{"id":"W4381433483","doi":"10.1021/acs.iecr.3c01358","title":"A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture","year":2023,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Carbon Dioxide Capture Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Bundesamt für Energie; Engineering and Physical Sciences Research Council; Total; Norges Forskningsråd; Equinor; Natural Environment Research Council; U.S. Department of Energy; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Horizon 2020 Framework Programme; UK Research and Innovation; Department for Business, Energy and Industrial Strategy, UK Government","keywords":"Adsorption; Carbon fibers; Chemical engineering; Materials science; Chemistry; Process engineering; Nanotechnology; Organic chemistry; Composite material; Engineering; Composite number","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":[0.002123792,0.001821798,0.001128663,0.00188146,0.0006644606,0.001770846,0.002385865,0.001349931,0.006108747],"category_scores_gemma":[0.003712846,0.00108452,0.001877613,0.0008614693,0.00073993,0.001216603,0.001533163,0.002463826,0.003046636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001162178,"about_ca_system_score_gemma":0.001736047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003918187,"about_ca_topic_score_gemma":0.004706344,"domain_scores_codex":[0.9988608,0.000129378,0.00008951244,0.0002347476,0.000581674,0.0001038071],"domain_scores_gemma":[0.998741,0.0003475535,0.0001359831,0.0003269855,0.0003773949,0.00007112611],"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":[0.000386362,0.0005892317,0.002727174,0.0009845627,0.0004460251,0.0006614052,0.0002819913,0.2918481,0.3981749,0.04004604,0.02264694,0.2412072],"study_design_scores_gemma":[0.00003378572,0.00008103659,0.0006893173,0.00002402518,0.00002247302,0.0001360248,0.0000308111,0.852533,0.1233954,0.01235688,0.0106059,0.00009117759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002423093,0.00005618621,0.9794838,0.00006139145,0.00001843044,0.00008050607,0.000627546,0.0168063,0.0004426084],"genre_scores_gemma":[0.06338987,0.0002383893,0.9296262,0.00009741942,0.00003201101,0.0006278468,0.002213386,0.002500959,0.00127396],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006108747,"threshold_uncertainty_score":0.02043587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1230636372649087,"score_gpt":0.3217258042437467,"score_spread":0.198662166978838,"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."}}