{"id":"W3080858763","doi":"10.1021/acsmaterialslett.0c00309","title":"Functional Conductive Hydrogels for Bioelectronics","year":2020,"lang":"en","type":"article","venue":"ACS Materials Letters","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":350,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Research Foundation Singapore; Canada Research Chairs; Agency for Science, Technology and Research","keywords":"Bioelectronics; Self-healing hydrogels; Nanotechnology; Materials science; Biocompatibility; Electrical conductor; Conductive polymer; Wearable computer; Tissue engineering; Electrically conductive; Biomedical engineering; Computer science; Engineering; Biosensor; Polymer; Composite material; Embedded system; Polymer chemistry","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":[0.00006510825,0.0001726162,0.0002265055,0.0000249663,0.00005834774,0.00006018736,0.00009558039,0.00005491626,0.0001333474],"category_scores_gemma":[0.0000537782,0.0001756752,0.00003609352,0.00006135429,0.00003362801,0.0001676052,0.00001711106,0.00004317815,0.00007330214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003702352,"about_ca_system_score_gemma":0.000007182246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003037389,"about_ca_topic_score_gemma":3.488415e-7,"domain_scores_codex":[0.9991772,0.0000195864,0.0002212505,0.0001946121,0.00008133449,0.0003060453],"domain_scores_gemma":[0.9997155,0.00004969383,0.00003777524,0.00010209,0.0000216321,0.00007332121],"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":[0.00003985527,0.000002611998,0.000001712221,0.00006087526,0.00004021426,0.000002147091,0.00005484262,0.03449015,0.9586073,0.0009184646,0.005701298,0.00008059654],"study_design_scores_gemma":[0.0003549211,0.00003777246,0.00002333545,0.000006967403,0.00001709909,0.000004757052,0.00001162635,0.0001779215,0.9862098,0.0003043187,0.01263824,0.0002132458],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9681961,0.00003134897,0.02844558,0.001450107,0.001043711,0.0001703935,0.0001128832,0.0004822935,0.00006760669],"genre_scores_gemma":[0.9931422,0.00001252601,0.003010728,0.002858815,0.0006889984,0.0000666215,0.0001376215,0.00006714623,0.00001533274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03431223,"threshold_uncertainty_score":0.7163827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02637775446324376,"score_gpt":0.2113866289216924,"score_spread":0.1850088744584486,"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."}}