{"id":"W2807932022","doi":"10.1016/j.seppur.2018.06.032","title":"Layer-by-layer self-assembled chitosan/PAA nanofiltration membranes","year":2018,"lang":"en","type":"article","venue":"Separation and Purification Technology","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":116,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Nanofiltration; Membrane; Chemical engineering; Polyelectrolyte; Chitosan; Acrylic acid; Aqueous solution; Chemistry; Microporous material; Cationic polymerization; Thermal stability; Layer by layer; Chloride; Layer (electronics); Polymer chemistry; Chromatography; Materials science; Organic chemistry; Polymer; Copolymer","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002425016,0.0002499776,0.0002230096,0.000245961,0.0004510886,0.00008729528,0.0003610816,0.0004759257,0.0008414822],"category_scores_gemma":[0.00009422846,0.0002442357,0.0000352035,0.0009462131,0.0003070244,0.0005061576,0.00009927495,0.0001868681,0.001308916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006197571,"about_ca_system_score_gemma":0.00002394132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003025114,"about_ca_topic_score_gemma":0.00006671473,"domain_scores_codex":[0.9981952,0.00007788622,0.0004565253,0.0006596396,0.0002846889,0.0003260857],"domain_scores_gemma":[0.9989672,0.00003859111,0.0002603769,0.0006148288,0.00004421425,0.00007473767],"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":[0.00001874318,0.00008265048,0.002818812,0.000009071006,0.00001313315,4.499827e-7,0.0002249977,0.000008305323,0.9735684,0.008545423,0.006717404,0.007992639],"study_design_scores_gemma":[0.000364035,0.0001948822,0.002147294,0.00000434818,0.0000178241,0.00002189732,0.0001729771,0.001840639,0.9143349,0.003303134,0.07732881,0.0002692481],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9634668,0.0002240805,0.006092665,0.006947795,0.000222395,0.0006712462,0.000006083059,0.001709816,0.02065907],"genre_scores_gemma":[0.9943472,0.0003075003,0.004067631,0.0003305119,0.00006103511,0.0001665562,0.00004434223,0.00002213426,0.0006531199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07061141,"threshold_uncertainty_score":0.9994687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01346275145444477,"score_gpt":0.2727076028979429,"score_spread":0.2592448514434982,"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."}}