{"id":"W3039779779","doi":"10.1016/j.gee.2020.06.025","title":"Novel magnetic carbon supported molybdenum disulfide catalyst and its application in residue upgrading","year":2020,"lang":"en","type":"article","venue":"Green Energy & Environment","topic":"Catalysis and Hydrodesulfurization Studies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Beijing Municipality; Youth Innovation Promotion Association of the Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"X-ray photoelectron spectroscopy; Materials science; Nanoflower; Molybdenum disulfide; High-resolution transmission electron microscopy; Catalysis; Superparamagnetism; Chemical engineering; Molybdenum; Nanomaterial-based catalyst; Hydrothermal circulation; Coke; Transmission electron microscopy; Nanoparticle; Magnetization; Nanotechnology; Chemistry; Organic chemistry; Metallurgy; Nanostructure; Magnetic field","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004580701,0.0001576084,0.0001979507,0.00006888729,0.00003388276,0.00001075687,0.00007835035,0.00004928309,0.00001909959],"category_scores_gemma":[0.000003783294,0.0001726383,0.00002825094,0.0001550348,0.00002339346,0.00004980839,0.00006108851,0.00006610564,0.000009554807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005318037,"about_ca_system_score_gemma":0.000002886941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007055238,"about_ca_topic_score_gemma":0.0004666401,"domain_scores_codex":[0.9991258,0.000008652834,0.000251969,0.0002828942,0.0001516926,0.0001789485],"domain_scores_gemma":[0.9997019,0.00001074081,0.00003343816,0.0001580331,0.000003197785,0.00009266343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002589914,0.00009150477,0.01860409,0.0001289275,0.0001588836,0.00003009432,0.001888759,0.2764619,0.6788136,0.0007040108,0.00009743139,0.02299489],"study_design_scores_gemma":[0.001558667,0.00008016548,0.223051,0.00002964179,0.0001662788,0.00001279356,0.0001922645,0.6686616,0.09105465,0.00008258384,0.01423001,0.0008803626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879227,0.003231857,0.006883083,0.0008544737,0.00002644743,0.0001763177,0.00003427882,0.0001130361,0.0007578416],"genre_scores_gemma":[0.9987129,0.0008025951,0.00005744691,0.00009395005,0.0000638463,0.00005121025,0.00009455591,0.00002564067,0.00009784546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.587759,"threshold_uncertainty_score":0.7039987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008622249894272058,"score_gpt":0.1703280603639095,"score_spread":0.1617058104696375,"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."}}