{"id":"W2886067465","doi":"10.1016/j.ijbiomac.2018.07.052","title":"Preparation of graphene oxide/chitosan/ferrite nanocomposite for Chromium(VI) removal from aqueous solution","year":2018,"lang":"en","type":"article","venue":"International Journal of Biological Macromolecules","topic":"Adsorption and biosorption for pollutant removal","field":"Environmental Science","cited_by":138,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"U.S. Environmental Protection Agency","keywords":"Thermogravimetric analysis; Nanocomposite; Adsorption; Materials science; Aqueous solution; Graphene; Scanning electron microscope; Nuclear chemistry; Fourier transform infrared spectroscopy; Oxide; Langmuir adsorption model; Chromium; Chemical engineering; Chemistry; Composite material; Nanotechnology; Metallurgy; Organic chemistry","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":[0.0001299747,0.0003724402,0.0001853469,0.0003907342,0.0002173421,0.0001641963,0.0002730682,0.0003623299,0.0008378353],"category_scores_gemma":[0.0001559007,0.0001637285,0.000376711,0.0002081906,0.0001056746,0.0001943198,0.000145153,0.0003412571,0.0001803233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002404225,"about_ca_system_score_gemma":0.000226904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001642994,"about_ca_topic_score_gemma":0.003856311,"domain_scores_codex":[0.9999118,0.000006345138,0.000007735119,0.00001856546,0.00003334566,0.0000221785],"domain_scores_gemma":[0.9999492,0.000008239202,0.00001057541,0.000004353479,0.00001809023,0.000009497237],"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":[0.00003780061,0.00001705776,0.00004072012,0.00004047535,0.000004848436,0.0000318801,0.000008534335,0.000129381,0.9983081,0.00002887927,0.000026535,0.00132572],"study_design_scores_gemma":[0.000006951195,0.00009085164,0.0005610264,0.000002264879,0.0000131676,0.00003702422,0.000006423846,0.0009097915,0.9978722,0.00001153874,0.0004832387,0.000005460735],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892105,0.0006598576,0.007945856,0.00009292168,0.0000483863,0.00006495299,0.0001608522,0.0001254635,0.001691268],"genre_scores_gemma":[0.9900149,0.000309566,0.007223112,0.00002968976,0.000008113673,0.00002565742,0.0001539131,0.00001645794,0.002218596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001642994,"threshold_uncertainty_score":0.003266871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01855579050290131,"score_gpt":0.2934825123402218,"score_spread":0.2749267218373205,"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."}}