{"id":"W1972328405","doi":"10.1007/s10856-006-0088-8","title":"Hydrogel–elastomer composite biomaterials: 1. Preparation of interpenetrating polymer networks and in vitro characterization of swelling stability and mechanical properties","year":2007,"lang":"en","type":"article","venue":"Journal of Materials Science Materials in Medicine","topic":"Hydrogels: synthesis, properties, applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Swelling; Gelatin; Materials science; Self-healing hydrogels; Differential scanning calorimetry; Biomaterial; Polymer; Elastomer; Fourier transform infrared spectroscopy; Gel permeation chromatography; Interpenetrating polymer network; Chemical engineering; Polymer chemistry; Composite material; Chemistry; Nanotechnology; Organic chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006153948,0.0001458969,0.0005480953,0.0002542642,0.00004412395,0.00003315565,0.0002065923,0.0001104132,0.00001980879],"category_scores_gemma":[0.0002912851,0.0001081184,0.00001580844,0.0001932252,0.0006487684,0.000053697,0.0001216895,0.00003643863,1.397494e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003001315,"about_ca_system_score_gemma":0.0000677966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007864126,"about_ca_topic_score_gemma":0.000009263667,"domain_scores_codex":[0.9975983,0.0001770532,0.001499029,0.0002473514,0.0002621822,0.0002161006],"domain_scores_gemma":[0.998695,0.00002954479,0.0008669037,0.0001771862,0.0001720622,0.00005928401],"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":[0.001442519,0.00005899092,0.0003780185,0.0001163449,0.000008671174,0.00000110072,0.000337802,0.00001333604,0.9974131,0.00001077657,1.622261e-7,0.000219151],"study_design_scores_gemma":[0.000457962,0.0002871359,0.002940566,0.0003757072,0.00001688262,0.00004355992,0.0002121593,0.0000880962,0.9954598,0.00002265132,0.000002440713,0.00009305581],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981107,0.0003281676,0.0009076815,0.00006398098,0.0003036079,0.0002679225,0.00001106265,0.000002630591,0.000004287786],"genre_scores_gemma":[0.999038,0.000236714,0.0005076876,0.00002625177,0.0001618256,0.000006938929,0.00000978344,0.00001175315,9.855052e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005862663,"threshold_uncertainty_score":0.4408939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01986239331620896,"score_gpt":0.2788771209593531,"score_spread":0.2590147276431441,"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."}}