{"id":"W4361806110","doi":"10.48550/arxiv.2303.16262","title":"Programming hydrogel adhesion with engineered polymer network topology","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adhesion, Friction, and Surface Interactions","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Deafness and Other Communication Disorders; Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; McGill University; Division of Civil, Mechanical and Manufacturing Innovation; National Science Foundation","keywords":"Self-healing hydrogels; Nanotechnology; Adhesion; Soft robotics; Materials science; Controllability; Synthetic biology; Computer science; Microfluidics; Topology (electrical circuits); Robotics; Tissue engineering; Robot; Artificial intelligence; Engineering; Biomedical engineering; Bioinformatics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000121119,0.0003913321,0.0001408353,0.0001958369,0.0001464201,0.0003794882,0.0002733678,0.0001806577,0.00108068],"category_scores_gemma":[0.0003645878,0.0001665872,0.00009099673,0.000194557,0.0001966287,0.0005309625,0.0003766591,0.0002910169,0.0002154747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003193372,"about_ca_system_score_gemma":0.0001624857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001720686,"about_ca_topic_score_gemma":0.0007061667,"domain_scores_codex":[0.9999262,0.00001018066,0.000005669144,0.00002374145,0.00001867619,0.00001540957],"domain_scores_gemma":[0.9998481,0.00004902094,0.00006040356,0.00001254873,0.00001168808,0.00001810973],"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":[0.00002972444,0.00003771763,0.0001875214,0.00005305715,0.00000619766,0.00004843285,0.00002235463,0.003123964,0.9885967,0.001557204,0.0001245766,0.006212505],"study_design_scores_gemma":[0.0000399003,0.0001750411,0.0009047122,0.00001326532,0.00001464371,0.00007399374,0.00002593264,0.04953399,0.9449291,0.0006542944,0.003614934,0.00002024353],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9629045,0.0005057793,0.02989849,0.0001454806,0.00004413486,0.00004752378,0.00009342672,0.0002246755,0.00613601],"genre_scores_gemma":[0.9883469,0.0003461114,0.009620235,0.00005621149,0.00001147736,0.00004970316,0.00004582015,0.00004848581,0.001475081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00108068,"threshold_uncertainty_score":0.00361526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04378420461743745,"score_gpt":0.178009914248052,"score_spread":0.1342257096306145,"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."}}