{"id":"W2801125870","doi":"10.3791/54502","title":"Fabricating Degradable Thermoresponsive Hydrogels on Multiple Length Scales via Reactive Extrusion, Microfluidics, Self-assembly, and Electrospinning","year":2018,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada","keywords":"Self-healing hydrogels; Materials science; Drug delivery; Nanofiber; Electrospinning; Nanotechnology; Microfluidics; Polymer; Methacrylate; Lower critical solution temperature; Tissue engineering; Ethylene glycol; Poly(N-isopropylacrylamide); Chemical engineering; Polymer chemistry; Monomer; Biomedical engineering; Copolymer; Composite material","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":[0.0008585807,0.0002514085,0.0003922458,0.0003174439,0.0002121541,0.00008115338,0.0003167977,0.0001367294,0.00003080349],"category_scores_gemma":[0.0006742403,0.000212472,0.00009297535,0.0002977448,0.0001389211,0.0002154891,0.0001184985,0.000450533,0.00002531919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003001096,"about_ca_system_score_gemma":0.00007327398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001912712,"about_ca_topic_score_gemma":6.329335e-7,"domain_scores_codex":[0.9977977,0.0001784184,0.0005898057,0.0002332918,0.0006841627,0.0005165754],"domain_scores_gemma":[0.9983861,0.0005801148,0.0002428475,0.0002147307,0.0002931243,0.0002830623],"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":[0.0002661566,0.0001210871,0.001083185,0.00001952559,0.0002032266,0.00002550155,0.001770228,0.000002945793,0.9846967,0.00002848673,0.0006099297,0.01117303],"study_design_scores_gemma":[0.001326187,0.0006440253,0.001793919,0.0002021229,0.00001697399,0.00007746898,0.0003326294,0.006347745,0.9864918,0.0001023672,0.002452098,0.00021265],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900854,0.002101153,0.006722375,0.00005216069,0.0002686319,0.0001882108,0.000001539464,0.00009784821,0.0004826806],"genre_scores_gemma":[0.989377,0.000401436,0.009750478,0.00007946178,0.0002899387,0.00001010146,0.00000123136,0.00006627324,0.00002406113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01096038,"threshold_uncertainty_score":0.8664356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01968351264569625,"score_gpt":0.3751184201885522,"score_spread":0.355434907542856,"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."}}