{"id":"W2397956406","doi":"10.1039/c6ta02809h","title":"Toward highly efficient in situ dry reforming of H<sub>2</sub>S contaminated methane in solid oxide fuel cells via incorporating a coke/sulfur resistant bimetallic catalyst layer","year":2016,"lang":"en","type":"article","venue":"Journal of Materials Chemistry A","topic":"Catalysts for Methane Reforming","field":"Chemical Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Innovates; National Institute for Nanotechnology; Alberta Hospital Edmonton; University of Alberta","funders":"Universiteit van Amsterdam; Climate Change and Emissions Management Corporation","keywords":"Bimetallic strip; Coke; Methane; Catalysis; Sulfur; Materials science; Oxide; In situ; Methane reformer; Carbon dioxide reforming; Contamination; Chemical engineering; Layer (electronics); Waste management; Chemistry; Metallurgy; Syngas; Nanotechnology; Steam reforming; Organic chemistry; Engineering; Hydrogen production","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001114264,0.0003341429,0.0002679624,0.0001346345,0.0001736085,0.0002106224,0.0002547374,0.0002736194,0.0006768823],"category_scores_gemma":[0.0001295033,0.0001316043,0.0001457226,0.0001198529,0.000247748,0.0002814133,0.0002644713,0.000306918,0.0001752656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001767515,"about_ca_system_score_gemma":0.0001637341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007193804,"about_ca_topic_score_gemma":0.002010513,"domain_scores_codex":[0.999928,0.000006690684,0.000004506458,0.00001461132,0.00002743046,0.00001870171],"domain_scores_gemma":[0.9999599,0.000007998011,0.00001057274,0.000005199865,0.000009392135,0.000006785034],"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":[0.00004092642,0.00000930587,0.0001181256,0.00002928346,0.000003932229,0.00002628823,0.00001844326,0.00009120723,0.9979522,0.000108882,0.00002416177,0.001577386],"study_design_scores_gemma":[0.000001953172,0.00004399647,0.0001931841,4.136488e-7,0.000002195801,0.00001819006,0.00000669212,0.0004091285,0.9989702,0.000008118141,0.0003446762,0.000001332321],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925814,0.0003986685,0.00562183,0.00009235037,0.00002967767,0.00001800555,0.0000703597,0.0001343313,0.001053408],"genre_scores_gemma":[0.9949239,0.0001760583,0.003910024,0.00001607282,0.000005057754,0.000005556779,0.00004860839,0.00001276576,0.0009019806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007193804,"threshold_uncertainty_score":0.00226438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01148879521250421,"score_gpt":0.2265544335970938,"score_spread":0.2150656383845896,"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."}}