{"id":"W4210423424","doi":"10.1016/j.colsurfb.2022.112359","title":"Polysaccharide-based layer-by-layer nanoarchitectonics with sulfated chitosan for tuning anti-thrombogenic properties","year":2022,"lang":"en","type":"article","venue":"Colloids and Surfaces B Biointerfaces","topic":"Polymer Surface Interaction Studies","field":"Materials Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Chitosan; Protein adsorption; Adhesion; Contact angle; Surface modification; Platelet adhesion; Monolayer; Materials science; Chemical engineering; Chemistry; Layer (electronics); Polymer; Adsorption; Nanotechnology; Biomedical engineering; Biochemistry; Organic chemistry; Composite material","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001089334,0.0004075496,0.0001270103,0.000160082,0.00008942785,0.0002525144,0.0002476826,0.0002535487,0.000657082],"category_scores_gemma":[0.0001558853,0.0001746722,0.0002005801,0.0001660408,0.0001535639,0.0001844359,0.0001877355,0.0003373829,0.0002020433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002913681,"about_ca_system_score_gemma":0.0001660158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007162672,"about_ca_topic_score_gemma":0.001725413,"domain_scores_codex":[0.9999268,0.000007711988,0.000007448131,0.00001296253,0.00002376511,0.00002140383],"domain_scores_gemma":[0.9999063,0.00001365411,0.00003653222,0.000008536284,0.00001685161,0.00001817455],"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":[0.00001909739,0.000008769608,0.0000269192,0.00001416516,0.000004088227,0.000009876603,0.000005086305,0.0001324618,0.9992414,0.00003339546,0.00001725121,0.0004876086],"study_design_scores_gemma":[0.000008371957,0.0001212507,0.0006426111,0.000002277152,0.00001674231,0.00002166694,0.00000691512,0.001644607,0.9970189,0.00001165038,0.0004977396,0.000007290968],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952208,0.0007083978,0.002809202,0.00004702569,0.00003596038,0.00002441106,0.00007741995,0.00008093016,0.0009958639],"genre_scores_gemma":[0.9954265,0.0003974216,0.003055103,0.00005272259,0.000007801073,0.00001743438,0.00006803111,0.00001723074,0.000957772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007162672,"threshold_uncertainty_score":0.00219816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02876406483523684,"score_gpt":0.2473751566947554,"score_spread":0.2186110918595186,"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."}}