{"id":"W4404485594","doi":"10.3390/engproc2024076083","title":"Valorization of Methane for Ethylene Production Through Oxidative Coupling: An Application of Density Functional Theory and Data Analytics in Catalyst Design for Improved Methane Conversion","year":2024,"lang":"en","type":"article","venue":"","topic":"Catalysis and Oxidation Reactions","field":"Chemical Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; University of Regina","keywords":"Oxidative coupling of methane; Methane; Catalysis; Ethylene; Density functional theory; Coupling (piping); Analytics; Production (economics); Environmental science; Oxidative phosphorylation; Chemistry; Chemical engineering; Computer science; Materials science; Process engineering; Waste management; Photochemistry; Organic chemistry; Database; Engineering; Computational chemistry; Composite material","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001307208,0.0001106018,0.0002373331,0.000151797,0.00002589174,0.00001023763,0.00009548084,0.00009454742,0.00000931609],"category_scores_gemma":[0.0005829752,0.0001030665,0.00004848718,0.0003979968,0.00004814253,0.0004980731,0.00004299604,0.00007429042,4.21061e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005637597,"about_ca_system_score_gemma":0.0000575029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001344256,"about_ca_topic_score_gemma":0.00004553387,"domain_scores_codex":[0.9989738,0.00002580525,0.0003733455,0.0004219956,0.0001086651,0.00009633216],"domain_scores_gemma":[0.998481,0.0006986379,0.0001374877,0.0003620092,0.0002898214,0.00003099752],"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":[0.0003079356,0.00008811384,0.00002705359,0.0002721265,0.0001254823,2.688578e-8,0.0001625032,0.01255074,0.9733315,0.01138697,0.00004147843,0.001706115],"study_design_scores_gemma":[0.0002466287,0.00003407337,0.00005086441,0.00001283434,0.0001694194,7.326068e-7,0.0002209918,0.4604454,0.5374001,0.001256102,0.0001012878,0.00006160812],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08687434,0.0001044442,0.9118761,0.0001148665,0.0001079618,0.0007266416,0.0001495019,0.00004087533,0.000005274458],"genre_scores_gemma":[0.9708439,0.0000549837,0.02413789,0.000007627273,0.0000840373,0.00008064579,0.004666995,0.00001937543,0.0001045285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8877382,"threshold_uncertainty_score":0.420293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06451300696543003,"score_gpt":0.3138901325137206,"score_spread":0.2493771255482906,"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."}}