{"id":"W4399913195","doi":"10.1016/j.compositesa.2024.108330","title":"Mussel-adhesive chemistry inspired conductive hydrogel with self-adhesion, biocompatibility, self-recovery and fatigue-resistance performances as flexible sensing electronics","year":2024,"lang":"en","type":"article","venue":"Composites Part A Applied Science and Manufacturing","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"National Natural Science Foundation of China","keywords":"Biocompatibility; Adhesive; Adhesion; Materials science; Electronics; Self adhesive; Electrical conductor; Electrically conductive; Composite material; Flexible electronics; Nanotechnology; Engineering; Electrical engineering; Metallurgy; Layer (electronics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002660753,0.0003254693,0.0002986853,0.0001144212,0.0004913411,0.0004302418,0.0001598492,0.0000696285,0.000005315602],"category_scores_gemma":[0.000009433157,0.0002794234,0.00002268138,0.0002822839,0.0002953196,0.0005957532,0.00009658375,0.0001994383,0.000006024618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001602858,"about_ca_system_score_gemma":0.00009485545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000916589,"about_ca_topic_score_gemma":0.000009569154,"domain_scores_codex":[0.998292,0.000008265191,0.0002516595,0.0006211334,0.0003045627,0.0005223457],"domain_scores_gemma":[0.999373,0.0001315378,0.00005057711,0.0002309251,0.00004807246,0.0001658792],"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":[0.00004676455,0.00001553092,0.00003370127,0.0007286092,0.00006391015,0.00001453939,0.0008809773,0.01372186,0.9829674,0.0003079044,0.00003051948,0.001188321],"study_design_scores_gemma":[0.0001973189,0.00004960059,0.0001446339,0.0001905704,0.00004283404,0.00005458331,0.0001742778,0.0123656,0.9824017,0.0008444841,0.003132507,0.0004018563],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928877,0.001044326,0.0005273438,0.00002027127,0.0001136068,0.0002317632,0.000004697682,0.0009323582,0.004237934],"genre_scores_gemma":[0.993881,0.0003000771,0.005593546,0.00004485088,0.00006552608,0.00001815191,0.000007182947,0.00003542094,0.00005427004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005066202,"threshold_uncertainty_score":0.9999658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00916055496788475,"score_gpt":0.2167041159497182,"score_spread":0.2075435609818334,"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."}}