{"id":"W4406929503","doi":"10.26434/chemrxiv-2025-wncqj","title":"Identification of metal-organic frameworks for near practical energy limit CO2 capture from wet flue gases: an integrated atomistic and process simulation screening of experimental MOFs","year":2025,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Catalytic Processes in Materials Science","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Ottawa","funders":"Total; Natural Sciences and Engineering Research Council of Canada; Mitacs; Alliance de recherche numérique du Canada; University of Ottawa","keywords":"Flue gas; Metal-organic framework; Process (computing); Limit (mathematics); Identification (biology); Materials science; Energy (signal processing); Process engineering; Nanotechnology; Environmental science; Computer science; Engineering; Waste management; Chemistry; Adsorption; Physics; 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":[0.000359435,0.0003793285,0.0003769897,0.0003105092,0.0004059234,0.0004349482,0.00045364,0.000447029,0.0007738578],"category_scores_gemma":[0.0007098179,0.0002461479,0.0005189414,0.0002360525,0.0002180596,0.0003579869,0.0003120679,0.0003508262,0.00008918335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006651715,"about_ca_system_score_gemma":0.00074384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004926595,"about_ca_topic_score_gemma":0.007199324,"domain_scores_codex":[0.9999098,0.0000119216,0.000003456595,0.00001287285,0.00003667995,0.00002528375],"domain_scores_gemma":[0.9998336,0.00009001105,0.00001438209,0.00001614685,0.0000322738,0.00001350812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003446198,0.0002334133,0.0118115,0.0002996928,0.0001016826,0.0004178256,0.0001471009,0.8392628,0.1293678,0.003762884,0.0004508451,0.01379981],"study_design_scores_gemma":[0.0000295219,0.0001635794,0.001997319,0.00000803873,0.00002254103,0.00003849997,0.00006309717,0.9690742,0.02756203,0.0004973431,0.0005310622,0.00001278884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991557,0.0001642063,0.006538059,0.00004766861,0.000004966463,0.00003047918,0.0002752447,0.00008589003,0.001296422],"genre_scores_gemma":[0.9898255,0.0001167087,0.009514528,0.00001259776,0.000001504922,0.00005040965,0.000256171,0.00001728105,0.0002052093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004926595,"threshold_uncertainty_score":0.009795845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02676768615034671,"score_gpt":0.3326762227483236,"score_spread":0.305908536597977,"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."}}