{"id":"W4412511123","doi":"10.1149/ma2025-01381911mtgabs","title":"Investigating the Degradation of Porous Transport Layer (PTL) Materials in Proton Exchange Membrane Water Electrolyzer via in-Operando Distribution of Relaxation Times Approach","year":2025,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Fuel Cells and Related Materials","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Université du Québec à Trois-Rivières","funders":"","keywords":"Relaxation (psychology); Electrolysis; Degradation (telecommunications); Materials science; Porosity; Chemical engineering; Membrane; Proton exchange membrane fuel cell; Layer (electronics); Chemical physics; Chemistry; Nanotechnology; Composite material; Electrode; Physical chemistry; Electrolyte; Electrical engineering","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":[0.0004364737,0.000367216,0.0003347879,0.000394661,0.000189473,0.000386777,0.0003873258,0.0005055491,0.0006604138],"category_scores_gemma":[0.0005605294,0.0001818556,0.0002688899,0.0003337764,0.0003717415,0.0007154812,0.0002359558,0.0007936606,0.0002047906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000372207,"about_ca_system_score_gemma":0.0001221758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003814492,"about_ca_topic_score_gemma":0.0005331531,"domain_scores_codex":[0.9996777,0.00002533237,0.00001955194,0.00009422022,0.0001509924,0.00003226911],"domain_scores_gemma":[0.9996283,0.0001105764,0.0001340484,0.00002514509,0.00008723768,0.00001469695],"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":[0.00007742814,0.00006064748,0.0006668697,0.000149632,0.00001116773,0.00009193434,0.00008333145,0.0002380941,0.993223,0.0001301447,0.0000924529,0.005175434],"study_design_scores_gemma":[0.000003570478,0.0003054676,0.003236659,0.000007461357,0.00001721228,0.000144019,0.00007997758,0.003885725,0.9912811,0.00006512173,0.0009618766,0.00001192645],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9760597,0.003188879,0.01804436,0.0002230735,0.00006910884,0.00006741856,0.0005855029,0.0002496633,0.001512201],"genre_scores_gemma":[0.9856434,0.00170546,0.01093558,0.000103284,0.00002649609,0.00006916267,0.0001937711,0.00004095229,0.001281768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006604138,"threshold_uncertainty_score":0.002700508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008101114490444995,"score_gpt":0.2014072145356332,"score_spread":0.1933061000451882,"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."}}