{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002285629,0.0001480142,0.0002279329,0.00008591659,0.00007092811,0.00003410679,0.0004619563,0.0001343677,0.0005567559],"category_scores_gemma":[0.0001166094,0.0001082581,0.0002738772,0.0001052153,0.0004117986,0.0001686707,0.0001233307,0.00009922586,0.00004091227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007679372,"about_ca_system_score_gemma":0.00001726927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009821441,"about_ca_topic_score_gemma":0.00003308459,"domain_scores_codex":[0.9984504,0.00008802879,0.0006420355,0.0002223114,0.0004286515,0.000168554],"domain_scores_gemma":[0.998971,0.00007950585,0.0006275083,0.0001027633,0.0001276249,0.00009157051],"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":[0.0009682446,0.0001501865,0.001793541,0.00000119022,0.00008100816,0.00002110923,0.00005714063,0.00004238455,0.9877668,0.0004907095,0.0003021449,0.008325542],"study_design_scores_gemma":[0.001903693,0.001891337,0.04870249,0.00008390721,0.00005115043,0.0007732721,0.00005545048,0.003120209,0.9192818,0.0111129,0.012702,0.0003217779],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9701241,0.00007660465,0.02742122,0.0005254537,0.0006340431,0.0001520724,0.0001290367,0.00001811225,0.0009193456],"genre_scores_gemma":[0.9803424,0.00007531464,0.01883481,0.0002675281,0.0003913773,0.000002620374,0.000042778,0.000007628993,0.00003556324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06848498,"threshold_uncertainty_score":0.6096087,"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."}}