{"id":"W2910155116","doi":"10.1007/s13762-019-02214-9","title":"Adsorptive removal of toluene from aqueous solution using metal–organic framework MIL-101(Cr): removal optimization by response surface methodology","year":2019,"lang":"en","type":"article","venue":"International Journal of Environmental Science and Technology","topic":"Metal-Organic Frameworks: Synthesis and Applications","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Shiraz University","keywords":"Adsorption; Toluene; Freundlich equation; Endothermic process; Aqueous solution; Fourier transform infrared spectroscopy; Chemistry; Response surface methodology; Dibenzothiophene; Desorption; Chemical engineering; Nuclear chemistry; Materials science; Inorganic chemistry; Chromatography; Organic chemistry; Catalysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001508124,0.0002965958,0.0002522988,0.0002029844,0.0001771901,0.0002376437,0.0002691574,0.0003081532,0.0005404109],"category_scores_gemma":[0.0001646996,0.0001370455,0.0003341393,0.0001879821,0.0001322993,0.0001634622,0.0001755953,0.0002996409,0.0002000867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002912606,"about_ca_system_score_gemma":0.000198274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001846392,"about_ca_topic_score_gemma":0.004819229,"domain_scores_codex":[0.9998195,0.00001652134,0.00001545524,0.00002614111,0.00007957885,0.0000428293],"domain_scores_gemma":[0.9999508,0.00001121323,0.00001166192,0.000004505881,0.00001576478,0.000006049338],"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":[0.00002971657,0.00001662104,0.00006358528,0.00003693324,0.000003116913,0.00001949106,0.00001063325,0.0001278169,0.9980512,0.00002285709,0.00002129806,0.001596722],"study_design_scores_gemma":[0.000001326655,0.00004739658,0.0002319606,8.101159e-7,0.000002788142,0.0000101436,0.000006208274,0.0005156853,0.9989709,0.000003195624,0.0002079873,0.000001822999],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937066,0.0007528908,0.004148193,0.00005830022,0.00002258546,0.00002575094,0.0001049405,0.0001078412,0.0010728],"genre_scores_gemma":[0.9940203,0.0004429716,0.00376194,0.0000231356,0.000005348228,0.00001130056,0.0001023014,0.00001232976,0.001620351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001846392,"threshold_uncertainty_score":0.003671229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01663984153542181,"score_gpt":0.2670756240757923,"score_spread":0.2504357825403705,"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."}}