{"id":"W3092432725","doi":"10.1039/d0cc05478j","title":"Dynamically tuning transient silicone polymer networks with hydrogen bonding","year":2020,"lang":"en","type":"article","venue":"Chemical Communications","topic":"Polymer composites and self-healing","field":"Materials Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Austrian Science Fund","keywords":"Supramolecular chemistry; Hydrogen bond; Supramolecular polymers; Polymer; Transient (computer programming); Covalent bond; Viscoelasticity; Materials science; Polymer chemistry; Chain (unit); Silicone; Polymer network; Polymer science; Chemical engineering; Chemical physics; Chemistry; Molecule; Organic chemistry; Composite material; Computer science","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":[0.00009907903,0.0001551528,0.0002199526,0.00002254773,0.0002812758,0.00007491583,0.001080011,0.00007478878,0.00006284485],"category_scores_gemma":[0.00002134695,0.0001385266,0.00006648193,0.0002854301,0.0002202586,0.000116528,0.0003269946,0.0003074304,0.00002777653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004096624,"about_ca_system_score_gemma":0.00003754348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004093302,"about_ca_topic_score_gemma":0.000006354488,"domain_scores_codex":[0.9988577,0.00007308559,0.000295945,0.0002718339,0.0001676023,0.0003338377],"domain_scores_gemma":[0.9985829,0.0001804722,0.00008431386,0.0008350466,0.00003936881,0.0002778795],"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":[0.00002818351,0.00005174611,0.00008584736,0.000008850686,0.00001307364,0.000001376157,0.0005166567,0.0003757994,0.997403,0.001152671,0.00004948301,0.0003133366],"study_design_scores_gemma":[0.0003726754,0.0000561558,0.00003091756,0.00006725018,0.00006804372,0.00001672723,0.0001467796,0.2447808,0.75211,0.00003464035,0.00197947,0.000336555],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9545062,0.003545382,0.02528614,0.01312383,0.00002836133,0.0001856902,0.00001767344,0.0003179596,0.002988839],"genre_scores_gemma":[0.9900225,0.00003468665,0.008336918,0.001397664,0.00005828547,0.00002637973,0.00006903494,0.00002706412,0.00002740633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.245293,"threshold_uncertainty_score":0.564895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02359054763483123,"score_gpt":0.2427442623945407,"score_spread":0.2191537147597095,"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."}}