{"id":"W3160773735","doi":"10.1039/d1ra02335g","title":"Hydrodynamic synthesis of Fe<sub>2</sub>O<sub>3</sub>@MoS<sub>2</sub> 0D/2D-nanocomposite material and its application as a catalyst in the glycolysis of polyethylene terephthalate","year":2021,"lang":"en","type":"article","venue":"RSC Advances","topic":"Nanomaterials for catalytic reactions","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Korea Environmental Industry and Technology Institute; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Ministry of Environment; National Research Foundation","keywords":"Nanocomposite; Polyethylene terephthalate; Catalysis; Materials science; Polyethylene; Polymerization; Chemical engineering; Composite material; Polymer chemistry; Polymer; Chemistry; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004260973,0.0005690751,0.001029913,0.0002368487,0.0002043273,0.0001039433,0.0005859899,0.0003168611,0.0000254421],"category_scores_gemma":[0.0002261446,0.0005248949,0.0002901641,0.0007628888,0.0002580394,0.0006106254,0.0002604965,0.0002348133,0.00004944678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001957956,"about_ca_system_score_gemma":0.0001690771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001199258,"about_ca_topic_score_gemma":0.0002317369,"domain_scores_codex":[0.9963581,0.0001554416,0.001295477,0.0009780258,0.000622984,0.0005899957],"domain_scores_gemma":[0.9969677,0.0004889918,0.001028539,0.001175326,0.0001973926,0.000142002],"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":[0.0002420393,0.0003371502,0.0003904582,0.0007798704,0.0001955201,0.00004499825,0.0003360686,0.0001068676,0.9851543,0.00006119678,0.000006386298,0.01234518],"study_design_scores_gemma":[0.0006554037,0.00004577462,0.001159052,0.0003524154,0.000389114,0.0003014835,0.0003981832,0.0002459141,0.9953981,0.0004268524,0.0001405003,0.0004871818],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966273,0.001210839,0.00004369311,0.0001640285,0.0002246022,0.0005021237,0.0007385045,0.00009257349,0.0003963235],"genre_scores_gemma":[0.9968321,0.001836629,0.00009001255,0.00002458472,0.0001869239,0.0004437132,0.000480024,0.00009218381,0.00001380122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.011858,"threshold_uncertainty_score":0.9997203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005192677552498408,"score_gpt":0.2249315137514724,"score_spread":0.219738836198974,"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."}}