{"id":"W2591383177","doi":"10.1016/j.colsurfa.2017.02.065","title":"High-performance iron oxide–graphene oxide nanocomposite adsorbents for arsenic removal","year":2017,"lang":"en","type":"article","venue":"Colloids and Surfaces A Physicochemical and Engineering Aspects","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":223,"is_retracted":false,"has_abstract":false,"ca_institutions":"Barrick Gold (Canada); Laurentian University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adsorption; Graphene; Oxide; Arsenic; Iron oxide; Nanocomposite; Amorphous solid; Materials science; Chemical engineering; Inorganic chemistry; Nanoparticle; Nuclear chemistry; Chemistry; Nanotechnology; Metallurgy; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001632736,0.0003378678,0.0001776211,0.0004762164,0.000331429,0.0002722548,0.000216743,0.0003555606,0.0007124647],"category_scores_gemma":[0.0001252712,0.0001770195,0.0002789991,0.0001539324,0.0001383132,0.0002375379,0.0002082035,0.0003111123,0.0002079281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003583077,"about_ca_system_score_gemma":0.0001956118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001641892,"about_ca_topic_score_gemma":0.00549965,"domain_scores_codex":[0.99988,0.00001361611,0.000005916952,0.00001633302,0.00005603027,0.00002811297],"domain_scores_gemma":[0.9999557,0.000008646439,0.000004927452,0.000002825381,0.00001849972,0.00000950605],"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":[0.00005384391,0.00003801902,0.0001056552,0.00004193536,0.000008305881,0.00002823497,0.0000107379,0.0002620443,0.996986,0.00007104021,0.00008870639,0.002305461],"study_design_scores_gemma":[0.000005444154,0.000129719,0.001092215,0.000002541736,0.00001510904,0.0000347587,0.00001609805,0.003479258,0.9940873,0.000032563,0.001099424,0.000005537508],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916601,0.001256473,0.004424893,0.0001161592,0.00005223159,0.00002290694,0.00007343027,0.00009792564,0.002295928],"genre_scores_gemma":[0.9947178,0.0003608644,0.002252213,0.00002705474,0.00001333037,0.000007043853,0.00006900874,0.000007834535,0.002544899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001641892,"threshold_uncertainty_score":0.003264606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005252977479150715,"score_gpt":0.1946113680151991,"score_spread":0.1893583905360484,"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."}}