{"id":"W4410912081","doi":"10.18331/brj2025.12.2.3","title":"Biochar-supported highly dispersed ultrasmall Cu/ZnO nanoparticles as a highly efficient novel catalyst for CO2 hydrogenation to methanol","year":2025,"lang":"en","type":"article","venue":"Biofuel Research Journal","topic":"Catalysts for Methane Reforming","field":"Chemical Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Regional Development Fund; Generalitat de Catalunya; European Commission; Ministerio de Ciencia e Innovación; Institut Català de Nanociència i Nanotecnologia","keywords":"Biochar; Catalysis; Methanol; Nanoparticle; Materials science; Chemical engineering; Nanotechnology; Chemistry; Organic chemistry; Pyrolysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.003363658,0.0003324084,0.0004569131,0.001129579,0.0005100652,0.0002067352,0.000817142,0.0002280176,0.00007687335],"category_scores_gemma":[0.004259612,0.0002827271,0.0003396396,0.001927634,0.0001395077,0.0002060783,0.0002614779,0.0008209368,0.0002271593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001019639,"about_ca_system_score_gemma":0.0005529479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002693515,"about_ca_topic_score_gemma":0.00003324756,"domain_scores_codex":[0.9957079,0.0001037013,0.0008373455,0.0005946357,0.001369086,0.00138738],"domain_scores_gemma":[0.9969641,0.0006542048,0.0001501978,0.0005230852,0.0009698526,0.0007385724],"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":[0.0003326814,0.0002882382,0.00005830986,0.0001677515,0.0002334022,0.00002828626,0.0004117156,0.000533413,0.9920978,0.001926647,0.0002514412,0.003670321],"study_design_scores_gemma":[0.001954848,0.0001881853,0.00007429201,0.0002328058,0.00007094979,0.0001124047,0.0005044936,0.008332855,0.976899,0.0002463897,0.01107241,0.0003113688],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9866564,0.0005406868,0.00840975,0.00211081,0.0004514978,0.001022701,0.0001074659,0.0001327017,0.0005679418],"genre_scores_gemma":[0.9947703,0.0000428896,0.001845347,0.0000904652,0.0003103233,0.0001798671,0.00007096924,0.00007071743,0.002619095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01519879,"threshold_uncertainty_score":0.9999625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04333837257890409,"score_gpt":0.3492570126249834,"score_spread":0.3059186400460793,"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."}}