{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004918937,0.0007284796,0.0007562008,0.0009825446,0.0007239108,0.0007189733,0.001951769,0.002015937,0.002275409],"category_scores_gemma":[0.001198516,0.0003063981,0.0009492302,0.0005472917,0.001966982,0.002253749,0.0008240714,0.001033231,0.0004919994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001539176,"about_ca_system_score_gemma":0.000700428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005852743,"about_ca_topic_score_gemma":0.002542776,"domain_scores_codex":[0.99976,0.00005152903,0.000009514129,0.00004469659,0.00007590005,0.00005837373],"domain_scores_gemma":[0.9995803,0.00024478,0.00005777071,0.00002471804,0.00004701619,0.00004536664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005929083,0.00009128135,0.0003886399,0.0001642132,0.00002084007,0.0003910609,0.0001818269,0.4741049,0.01714188,0.5027621,0.001749715,0.002944301],"study_design_scores_gemma":[0.000009624781,0.00001101309,0.00009222486,0.000005017969,0.000002668605,0.00004651278,0.00001026629,0.9662937,0.0003380305,0.03271784,0.0004638558,0.0000093011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.157953,0.003135527,0.7885374,0.004689024,0.0003524071,0.0003772399,0.0008701761,0.0006059377,0.04347932],"genre_scores_gemma":[0.9381643,0.002172067,0.03306716,0.000507246,0.0001938399,0.0004585852,0.0002795437,0.0001174582,0.02503978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005852743,"threshold_uncertainty_score":0.01163733,"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."}}