{"id":"W4213031761","doi":"10.1515/ntrev-2022-0055","title":"Polyethyleneimine-impregnated activated carbon nanofiber composited graphene-derived rice husk char for efficient post-combustion CO <sub>2</sub> capture","year":2022,"lang":"en","type":"article","venue":"Nanotechnology Reviews","topic":"Carbon Dioxide Capture Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Research Management Centre, Universiti Teknologi Malaysia; National Institute for Materials Science; Universiti Teknologi Malaysia; Mitacs; Ministerstvo Školství, Mládeže a Tělovýchovy; European Commission","keywords":"Char; Langmuir adsorption model; Adsorption; Electrospinning; Physisorption; Materials science; Nanofiber; Activated carbon; Specific surface area; Chemical engineering; Husk; Crystallinity; Graphene; Nuclear chemistry; Chemistry; Composite material; Combustion; Nanotechnology; Organic chemistry; Catalysis; Polymer","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005235935,0.0007787554,0.001273701,0.001098345,0.0002315615,0.00002898991,0.001002012,0.001012539,0.00001814827],"category_scores_gemma":[0.000295379,0.0007871272,0.0004320886,0.002133101,0.0002654502,0.00007155787,0.00032719,0.001418794,0.00001904946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005882935,"about_ca_system_score_gemma":0.00004674922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003122432,"about_ca_topic_score_gemma":0.00002116568,"domain_scores_codex":[0.9966528,0.0001884535,0.0009689155,0.0008725387,0.0003600043,0.0009572379],"domain_scores_gemma":[0.997871,0.0001570504,0.0003858893,0.001299656,0.0001864967,0.00009991498],"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":[0.00008109184,0.00009051646,0.00001356735,0.0002748065,0.000137036,0.00002092358,0.0001505592,0.004876138,0.9824514,0.0001925245,0.001099424,0.01061205],"study_design_scores_gemma":[0.001310282,0.0002375773,0.00005822863,0.0001277969,0.0001880513,0.0001354555,0.0002198813,0.005403661,0.9777622,0.00009530766,0.01364683,0.0008147695],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9510294,0.03663588,0.00102122,0.000990508,0.0008911589,0.003329882,0.000184942,0.005812712,0.0001043185],"genre_scores_gemma":[0.9931555,0.002803343,0.0008390116,0.0003114918,0.00003930293,0.002292286,0.0003479956,0.0001920298,0.00001900427],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04212616,"threshold_uncertainty_score":0.999458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01112815733438067,"score_gpt":0.2217754561542978,"score_spread":0.2106472988199171,"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."}}