0002088325,0.0005033603,0.0004307357,0.000345971,0.0003146602,0.0002133875,0.0003440683,0.0003624181,0.001044412],"category_scores_gemma":[0.0001721077,0.0003148219,0.0002889065,0.0002190775,0.000163013,0.0006516513,0.0004184497,0.0006168678,0.0004926592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002079007,"about_ca_system_score_gemma":0.0001985243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001897368,"about_ca_topic_score_gemma":0.0005768121,"domain_scores_codex":[0.9998268,0.00001626128,0.00002139137,0.00004562495,0.00006951761,0.00002046634],"domain_scores_gemma":[0.9998989,0.00001795957,0.00004056383,0.00001499407,0.00001606127,0.0000114606],"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":[0.00002550112,0.00001482416,0.00005884579,0.00009442559,0.00000517392,0.00006466435,0.00002897324,0.00006909298,0.9954849,0.0002093278,0.0001029146,0.003841321],"study_design_scores_gemma":[0.000008056693,0.00004721186,0.0003224127,0.000003682072,0.000008336782,0.0001824134,0.000008629265,0.001236188,0.9956309,0.00003912138,0.002504328,0.000008788148],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7861937,0.004418443,0.1994748,0.0004637324,0.0003979496,0.0001882679,0.0005745336,0.001316283,0.006972221],"genre_scores_gemma":[0.9200286,0.001629965,0.07346141,0.00007968031,0.00007873977,0.0001064397,0.0003507997,0.0001678616,0.00409647],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001044412,"threshold_uncertainty_score":0.003493965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01664301865862781,"score_gpt":0.2773940943321902,"score_spread":0.2607510756735624,"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."}}