{"id":"W2041553158","doi":"10.1016/j.jpowsour.2010.05.029","title":"In situ quantification of the in-plane water content in the Nafion® membrane of an operating polymer-electrolyte membrane fuel cell using 1H micro-magnetic resonance imaging experiments","year":2010,"lang":"en","type":"article","venue":"Journal of Power Sources","topic":"Fuel Cells and Related Materials","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"University of Alberta; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Government of Canada","keywords":"Nafion; Proton exchange membrane fuel cell; Calibration curve; Electrolyte; Calibration; Analytical Chemistry (journal); Membrane; Water transport; Relaxation (psychology); Chemistry; Materials science; Nuclear magnetic resonance; Electrode; Water flow; Chromatography; Detection limit; Electrochemistry; Physics; Environmental science","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":[0.0007280633,0.0001303373,0.0002775644,0.0001581209,0.00002933556,0.00003994472,0.0003120611,0.00007235369,0.00005118387],"category_scores_gemma":[0.00001295189,0.00007198999,0.00005584862,0.0001295085,0.00005563324,0.0001718223,0.00002287282,0.0003620417,7.656247e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002137364,"about_ca_system_score_gemma":0.00001664827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002167925,"about_ca_topic_score_gemma":0.00005963334,"domain_scores_codex":[0.9986253,0.0001230063,0.0007352449,0.0000966001,0.000209697,0.000210116],"domain_scores_gemma":[0.999511,0.00004233247,0.0001944371,0.0001811223,0.00004620001,0.00002491237],"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":[0.00003972207,0.00008487447,0.0006628403,0.0001202098,0.000004653262,0.00001479285,0.007190529,0.00403993,0.9878041,0.000003062444,0.00000220623,0.0000330395],"study_design_scores_gemma":[0.0007807836,0.00004942806,0.001846827,0.0001636434,0.00001150653,0.0000670525,0.001224647,0.001609983,0.9940076,0.000008415755,0.0001417987,0.00008834667],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881856,0.01068582,0.000002869149,0.0001132146,0.0005059849,0.000126644,0.000002066062,0.000003270713,0.0003745338],"genre_scores_gemma":[0.9994782,0.0002196908,0.0001820837,0.0000373656,0.00004203954,0.000001484577,8.502208e-7,0.00001875116,0.00001948634],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01129265,"threshold_uncertainty_score":0.2935667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01025439820651669,"score_gpt":0.2201529429256159,"score_spread":0.2098985447190992,"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."}}