{"id":"W3213637832","doi":"10.1016/j.jece.2021.106741","title":"Efficiency and mechanisms of Sb(III/V) removal by Fe-modified biochars using X-ray absorption spectroscopy","year":2021,"lang":"en","type":"article","venue":"Journal of environmental chemical engineering","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Canadian Light Source (Canada)","funders":"Fundamental Research Funds for the Central Universities; China University of Geosciences, Wuhan; Argonne National Laboratory; National Natural Science Foundation of China","keywords":"X-ray absorption spectroscopy; Antimony; Aqueous solution; Coprecipitation; Chemistry; Adsorption; Absorption (acoustics); Isothermal process; Biochar; Analytical Chemistry (journal); Nuclear chemistry; Pyrolysis; Spectroscopy; Langmuir adsorption model; Extended X-ray absorption fine structure; Absorption spectroscopy; Inorganic chemistry; Materials science; Physical chemistry; Chromatography; 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":[0.0002670904,0.0002872816,0.0002196918,0.0002822388,0.0001800112,0.0004497424,0.0004407657,0.0004296573,0.0004680154],"category_scores_gemma":[0.0002078797,0.0002528466,0.000408907,0.0001513196,0.0002160557,0.0002568509,0.0001815796,0.0002889368,0.000228224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004481633,"about_ca_system_score_gemma":0.0001357756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00232581,"about_ca_topic_score_gemma":0.001999474,"domain_scores_codex":[0.9998426,0.00001926231,0.00001531537,0.00003204784,0.00005031495,0.00004045303],"domain_scores_gemma":[0.9999171,0.00002158298,0.0000160438,0.00001007579,0.00003005171,0.000005086095],"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":[0.00008825959,0.00001755659,0.000414348,0.00004086634,0.000009634266,0.00001851651,0.00001661621,0.0003294896,0.9971438,0.00009527759,0.00002421835,0.001801486],"study_design_scores_gemma":[0.000001577263,0.00002967161,0.00111407,0.000002120978,0.000007246253,0.000016033,0.000009484575,0.001421111,0.9971809,0.00002434079,0.0001905089,0.000003011751],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928669,0.001163479,0.004707314,0.00005232429,0.00001569518,0.00001492087,0.0001322186,0.00008288112,0.0009642348],"genre_scores_gemma":[0.9968611,0.0004230883,0.001497632,0.00001497967,0.000002852917,0.000006470864,0.00009900743,0.000008402832,0.001086359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00232581,"threshold_uncertainty_score":0.004624605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004481698577949764,"score_gpt":0.1913330650848711,"score_spread":0.1868513665069214,"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."}}