{"id":"W3212189156","doi":"10.1021/acs.biomac.1c00909","title":"Regioselective Protection and Deprotection of Nanocellulose Molecular Design Architecture: Robust Platform for Multifunctional Applications","year":2021,"lang":"en","type":"article","venue":"Biomacromolecules","topic":"Advanced Cellulose Research Studies","field":"Materials Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Ontario Research Foundation","keywords":"Nanocellulose; Regioselectivity; Surface modification; Ionic liquid; Cellulose; Chemistry; Nanomaterials; Materials science; Polymer chemistry; Chemical engineering; Combinatorial chemistry; Nanotechnology; Organic chemistry; Catalysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002045568,0.0005081337,0.0002140493,0.000180754,0.0000997598,0.0002173597,0.0002507123,0.0003034206,0.000636458],"category_scores_gemma":[0.0002010249,0.0001551106,0.000268382,0.0001326141,0.0001664325,0.0002772995,0.000196402,0.0004406798,0.0003967798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002967199,"about_ca_system_score_gemma":0.000236332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003512042,"about_ca_topic_score_gemma":0.0006655161,"domain_scores_codex":[0.9998627,0.00001949266,0.00001380587,0.00003768491,0.00003107234,0.00003515244],"domain_scores_gemma":[0.9998735,0.00001619781,0.00005154044,0.0000237168,0.00001556908,0.00001946716],"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":[0.00001315126,0.000008477396,0.00003547757,0.00003037181,0.000003408029,0.00002964929,0.000009306859,0.0001971938,0.9977471,0.0001396381,0.00001670809,0.001769364],"study_design_scores_gemma":[0.000002328802,0.00003898628,0.0001574468,0.000001512602,0.000004126777,0.00004211114,0.000002620366,0.0004052705,0.9982784,0.00001931229,0.001044918,0.000002892258],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9216,0.00363362,0.06864004,0.0002126407,0.0000683785,0.0001895171,0.0003696336,0.000368414,0.004917804],"genre_scores_gemma":[0.9596316,0.001712572,0.03587227,0.0001133269,0.00001830327,0.0001033731,0.0002698482,0.00005432129,0.002224338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000636458,"threshold_uncertainty_score":0.00215292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04126847041774535,"score_gpt":0.2698355627554676,"score_spread":0.2285670923377222,"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."}}