{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004404445,0.0003549745,0.0004677133,0.00004443034,0.000296201,0.0002558697,0.0007066857,0.0003939192,0.00001541117],"category_scores_gemma":[0.002065919,0.0003096706,0.00007494712,0.0007288801,0.0005600619,0.0004574085,0.0006255406,0.0005105424,0.000007972891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001682706,"about_ca_system_score_gemma":0.0002610913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000121178,"about_ca_topic_score_gemma":0.000005024554,"domain_scores_codex":[0.9964426,0.00009980405,0.0006271712,0.0006603971,0.0009846329,0.001185439],"domain_scores_gemma":[0.9972283,0.001253541,0.0001954876,0.0006097592,0.0003287114,0.0003841679],"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":[0.0001567441,0.00005616705,0.00004869892,0.0004037127,0.00002161578,0.000006693568,0.000194921,0.0006677955,0.9916885,0.0001186311,0.00008542526,0.006551053],"study_design_scores_gemma":[0.0008278741,0.00008095378,0.0001808045,0.0001423033,0.00004728388,0.0001167547,0.0001044269,0.004748988,0.9926777,0.00008816915,0.0006602511,0.0003244313],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955364,0.00260697,0.0002394564,0.000244246,0.0005390065,0.0006345858,0.00007519456,0.00008800285,0.00003610615],"genre_scores_gemma":[0.9978821,0.0002602285,0.000548599,0.00001195435,0.001022996,0.0001691148,0.00001005898,0.00006033455,0.00003456413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006226622,"threshold_uncertainty_score":0.9999356,"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."}}