{"id":"W4409158743","doi":"10.1016/j.jece.2025.116478","title":"Sustainable layer-by-layer assembled nanofiltration membranes with optimized pore size for lithium-magnesium selective separation","year":2025,"lang":"en","type":"article","venue":"Journal of environmental chemical engineering","topic":"Extraction and Separation Processes","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada's Oil Sands Innovation Alliance","keywords":"Nanofiltration; Layer (electronics); Membrane; Chemical engineering; Magnesium; Separation (statistics); Materials science; Lithium (medication); Layer by layer; Chemistry; Nanotechnology; Metallurgy; Computer science; Engineering","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":[0.0002512891,0.0004814378,0.0003164092,0.0002055585,0.0001594369,0.000264926,0.0002758282,0.0006995918,0.0003417336],"category_scores_gemma":[0.0001904046,0.0002099163,0.0003193631,0.0001564984,0.0001516739,0.0005338228,0.0002824223,0.0004157216,0.0003279815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000422693,"about_ca_system_score_gemma":0.0002103968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003210993,"about_ca_topic_score_gemma":0.000818486,"domain_scores_codex":[0.999855,0.00002199559,0.00001385523,0.00002449142,0.00005275965,0.00003180232],"domain_scores_gemma":[0.9999468,0.000008586509,0.00001714594,0.000002952975,0.00001457338,0.00000989847],"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":[0.000006795495,0.000005678266,0.00002293119,0.00002398092,0.000001785185,0.00001119351,0.000005900527,0.00009428158,0.999253,0.00003585207,0.00001766533,0.0005209835],"study_design_scores_gemma":[0.000008531947,0.00008227421,0.0006822155,0.000003823365,0.000009621955,0.00005925198,0.00001021869,0.003427292,0.9942213,0.0000429726,0.001443509,0.000008990424],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9531684,0.002620938,0.04191903,0.0002122402,0.00004786764,0.00007386982,0.0002804853,0.0004595375,0.001217607],"genre_scores_gemma":[0.9487155,0.001271999,0.04772878,0.0001200502,0.00001270914,0.0001031376,0.0002902974,0.00004669079,0.001710868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006995918,"threshold_uncertainty_score":0.003066838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003935724376931998,"score_gpt":0.219041751059828,"score_spread":0.215106026682896,"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."}}