{"id":"W2951988115","doi":"10.1021/acsomega.9b00981","title":"Removal of Heavy Metal Water Pollutants (Co<sup>2+</sup> and Ni<sup>2+</sup>) Using Polyacrylamide/Sodium Montmorillonite (PAM/Na-MMT) Nanocomposites","year":2019,"lang":"en","type":"article","venue":"ACS Omega","topic":"Adsorption and biosorption for pollutant removal","field":"Environmental Science","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Universidad de Cartagena; University of Alberta","keywords":"Polyacrylamide; Montmorillonite; Adsorption; Nanocomposite; Freundlich equation; Metal ions in aqueous solution; Langmuir; Nuclear chemistry; Coprecipitation; Fourier transform infrared spectroscopy; Langmuir adsorption model; Chemistry; Metal; Polymerization; Materials science; Chemical engineering; Polymer; Inorganic chemistry; Polymer chemistry; Organic chemistry; Composite material","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":[0.0001217626,0.0005342682,0.0002859865,0.0001766511,0.0001696617,0.0003107559,0.0001639141,0.0003952828,0.0009209254],"category_scores_gemma":[0.000229293,0.0001774571,0.0002602929,0.000142549,0.0001023766,0.0002511475,0.0002010093,0.0002700674,0.0004147231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002065459,"about_ca_system_score_gemma":0.0002630431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0017312,"about_ca_topic_score_gemma":0.00362173,"domain_scores_codex":[0.999831,0.00001787562,0.00001455585,0.00004601579,0.00005853112,0.0000320485],"domain_scores_gemma":[0.9999287,0.00001146137,0.00001407636,0.000004622462,0.00003146321,0.000009696701],"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":[0.00002489264,0.00000462178,0.00008717183,0.00003820139,0.000003869021,0.0000114966,0.000007469094,0.00003574217,0.998428,0.00001140311,0.00001848349,0.00132874],"study_design_scores_gemma":[0.000001377109,0.00004718318,0.0004877199,0.000001547757,0.000008606628,0.00002884745,0.000008284597,0.0004674064,0.9984319,0.000004697917,0.0005102981,0.000002065516],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894036,0.001313476,0.006500716,0.00008396406,0.00004267629,0.00002759717,0.0001226932,0.0001936439,0.002311631],"genre_scores_gemma":[0.9834467,0.0008604921,0.009257516,0.0000452597,0.00001250302,0.00002551439,0.0002413663,0.00003752844,0.006073054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0017312,"threshold_uncertainty_score":0.003442287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01273715348399414,"score_gpt":0.2308626371903622,"score_spread":0.2181254837063681,"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."}}