{"id":"W3006505164","doi":"10.1021/acs.energyfuels.9b03442","title":"Application of Core-Shell-Structured K<sub>2</sub>CO<sub>3</sub>-Based Sorbents in Postcombustion CO<sub>2</sub> Capture: Statistical Analysis and Optimization Using Response Surface Methodology","year":2020,"lang":"en","type":"article","venue":"Energy & Fuels","topic":"Carbon Dioxide Capture Technologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Boehmite; Carbonation; Sorbent; Thermogravimetric analysis; Materials science; Core (optical fiber); Response surface methodology; Chemical engineering; Shell (structure); Moisture; Chemistry; Composite material; Chromatography; Engineering; Organic 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006873708,0.0005517958,0.001019578,0.000761283,0.00005755294,0.00005028904,0.0003122162,0.000694776,0.000006667576],"category_scores_gemma":[0.0008716389,0.0006309708,0.0001786618,0.002012971,0.0002805805,0.0001905362,0.0000928818,0.0004816351,0.000002597015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003473391,"about_ca_system_score_gemma":0.00009513189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001042635,"about_ca_topic_score_gemma":0.0002156233,"domain_scores_codex":[0.9966739,0.0005675138,0.0009248981,0.0008134354,0.0004672675,0.0005529889],"domain_scores_gemma":[0.9976886,0.0009794846,0.0003407506,0.0006096753,0.0001776222,0.0002038801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003504325,0.00001743356,0.0007449419,0.00005824742,0.0001425865,0.00001620481,0.00008677466,0.4722035,0.52435,0.0002083663,0.00003186171,0.001789614],"study_design_scores_gemma":[0.0006724699,0.00005824509,0.005441881,0.00001977517,0.0003087398,0.000008038378,0.00006981305,0.3216096,0.6711355,0.0003053882,0.000008793448,0.0003617315],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5429977,0.0003015558,0.4558805,0.00009431986,0.00006966646,0.0002018498,0.0001213405,0.0003284975,0.000004631112],"genre_scores_gemma":[0.9553907,0.000190364,0.04360851,0.0001600785,0.00004196271,0.00003757339,0.0004633488,0.0001071148,3.51147e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.412393,"threshold_uncertainty_score":0.9996142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02329020831458783,"score_gpt":0.2587506454756545,"score_spread":0.2354604371610667,"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."}}