{"id":"W3024680191","doi":"10.1016/j.msec.2020.111089","title":"Engineered magnetoactive collagen hydrogels with tunable and predictable mechanical response","year":2020,"lang":"en","type":"article","venue":"Materials Science and Engineering C","topic":"Collagen: Extraction and Characterization","field":"Materials Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Academia Româna; Ontario Ministry of Research, Innovation and Science","keywords":"Self-healing hydrogels; Swelling; Materials science; Nanoparticle; Aqueous solution; Nanocomposite; Superparamagnetism; Chemical engineering; Composite material; Magnetic nanoparticles; Nanotechnology; Polymer chemistry; Chemistry; Magnetization; Magnetic field; Organic chemistry","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":[0.0001586737,0.0003115554,0.00008800509,0.0001300491,0.0000728667,0.0002510556,0.00015707,0.0002909215,0.0005550816],"category_scores_gemma":[0.0001784662,0.0001314935,0.00009903499,0.0001000095,0.0001310528,0.000195928,0.0001669828,0.000282463,0.0001882616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002795226,"about_ca_system_score_gemma":0.0001312474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002515683,"about_ca_topic_score_gemma":0.001018013,"domain_scores_codex":[0.9998889,0.00001200321,0.000008466088,0.0000217763,0.00003183161,0.00003705425],"domain_scores_gemma":[0.9997725,0.00004277333,0.00009197571,0.00001395155,0.00002909142,0.00004969617],"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":[0.00001768097,0.000005265132,0.00003725131,0.000009424358,0.000001238776,0.00002056201,0.000005849123,0.00007118002,0.9993377,0.00005454933,0.00001903259,0.0004202797],"study_design_scores_gemma":[0.000009526035,0.0000431443,0.0006774298,0.000001994478,0.000003459537,0.00006866894,0.00001057238,0.0008698687,0.9971977,0.00002252458,0.001089889,0.000005171935],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9857368,0.0007192153,0.01055293,0.00008103423,0.00008982392,0.00003431401,0.0002697638,0.0001238918,0.002392333],"genre_scores_gemma":[0.9923516,0.0002232635,0.005529576,0.00006244582,0.0000139772,0.00002527784,0.0001312224,0.0000273384,0.001635329],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005550816,"threshold_uncertainty_score":0.002028048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007908662116769116,"score_gpt":0.1882546076168819,"score_spread":0.1803459455001128,"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."}}