{"id":"W3174541067","doi":"10.1016/j.jcis.2021.06.157","title":"A photo-Fenton nanocomposite ultrafiltration membrane for enhanced dye removal with self-cleaning properties","year":2021,"lang":"en","type":"article","venue":"Journal of Colloid and Interface Science","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":61,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation","keywords":"Membrane; Chemical engineering; Ultrafiltration (renal); Photocatalysis; Fouling; Phase inversion; Hydrophilization; Biofouling; Membrane fouling; Filtration (mathematics); Chemistry; Nanoparticle; Materials science; Chromatography; Organic chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000544075,0.0001116001,0.0001800243,0.0000736533,0.000259922,0.0001878904,0.0002942246,0.0000361554,0.00008639517],"category_scores_gemma":[0.0001549855,0.00007791126,0.00003600561,0.0004893001,0.0004117035,0.001047726,0.00007657609,0.0001198589,0.000005302029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001268996,"about_ca_system_score_gemma":0.0001307828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006497941,"about_ca_topic_score_gemma":0.00002766756,"domain_scores_codex":[0.9987543,0.00002502922,0.000302523,0.0002412102,0.0004654027,0.0002115733],"domain_scores_gemma":[0.9993466,0.00003454682,0.0002637898,0.0001344624,0.0001410664,0.00007956657],"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":[0.0001214784,0.00004056729,0.00008849632,0.00001628925,0.000008046876,0.0000018482,0.001046452,0.001325911,0.9965057,0.00003628176,0.00006686076,0.0007420626],"study_design_scores_gemma":[0.0004986982,0.0005416311,0.0002258176,0.0001029467,0.00001514684,0.0003760905,0.001044442,0.001953005,0.9926485,0.00004396862,0.00243594,0.0001137832],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909386,0.0001443114,0.005654238,0.0003518054,0.0001306574,0.0002141625,8.941935e-7,0.00002971894,0.002535628],"genre_scores_gemma":[0.9755723,0.0000936729,0.02343537,0.00008653513,0.00001715581,0.000005817123,1.604022e-7,0.000006239733,0.0007827805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01778113,"threshold_uncertainty_score":0.3177129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01013129930347425,"score_gpt":0.2361065329310149,"score_spread":0.2259752336275407,"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."}}