{"id":"W4214824388","doi":"10.1016/j.jcis.2022.02.137","title":"Surface modified silver/magnetite nanocomposite activating hydrogen peroxide for efficient degradation of chlorophenols","year":2022,"lang":"en","type":"article","venue":"Journal of Colloid and Interface Science","topic":"Advanced oxidation water treatment","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Collaborative Innovation Center for Water Treatment Technology and Materials; Suzhou University of Science and Technology; National Natural Science Foundation of China","keywords":"2,4-Dichlorophenol; Hydrogen peroxide; Catalysis; Chemistry; Phenol; Degradation (telecommunications); Chlorophenol; Nanocomposite; Magnetite; 4-Nitrophenol; Peroxide; Inorganic chemistry; Nuclear chemistry; Chemical engineering; Organic chemistry; Materials science; Metallurgy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007550902,0.0001041461,0.0001802341,0.00007947173,0.0004087476,0.00003564025,0.0003640142,0.00001374477,0.00009986641],"category_scores_gemma":[0.00006219177,0.00009047052,0.00005698927,0.0004212589,0.0002763391,0.0004365189,0.0002801632,0.0001001845,0.000001662374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003967911,"about_ca_system_score_gemma":0.00005508501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002505598,"about_ca_topic_score_gemma":0.000001749215,"domain_scores_codex":[0.99851,0.00004031747,0.0003961246,0.0002195467,0.0006275253,0.0002065077],"domain_scores_gemma":[0.9991126,0.00007872307,0.0005197006,0.0001284056,0.00006436514,0.00009620919],"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":[0.00007995081,0.00006174253,0.0003820694,0.000003512204,0.00000441273,1.877064e-7,0.0004945702,0.3476438,0.6504787,0.00001472388,0.00001347419,0.0008229113],"study_design_scores_gemma":[0.0006773935,0.0007236262,0.001669303,0.00002009449,0.00001210601,0.00003402424,0.0005672795,0.02057787,0.9748972,0.00009414829,0.0006273356,0.00009958658],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903358,0.0001104464,0.008719646,0.0001687016,0.0001336023,0.0002946371,0.00001090542,0.000005712163,0.0002205634],"genre_scores_gemma":[0.9882429,0.0000096178,0.01125844,0.00003019097,0.000008294091,0.000007223271,5.848228e-7,0.000007296154,0.000435469],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3270659,"threshold_uncertainty_score":0.3689281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01189199018185721,"score_gpt":0.2521162068898893,"score_spread":0.2402242167080321,"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."}}