{"id":"W3005188418","doi":"10.1021/acsaem.9b02371","title":"Designing Tailored Gas Diffusion Layers with Pore Size Gradients via Electrospinning for Polymer Electrolyte Membrane Fuel Cells","year":2020,"lang":"en","type":"article","venue":"ACS Applied Energy Materials","topic":"Fuel Cells and Related Materials","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Karlsruhe House of Young Scientists; University of Toronto; Bundesministerium für Bildung und Forschung; Natural Sciences and Engineering Research Council of Canada; Queen's University; Canada Research Chairs; Deutscher Akademischer Austauschdienst; Canada Foundation for Innovation","keywords":"Electrolyte; Electrospinning; Materials science; Proton exchange membrane fuel cell; Chemical engineering; Polymer; Membrane; Composite material; Water transport; Gaseous diffusion; Chemistry; Water flow; Fuel cells; Electrode","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":[0.0001079108,0.0003436846,0.0001221564,0.0001713515,0.0001022404,0.000237211,0.0003048489,0.0002467511,0.0004094812],"category_scores_gemma":[0.0002136843,0.0001379867,0.0001369369,0.0001121689,0.0001936846,0.0004727095,0.000278963,0.0003530126,0.0001814828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002622973,"about_ca_system_score_gemma":0.0001249436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002802925,"about_ca_topic_score_gemma":0.0009666014,"domain_scores_codex":[0.9999321,0.000005090995,0.000006983755,0.00002080088,0.00002130448,0.00001363012],"domain_scores_gemma":[0.999911,0.00002205055,0.00003032957,0.000008561336,0.00001529411,0.00001292867],"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":[0.00000668671,0.00001044289,0.00006267441,0.00002792567,0.000002426315,0.00001949379,0.00001186886,0.0004353336,0.9966797,0.0002217881,0.00002516996,0.002496627],"study_design_scores_gemma":[0.000009987316,0.00004762646,0.0003681273,0.000003124133,0.000003563835,0.0000359901,0.00000889953,0.004232016,0.9937899,0.00009453326,0.001399383,0.000006928602],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9490145,0.00125104,0.04595827,0.0001651649,0.00005269351,0.00008595932,0.0001457267,0.0004078292,0.002918759],"genre_scores_gemma":[0.9674534,0.0005938442,0.03086536,0.00006762813,0.00001286861,0.00006229167,0.00006545816,0.00003710171,0.000842178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004094812,"threshold_uncertainty_score":0.001903057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004642670640028778,"score_gpt":0.1622707822218924,"score_spread":0.1576281115818636,"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."}}