{"id":"W4322496008","doi":"10.1021/acsnano.2c10384","title":"Sorption–Deformation–Percolation Model for Diffusion in Nanoporous Media","year":2023,"lang":"en","type":"article","venue":"ACS Nano","topic":"Diffusion Coefficients in Liquids","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Southern University of Science and Technology; Khalifa University of Science, Technology and Research; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Percolation (cognitive psychology); Nanoporous; Diffusion; Porous medium; Sorption; Materials science; Tortuosity; Percolation theory; Deformation (meteorology); Thermal diffusivity; Chemical physics; Fragility; Percolation threshold; Diffusion process; Statistical physics; Porosity; Thermodynamics; Nanotechnology; Adsorption; Physical chemistry; Chemistry; Physics; Conductivity; Composite material; Computer science; Electrical resistivity and conductivity","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002947819,0.0001454316,0.0001608977,0.0002152095,0.0001312379,0.00003709341,0.0002405361,0.0001883103,0.0001855384],"category_scores_gemma":[0.0003718333,0.0001422057,0.00005858553,0.0004343107,0.00003189156,0.0002411033,0.0001270964,0.00009786596,0.0002102436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001506009,"about_ca_system_score_gemma":0.00006383027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009694306,"about_ca_topic_score_gemma":0.00003314203,"domain_scores_codex":[0.9986735,0.00001085328,0.0003884172,0.0002608833,0.0003462633,0.0003200583],"domain_scores_gemma":[0.9992512,0.0001829834,0.00009297638,0.0003196129,0.00009427597,0.00005893253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001352063,0.0004061387,0.002379234,0.0002490339,0.00001355281,0.000002908987,0.008301165,0.03382511,0.9154754,0.004082198,0.01069458,0.02443548],"study_design_scores_gemma":[0.001685679,0.00001716042,0.000798943,0.00008584368,0.00001208537,0.000002319477,0.0004571282,0.958589,0.03018597,0.003616764,0.004257582,0.0002915943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923672,0.00001267987,0.003243256,0.0002076464,0.0003171158,0.0001899619,0.00006164748,0.0002966586,0.00330382],"genre_scores_gemma":[0.9927526,0.00003815418,0.0005764504,0.0001027462,0.0001232025,0.0001599361,0.0005131426,0.00003450159,0.00569924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9247638,"threshold_uncertainty_score":0.5798982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0241414415347915,"score_gpt":0.2691467009046669,"score_spread":0.2450052593698754,"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."}}