{"id":"W7115071057","doi":"10.1016/j.jclepro.2025.147319","title":"Programmable oil/water separation performance of wood-based membranes via structural anisotropy and delignification","year":2025,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Surface Modification and Superhydrophobicity","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Major Basic Research Project of the Natural Science Foundation of the Jiangsu Higher Education Institutions; National Key Research and Development Program of China; Graduate Research and Innovation Projects of Jiangsu Province; China Scholarship Council","keywords":"Membrane; Wetting; Flux (metallurgy); Contact angle; Anisotropy; Membrane technology; Coating","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":[0.0002146866,0.0004417297,0.0001789814,0.0002052581,0.0001559528,0.0003541702,0.0001848966,0.0002979173,0.000485731],"category_scores_gemma":[0.0002211645,0.0002059353,0.0001909429,0.000150194,0.0002480668,0.0004871633,0.0002812428,0.0005695357,0.0001841148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002849051,"about_ca_system_score_gemma":0.0001627407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005907558,"about_ca_topic_score_gemma":0.001103475,"domain_scores_codex":[0.9999127,0.00001020788,0.000006876097,0.00002153868,0.00001840362,0.00003028491],"domain_scores_gemma":[0.999878,0.0000364181,0.00004170767,0.00000829864,0.00001821297,0.00001725766],"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":[0.00001320446,0.000004904819,0.00004794848,0.00001184602,0.000001850197,0.00001037687,0.00001154571,0.00009710082,0.999178,0.0000745054,0.00000641795,0.0005422515],"study_design_scores_gemma":[0.000002419517,0.00002062295,0.0004562867,0.000002138281,0.000003731992,0.00001574037,0.00001218037,0.001169176,0.9979606,0.00002996889,0.0003232787,0.000003955952],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938049,0.0003623115,0.004895878,0.00004731125,0.000007288475,0.000007676294,0.0000625353,0.00007512679,0.0007368386],"genre_scores_gemma":[0.996148,0.0003031213,0.003025532,0.00001606608,0.000002568159,0.00001035666,0.00004727744,0.00001796792,0.0004291778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005907558,"threshold_uncertainty_score":0.002067149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01078999113873928,"score_gpt":0.2619296217939689,"score_spread":0.2511396306552296,"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."}}