{"id":"W3201795512","doi":"10.1016/j.cis.2021.102524","title":"Surface characterization of thin-film composite membranes using contact angle technique: Review of quantification strategies and applications","year":2021,"lang":"en","type":"review","venue":"Advances in Colloid and Interface Science","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":229,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of Alberta","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Government of Alberta","keywords":"Contact angle; Wetting; Membrane; Materials science; Surface energy; Surface roughness; Permeation; Characterization (materials science); Thin-film composite membrane; Chemical engineering; Surface modification; Desalination; Nanotechnology; Composite material; Chemistry; Reverse osmosis; 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":[0.001099839,0.001377447,0.001936836,0.003045018,0.0002209455,0.000974801,0.001244147,0.001027418,0.001038169],"category_scores_gemma":[0.0009008431,0.0004823366,0.0008312759,0.003148293,0.0003850005,0.00144501,0.0005466364,0.001161788,0.0007447738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005611919,"about_ca_system_score_gemma":0.001051073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001364702,"about_ca_topic_score_gemma":0.002482637,"domain_scores_codex":[0.999467,0.00005243615,0.00005941222,0.0001009519,0.0002815733,0.00003867409],"domain_scores_gemma":[0.9993554,0.0002678502,0.0001253516,0.00002143833,0.0002059198,0.00002414282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008565171,0.0001641947,0.0004805747,0.03490698,0.0002084375,0.0001578599,0.00005720404,0.0006674237,0.0422512,0.002164599,0.008081752,0.9107742],"study_design_scores_gemma":[0.00003196834,0.0006890142,0.00542805,0.006669285,0.0008923243,0.002234019,0.0001905169,0.001333142,0.06893203,0.002480758,0.9109411,0.0001778408],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0005757324,0.9980221,0.0007410225,0.0001183851,0.00007765141,0.000008600857,0.00004255113,0.00001129439,0.0004027133],"genre_scores_gemma":[0.001825795,0.9961975,0.001243254,0.00009951973,0.00006616066,0.00001234926,0.00006340542,0.000002406441,0.0004896887],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003045018,"threshold_uncertainty_score":0.005816579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03194607008674725,"score_gpt":0.3538308092799308,"score_spread":0.3218847391931836,"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."}}