{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005141832,0.0005664053,0.0006962138,0.0002328524,0.0002854521,0.0001159504,0.0005053345,0.0003057434,0.001376549],"category_scores_gemma":[0.00002511874,0.0004370448,0.0002540063,0.000457025,0.0005367282,0.0008680339,0.0005160862,0.0003337395,0.001447909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002361946,"about_ca_system_score_gemma":0.00003033649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005566164,"about_ca_topic_score_gemma":0.00001579256,"domain_scores_codex":[0.9962877,0.0001679183,0.000795001,0.000880097,0.0009140694,0.0009552055],"domain_scores_gemma":[0.9986676,0.00005345385,0.000232508,0.0006756429,0.00003284143,0.0003379757],"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":[0.0003342697,0.0002102981,0.02302585,0.00004874574,0.000129157,0.00009890458,0.001428977,0.003843691,0.9662043,0.0001437742,0.0002496621,0.004282352],"study_design_scores_gemma":[0.007397322,0.001318658,0.0312571,0.0002442227,0.0004537975,0.005232139,0.002449021,0.114372,0.7712133,0.0008784211,0.06177102,0.003412952],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945429,0.000222297,0.00005241191,0.0003415155,0.0002019612,0.0005936592,0.0001974408,0.000122574,0.003725227],"genre_scores_gemma":[0.9896008,0.00006589405,0.001902631,0.0004543438,0.0001249176,0.000004651994,0.00003209627,0.00007125882,0.007743452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.194991,"threshold_uncertainty_score":0.9998081,"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."}}