{"id":"W2793850032","doi":"10.1002/cjce.23185","title":"Green synthesis of copper oxide nanoparticles impregnated on activated carbon using <i>Moringa oleifera</i> leaves extract for the removal of nitrates from water","year":2018,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Copper-based nanomaterials and applications","field":"Materials Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação Araucária; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Adsorption; Activated carbon; Copper; Nanoparticle; Nuclear chemistry; Materials science; Carbon fibers; BET theory; Moringa; Oxide; Chemistry; Chemical engineering; Inorganic chemistry; Nanotechnology; Metallurgy; Organic chemistry; Composite material; Composite number","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007357464,0.0004155954,0.0002311868,0.0004439424,0.0001709794,0.0002275108,0.0002515648,0.000359025,0.0006059477],"category_scores_gemma":[0.0001057313,0.000202665,0.0004257652,0.0002240993,0.0001401174,0.0001814972,0.0001686528,0.0002350686,0.0002484842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002361252,"about_ca_system_score_gemma":0.0001231329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008750695,"about_ca_topic_score_gemma":0.002298521,"domain_scores_codex":[0.9999177,0.000007714772,0.000005919353,0.00002261343,0.00002995833,0.00001603575],"domain_scores_gemma":[0.9999311,0.00000930897,0.00001954698,0.000007299632,0.00001952902,0.00001319431],"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":[0.00001719664,0.000008167184,0.00004343319,0.0000436569,0.000003902859,0.00003298608,0.000005010686,0.00005141582,0.9987867,0.00001697561,0.00001369507,0.0009767879],"study_design_scores_gemma":[0.000001949379,0.00007817205,0.0009544505,0.000003631777,0.00001015279,0.00003511167,0.000005604659,0.0005000636,0.9977661,0.000007598218,0.0006341285,0.000003119016],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874572,0.001361225,0.008343385,0.00003951224,0.00004641511,0.00004677004,0.0002317708,0.0001962286,0.002277579],"genre_scores_gemma":[0.986596,0.0007991918,0.009786199,0.00003850925,0.00001007769,0.00003512955,0.0002398788,0.00004707762,0.002447865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008750695,"threshold_uncertainty_score":0.002027094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01857213085065189,"score_gpt":0.2201054015026819,"score_spread":0.20153327065203,"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."}}