{"id":"W4385334570","doi":"10.1021/acs.iecr.3c00192","title":"Syngas Quality Enhancement by CO<sub>2</sub> Injection during the Co-Gasification of Biomass and Plastic","year":2023,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; Western University","funders":"Mitacs; Western University","keywords":"Syngas; tar (computing); Wood gas generator; Waste management; High-density polyethylene; Biomass (ecology); Thermogravimetric analysis; Char; Raw material; Producer gas; Materials science; Carbon fibers; Solid fuel; Environmental science; Fuel gas; Chemical engineering; Combustion; Pulp and paper industry; Polyethylene; Chemistry; Pyrolysis; Organic chemistry; Composite number; Catalysis; Coal; Composite material; Engineering","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":[0.0001746922,0.0004316015,0.0002513557,0.0001495737,0.0001033102,0.0002629584,0.0001653693,0.0002125642,0.0006676392],"category_scores_gemma":[0.0001678546,0.0001299997,0.0001635721,0.0001802178,0.0002946828,0.0002979019,0.0001953839,0.0001886386,0.0001486041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000174699,"about_ca_system_score_gemma":0.0001454207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003271991,"about_ca_topic_score_gemma":0.001019681,"domain_scores_codex":[0.9999015,0.00002279073,0.000004362606,0.00001560802,0.00003086427,0.00002481966],"domain_scores_gemma":[0.9999,0.000028825,0.0000311396,0.000009543689,0.00002138951,0.00000922916],"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":[0.0001442084,0.00001200581,0.0005906476,0.00004756891,0.00000526741,0.00004916183,0.00001125498,0.0002307862,0.9969291,0.00004068688,0.000007571483,0.001931927],"study_design_scores_gemma":[0.000001603137,0.00007815912,0.001348144,0.000001026846,0.000004616727,0.00003302053,0.000009942959,0.0004550159,0.9979055,0.000009863325,0.000151745,0.000001378415],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937039,0.0005318208,0.004858943,0.00002172469,0.00001281169,0.00002062198,0.0000521001,0.00005986839,0.0007382086],"genre_scores_gemma":[0.9975561,0.000218413,0.001701554,0.000009736147,0.000002733231,0.000004648709,0.00002916598,0.00001204643,0.0004655446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006676392,"threshold_uncertainty_score":0.002233446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05179549517240979,"score_gpt":0.3119318032236423,"score_spread":0.2601363080512325,"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."}}