{"id":"W4393853623","doi":"10.1016/j.ijhydene.2024.03.174","title":"Performance assessment of a novel porous catalytic reactor with a hydrogen-selective membrane for enhanced methane dry reforming process – CFD investigation","year":2024,"lang":"en","type":"article","venue":"International Journal of Hydrogen Energy","topic":"Catalysts for Methane Reforming","field":"Chemical Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Carbon dioxide reforming; Methane; Membrane reactor; Hydrogen; Catalysis; Porosity; Methane reformer; Chemical engineering; Process (computing); Membrane; Computational fluid dynamics; Hydrogen production; Dry gas; Chemistry; Materials science; Steam reforming; Environmental science; Chromatography; Syngas; Computer science; Thermodynamics; Engineering; Organic chemistry; Physics","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":[0.0003273152,0.0002640731,0.0005195031,0.000212322,0.0003357154,0.0004643677,0.0005391016,0.0006287184,0.0007527748],"category_scores_gemma":[0.0004070557,0.0001843799,0.0004021379,0.0001751667,0.0002919398,0.0004281827,0.0002662691,0.0002386084,0.0001619547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004670804,"about_ca_system_score_gemma":0.0004536047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002838818,"about_ca_topic_score_gemma":0.002158922,"domain_scores_codex":[0.9998654,0.00001281855,0.000009551736,0.00003184474,0.00005887425,0.00002144364],"domain_scores_gemma":[0.9998591,0.00005615728,0.00001872067,0.00001345875,0.00004094149,0.00001172472],"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":[0.000630643,0.00009355087,0.001428501,0.000142228,0.00002047059,0.0001327007,0.00003844588,0.0117856,0.9780347,0.0002986382,0.000103118,0.007291324],"study_design_scores_gemma":[0.00005432008,0.000613328,0.004893838,0.000006992562,0.00004013578,0.0001132141,0.00005283866,0.1262147,0.8667769,0.00007082863,0.001143919,0.00001906358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895857,0.0002925944,0.008958544,0.00006505221,0.00001685188,0.00001921015,0.0001317584,0.0001141564,0.0008162578],"genre_scores_gemma":[0.9957165,0.0001332813,0.00356163,0.000006910909,0.000003563965,0.000009946414,0.00006862561,0.000007626311,0.0004918755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002838818,"threshold_uncertainty_score":0.00564456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01300666826187164,"score_gpt":0.2792809208384063,"score_spread":0.2662742525765346,"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."}}