{"id":"W3135519832","doi":"10.1002/cjce.24104","title":"Efficient extraction and theoretical insights for separating <i>o</i> ‐, <i>m</i> ‐, and <i>p</i> ‐cresol from model coal tar by an ionic liquid [ <scp>Emim</scp> ][ <scp>DCA</scp> ]","year":2021,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Ionic liquids properties and applications","field":"Chemical Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Ionic liquid; Chemistry; Extraction (chemistry); Coal tar; tar (computing); Cresol; Dicyanamide; Phenol; Hydrogen bond; Coal; Organic chemistry; Catalysis; Molecule","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002316743,0.0005093657,0.000246869,0.0003771031,0.0004072778,0.0004146525,0.0005254105,0.0005352382,0.001471816],"category_scores_gemma":[0.000327708,0.0002330678,0.0004803197,0.0001976366,0.0005343884,0.000714048,0.0003926585,0.0004735549,0.0003277355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007451795,"about_ca_system_score_gemma":0.0005876513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002097662,"about_ca_topic_score_gemma":0.00181494,"domain_scores_codex":[0.9999211,0.00001046835,0.000005146678,0.00001295766,0.00002911729,0.00002131567],"domain_scores_gemma":[0.9999473,0.00001948513,0.000009301072,0.000005886876,0.00001475642,0.00000326035],"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":[0.0002004068,0.0002683958,0.001437085,0.001800012,0.00004418164,0.001331152,0.0002851782,0.05847738,0.7022441,0.2098341,0.001467966,0.0226099],"study_design_scores_gemma":[0.00004314952,0.0002041485,0.001089056,0.00006892414,0.00004162371,0.000533531,0.0002691419,0.6749654,0.2782825,0.03494186,0.009500857,0.00005973836],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6598489,0.00869791,0.2762859,0.002180903,0.0001843352,0.0002111258,0.0003940092,0.0003068299,0.05189008],"genre_scores_gemma":[0.9579491,0.004715079,0.03335401,0.0001989818,0.00002341169,0.0001685962,0.0001674241,0.00003011567,0.003393245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002097662,"threshold_uncertainty_score":0.005406678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007991411122330911,"score_gpt":0.20678130833812,"score_spread":0.1987898972157891,"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."}}