{"id":"W2998448269","doi":"10.1016/j.cej.2019.123983","title":"Polymer-assisted in-situ thermal reduction of silver precursors: A solventless route for silver nanoparticles-polymer composites","year":2019,"lang":"en","type":"article","venue":"Chemical Engineering Journal","topic":"Nanoparticles: synthesis and applications","field":"Materials Science","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Innosuisse - Schweizerische Agentur für Innovationsförderung; EMS Ingénierie","keywords":"Silver nanoparticle; Polypropylene; Materials science; Polylactic acid; Polymer; Composite material; Polyamide; Extrusion; Nanocomposite; Wetting; Composite number; Nanoparticle; Chemical engineering; Nanotechnology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002357336,0.0001427483,0.0002607238,0.00007092574,0.00003823116,0.00005407248,0.0002187478,0.00007580678,0.0001297831],"category_scores_gemma":[0.00002779114,0.0001268213,0.0001229422,0.0001628833,0.0000418495,0.0001904732,0.00003783026,0.000132347,0.00002357127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006844622,"about_ca_system_score_gemma":0.00003492416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005589609,"about_ca_topic_score_gemma":2.181739e-7,"domain_scores_codex":[0.9988282,0.0000177341,0.0004331159,0.0002015561,0.0001909339,0.0003284347],"domain_scores_gemma":[0.9993986,0.0001003437,0.0001460342,0.0001851391,0.00005701824,0.0001127889],"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":[0.00006597606,0.0001668214,0.0002353452,0.0000422162,0.00001433798,6.78555e-7,0.0001583389,0.001148865,0.9971832,0.00030076,0.00003286362,0.0006505736],"study_design_scores_gemma":[0.0006300142,0.00003600401,0.000846578,0.0001401317,0.00002646978,0.00003891063,0.00003365159,0.002028256,0.9959867,0.00002584572,0.0000537978,0.0001536558],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981455,0.0004566783,0.0007517945,0.0001579347,0.0002171322,0.0001878019,0.000009718672,0.00003387133,0.00003952553],"genre_scores_gemma":[0.9966052,0.000003440267,0.003124669,0.00001016109,0.0001372228,0.00003350138,0.00000290272,0.00002454799,0.00005839985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002372875,"threshold_uncertainty_score":0.5171623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01040856122269356,"score_gpt":0.223009985958629,"score_spread":0.2126014247359354,"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."}}