{"id":"W4411505190","doi":"10.1016/j.jpowsour.2025.237696","title":"Nanoscale X-ray tomographic imaging of liquid water in fuel cell electrode materials","year":2025,"lang":"en","type":"article","venue":"Journal of Power Sources","topic":"Fuel Cells and Related Materials","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ballard Power Systems (Canada); Simon Fraser University","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation; Ballard Power Systems","keywords":"Nanoscopic scale; Electrode; Fuel cells; X-ray; Materials science; Tomographic reconstruction; Tomography; Nanotechnology; Optics; Chemical engineering; Chemistry; Physics; Engineering; 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":[0.0001655059,0.0001742158,0.0001100695,0.000345892,0.0001410751,0.0003713801,0.0002404145,0.0003749277,0.001593521],"category_scores_gemma":[0.0003204531,0.0002547665,0.00008910743,0.0002405717,0.0002373778,0.0005441202,0.0002210789,0.0003098197,0.0002257125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002781407,"about_ca_system_score_gemma":0.0002069137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006884499,"about_ca_topic_score_gemma":0.001191671,"domain_scores_codex":[0.9999369,0.000009700591,0.00000450962,0.00001587292,0.00002441121,0.000008517255],"domain_scores_gemma":[0.9998841,0.00004913181,0.00002584528,0.0000130122,0.0000204382,0.000007434061],"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":[0.00003708797,0.000008919748,0.0003022629,0.00008223503,0.000002404391,0.00007806443,0.00003151719,0.0007119899,0.9949235,0.0003111166,0.0001165109,0.00339437],"study_design_scores_gemma":[0.00001230706,0.00007271707,0.004824977,0.00002553676,0.00001155143,0.0004902376,0.00008608741,0.01530142,0.9753478,0.0001655282,0.003650017,0.0000118845],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9140434,0.002543431,0.07510585,0.0004078728,0.00003176788,0.00007263312,0.000649503,0.0006364887,0.006508929],"genre_scores_gemma":[0.900997,0.001338632,0.09421965,0.00008630362,0.00001574332,0.00007635818,0.0003069671,0.0001132017,0.002846091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001593521,"threshold_uncertainty_score":0.005330861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.001821897358986694,"score_gpt":0.1774215840802207,"score_spread":0.175599686721234,"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."}}