{"id":"W2911059574","doi":"10.1039/c8en00869h","title":"Efficient removal of both antimonite (Sb(<scp>iii</scp>)) and antimonate (Sb(<scp>v</scp>)) from environmental water using titanate nanotubes and nanoparticles","year":2019,"lang":"en","type":"article","venue":"Environmental Science Nano","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saskatoon Medical Imaging; University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Antimonate; Nanoparticle; Titanate; Adsorption; Absorption (acoustics); Materials science; Chemical engineering; Antimony; Nanotechnology; Chemistry; Metallurgy; Composite material; Physical chemistry; Ceramic","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":[0.00005878558,0.0002379799,0.0001102199,0.0001367511,0.0001299531,0.0001820206,0.0001854624,0.0003098241,0.0007304839],"category_scores_gemma":[0.0001184578,0.0001749402,0.0002029904,0.0001132395,0.00008900366,0.0001701648,0.0001804981,0.0001950756,0.0002743335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002724407,"about_ca_system_score_gemma":0.000196866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002589425,"about_ca_topic_score_gemma":0.007797219,"domain_scores_codex":[0.9999397,0.000004980267,0.000003507766,0.00001405444,0.00002578285,0.00001203652],"domain_scores_gemma":[0.9999709,0.000004345473,0.000005483681,0.000002854721,0.00001312802,0.000003296244],"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":[0.00002976123,0.000006320544,0.0001227216,0.00002633756,0.000003643587,0.00001074825,0.000008267903,0.0002243074,0.9981281,0.00004775871,0.00005680083,0.001335231],"study_design_scores_gemma":[0.000002785726,0.00003800455,0.0006345009,0.000001593607,0.000004182205,0.00001998677,0.00001237905,0.002185467,0.9966073,0.00001905933,0.0004725519,0.000002379531],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929162,0.0004135762,0.003964887,0.00006994797,0.00001642334,0.00001341099,0.0002375031,0.0001069769,0.002261042],"genre_scores_gemma":[0.9929899,0.0002939016,0.003842001,0.00002157887,0.000002045065,0.00001231921,0.0002031817,0.00001675124,0.002618291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002589425,"threshold_uncertainty_score":0.005148649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005260057678646117,"score_gpt":0.1928404424870244,"score_spread":0.1875803848083783,"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."}}