{"id":"W4387324747","doi":"10.1016/j.apenergy.2023.122004","title":"Predicting PEMFC performance from a volumetric image of catalyst layer structure using pore network modeling","year":2023,"lang":"en","type":"article","venue":"Applied Energy","topic":"Electrocatalysts for Energy Conversion","field":"Energy","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canarie; U.S. Department of Energy; Office of Energy Efficiency and Renewable Energy; National Science Foundation","keywords":"Ionomer; Knudsen diffusion; Materials science; Proton exchange membrane fuel cell; Porosity; Polarization (electrochemistry); Catalysis; Chemical engineering; Thermal diffusivity; Composite material; Analytical Chemistry (journal); Chemistry; Thermodynamics; Chromatography; Physical chemistry","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":[0.00007308194,0.0003432746,0.0001892199,0.0002155779,0.0001381481,0.0002657016,0.0003491953,0.0005636899,0.0005154371],"category_scores_gemma":[0.0004115516,0.0001863945,0.0002424741,0.000259198,0.0001261915,0.0004003188,0.0001007342,0.0002800795,0.0001555718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005696198,"about_ca_system_score_gemma":0.0003303316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01089341,"about_ca_topic_score_gemma":0.008475192,"domain_scores_codex":[0.9999727,0.000002127442,0.000001139714,0.000006746625,0.00001248575,0.000004659571],"domain_scores_gemma":[0.9999067,0.00005678884,0.000008184337,0.000006065662,0.00001796761,0.000004304493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005854984,0.00003811346,0.001716841,0.00005192341,0.00001885821,0.00007355375,0.0000177688,0.9330088,0.05443807,0.0005566066,0.0002119373,0.009808874],"study_design_scores_gemma":[0.000001506085,0.000004776133,0.0003116627,7.381827e-7,0.000002237229,0.000005713078,0.000002356647,0.9930467,0.006477735,0.0000951097,0.0000497047,0.000001718049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.909431,0.0002605224,0.08411695,0.0001323461,0.00001642268,0.00002985427,0.0006819755,0.0008070958,0.004523823],"genre_scores_gemma":[0.9951606,0.00008202283,0.004234802,0.000006042377,0.000002321084,0.000009672465,0.0001307794,0.000015222,0.0003584669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01089341,"threshold_uncertainty_score":0.02165997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01268191980489968,"score_gpt":0.2106404036302434,"score_spread":0.1979584838253437,"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."}}