{"id":"W2590289354","doi":"10.1016/j.watres.2017.02.042","title":"Electrochemical regeneration of a reduced graphene oxide/magnetite composite adsorbent loaded with methylene blue","year":2017,"lang":"en","type":"article","venue":"Water Research","topic":"Graphene research and applications","field":"Materials Science","cited_by":104,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Canada Foundation for Innovation","keywords":"Methylene blue; Adsorption; Graphene; Magnetite; Composite number; Electrochemistry; Oxide; Materials science; Magnetite Nanoparticles; Chemical engineering; Regeneration (biology); Nuclear chemistry; Chemistry; Inorganic chemistry; Composite material; Electrode; Nanotechnology; Metallurgy; Nanoparticle; Organic chemistry; Catalysis; Magnetic nanoparticles; Photocatalysis","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":[0.0001896178,0.0003368832,0.0003591021,0.0004862779,0.0002790345,0.0003307132,0.0006207142,0.000746237,0.001120242],"category_scores_gemma":[0.0001952728,0.0002353316,0.0003339776,0.0002642187,0.0001691227,0.0003642465,0.0002646571,0.0004188163,0.0003772662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003408416,"about_ca_system_score_gemma":0.0002259523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001464778,"about_ca_topic_score_gemma":0.004078975,"domain_scores_codex":[0.9998015,0.00001979845,0.0000104884,0.00004079197,0.00006754306,0.00005985513],"domain_scores_gemma":[0.9999231,0.00001770585,0.00001035871,0.000008333863,0.00002476943,0.00001563395],"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":[0.00008726523,0.00002206844,0.00004525046,0.00005326314,0.000005282252,0.00003315207,0.00001638793,0.00005703347,0.9980405,0.00005074984,0.0000550513,0.00153407],"study_design_scores_gemma":[0.000006207643,0.00009760878,0.0004662871,0.000003554355,0.00001014238,0.00002467875,0.0000146273,0.001263036,0.997491,0.00001470184,0.000602987,0.000005249206],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909428,0.001163872,0.004618922,0.0002097307,0.00009638587,0.00002644715,0.0001663884,0.0002329938,0.002542501],"genre_scores_gemma":[0.9920186,0.0003155597,0.003001059,0.00006981599,0.00001404892,0.00001192669,0.0001107171,0.00001723286,0.004441032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001464778,"threshold_uncertainty_score":0.003747582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04919674747678544,"score_gpt":0.3455417995508053,"score_spread":0.2963450520740198,"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."}}