{"id":"W4389334040","doi":"10.1016/j.jcou.2023.102635","title":"Integrated kinetics-computational fluid dynamic-optimization for catalytic hydrogenation of CO2 to formic acid","year":2023,"lang":"en","type":"article","venue":"Journal of CO2 Utilization","topic":"Carbon dioxide utilization in catalysis","field":"Chemical Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Information Technology Research Centre; Ministry of Science and ICT, South Korea; Ministry of Education and Human Resources Development; Ministry of Trade, Industry and Energy; Iran Telecommunication Research Center; National Research Foundation of Korea; Korea Institute of Energy Technology Evaluation and Planning; Ministry of Science, ICT and Future Planning; Chung-Ang University","keywords":"Formic acid; Catalysis; Chemistry; Computational fluid dynamics; Methanation; Chemical kinetics; Work (physics); Chemical engineering; Materials science; Process engineering; Kinetics; Organic chemistry; Thermodynamics; Physics; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004146165,0.000185708,0.0003631361,0.0009395905,0.00004406597,0.00003425423,0.0002323134,0.0001356219,0.0000608502],"category_scores_gemma":[0.001366472,0.0001919359,0.0001975363,0.001923056,0.0000318592,0.0003113885,0.00003980378,0.0001033959,0.00001175593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003097844,"about_ca_system_score_gemma":0.0001157734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003346868,"about_ca_topic_score_gemma":0.000006146832,"domain_scores_codex":[0.9978756,0.00002886569,0.001118209,0.0001927665,0.0005716519,0.0002128537],"domain_scores_gemma":[0.9972324,0.0002183679,0.0006045877,0.0001836115,0.001640004,0.0001209801],"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":[0.00009636729,0.00005060019,0.00007988678,0.00009208114,0.00007662023,0.000001066078,0.0002391979,0.8653876,0.1312431,0.0004579441,0.0004407681,0.001834715],"study_design_scores_gemma":[0.0007588214,0.000114836,0.000182027,0.0001027567,0.0001078377,0.00001466045,0.0001517462,0.854056,0.1435936,0.0004313346,0.0003423842,0.0001439606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2267656,0.00007118188,0.7721661,0.0002021327,0.0002806519,0.0002969725,0.00006998288,0.00007732696,0.00007002991],"genre_scores_gemma":[0.9868031,0.00003946347,0.01170745,0.00005027492,0.00007214965,0.00001903354,0.001140192,0.00005517248,0.0001131746],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7604587,"threshold_uncertainty_score":0.7826917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02149827128465252,"score_gpt":0.2797969712970155,"score_spread":0.258298700012363,"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."}}