{"id":"W4313705742","doi":"10.1016/j.jhazmat.2023.130752","title":"One-stop rapid decomplexation and copper capture of Cu(II)-EDTA with nanoscale zero valent iron","year":2023,"lang":"en","type":"article","venue":"Journal of Hazardous Materials","topic":"Environmental remediation with nanomaterials","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Zerovalent iron; Copper; Wastewater; Metal; Chemistry; Effluent; Adsorption; Metal ions in aqueous solution; Precipitation; Chelation; Inorganic chemistry; Nuclear chemistry; Metallurgy; Materials science; Environmental engineering; Environmental science; 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":[0.0003191957,0.0003751844,0.0002768782,0.0001957607,0.0002333907,0.000177464,0.0005012667,0.000378185,0.001328639],"category_scores_gemma":[0.0004079303,0.0001928024,0.0001377878,0.0001229215,0.0002004281,0.0002396386,0.0002691315,0.0004594265,0.0004711472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002896769,"about_ca_system_score_gemma":0.0002931531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008890307,"about_ca_topic_score_gemma":0.002115398,"domain_scores_codex":[0.9997545,0.0000358998,0.00001477825,0.00006728453,0.00005147826,0.00007600857],"domain_scores_gemma":[0.999885,0.00003338836,0.00002071107,0.00002582022,0.00002092992,0.00001407996],"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":[0.0003029076,0.00005722569,0.0001958705,0.00008450975,0.00001056454,0.00008445126,0.0001179641,0.0002078832,0.9929748,0.0003132453,0.0004111865,0.005239469],"study_design_scores_gemma":[0.00001426107,0.00009728584,0.0002347434,0.000003791549,0.000003664456,0.00003542567,0.0000141283,0.00059561,0.9976614,0.00002155909,0.001313842,0.000004284269],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9762049,0.0006670767,0.01897153,0.0002090243,0.00007449102,0.0001327323,0.0002001497,0.0003046926,0.003235399],"genre_scores_gemma":[0.9901686,0.0002152149,0.005342531,0.00006195134,0.0000109536,0.000045382,0.0002427546,0.00003294569,0.003879686],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001328639,"threshold_uncertainty_score":0.004444778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01150750199124024,"score_gpt":0.2089761475829733,"score_spread":0.1974686455917331,"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."}}