{"id":"W2331871804","doi":"10.1021/ie3019092","title":"Fe<sub>3</sub>O<sub>4</sub> Nanoparticles and Carboxymethyl Cellulose: A Green Option for the Removal of Atmospheric Benzene, Toluene, Ethylbenzene, and <i>o</i>-Xylene (BTEX)","year":2012,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Catalytic Processes in Materials Science","field":"Materials Science","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Concordia University","keywords":"BTEX; Ethylbenzene; Toluene; Benzene; Nanoparticle; Chemistry; Xylene; Adsorption; Carboxymethyl cellulose; Flame ionization detector; Gas chromatography; Chemical engineering; Nuclear chemistry; Materials science; Organic chemistry; Chromatography; Nanotechnology; Sodium","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":[0.00009490205,0.0003832351,0.0002346548,0.0001380982,0.0001337026,0.0002124496,0.0002248852,0.0004432326,0.0004841201],"category_scores_gemma":[0.00009730442,0.0001600401,0.0002582629,0.00008410657,0.0002142394,0.000224454,0.0001767986,0.0002658549,0.0002722356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003183766,"about_ca_system_score_gemma":0.0001687386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001465498,"about_ca_topic_score_gemma":0.002790262,"domain_scores_codex":[0.9998999,0.000008654677,0.000004784136,0.00001714615,0.00004481417,0.00002478116],"domain_scores_gemma":[0.9999444,0.000008517491,0.00002219501,0.000004199668,0.0000116483,0.000009013721],"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":[0.00003270219,0.00001035694,0.0001742021,0.0000315614,0.000005720688,0.00003358322,0.000005238296,0.0001038352,0.9985756,0.0000297342,0.0000213035,0.0009761796],"study_design_scores_gemma":[0.000002623673,0.00005998506,0.001139619,0.000002218149,0.00000892107,0.00005611874,0.00001030975,0.001014726,0.9969583,0.00001341252,0.0007315898,0.000002330949],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862236,0.001665401,0.009825875,0.0001172546,0.00003756818,0.00002125434,0.00006639528,0.0000961242,0.001946445],"genre_scores_gemma":[0.987801,0.0005989261,0.007901167,0.00008809224,0.000009873971,0.00001589182,0.00008173411,0.00002574742,0.003477662],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001465498,"threshold_uncertainty_score":0.002913952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04735118237810539,"score_gpt":0.2886118894922836,"score_spread":0.2412607071141782,"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."}}