{"id":"W3016901786","doi":"10.1039/c9ra10437b","title":"Co<sub>3</sub>O<sub>4</sub>–Ag photocatalysts for the efficient degradation of methyl orange","year":2020,"lang":"en","type":"article","venue":"RSC Advances","topic":"Copper-based nanomaterials and applications","field":"Materials Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"North University of China; Natural Science Foundation of Shanxi Province; Shanxi Scholarship Council of China","keywords":"Methyl orange; Degradation (telecommunications); Orange (colour); Chemistry; Nuclear chemistry; Materials science; Photochemistry; Photocatalysis; Organic chemistry; Food science; Computer science; Catalysis; Telecommunications","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":[0.0004219073,0.0002067107,0.0003230964,0.00004350258,0.000290673,0.00007231725,0.0003989973,0.00005911779,0.00003653706],"category_scores_gemma":[0.000154375,0.0001501396,0.0001191793,0.0003430958,0.0001744456,0.0001773195,0.0000542709,0.00005425271,0.0001127863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003711481,"about_ca_system_score_gemma":0.00007962628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001192699,"about_ca_topic_score_gemma":0.00002716186,"domain_scores_codex":[0.9984043,0.0000682371,0.0004634954,0.000448053,0.0003143361,0.0003016183],"domain_scores_gemma":[0.9986069,0.0004448324,0.0003392078,0.0003694703,0.0001370868,0.0001025494],"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":[0.0001133206,0.00007163556,0.0000171127,0.00010973,0.000009146128,5.148477e-7,0.0001906116,0.001069854,0.9832385,0.0003629077,0.0003886324,0.0144281],"study_design_scores_gemma":[0.0004381561,0.0001211785,0.0001394697,0.00003071553,0.00005648401,0.00000143693,0.0001720701,0.001267211,0.9891867,0.0001596368,0.008242783,0.0001841702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9741263,0.001468008,0.02125042,0.0009724938,0.0002850032,0.00128249,0.0004658394,0.00009692478,0.00005249429],"genre_scores_gemma":[0.9976542,0.0002554642,0.0009909398,0.0002284099,0.0001865764,0.0005691553,0.00008419615,0.00002699327,0.000004048797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02352789,"threshold_uncertainty_score":0.6122515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02443175573787247,"score_gpt":0.2764587347079287,"score_spread":0.2520269789700562,"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."}}