{"id":"W4411500770","doi":"10.1002/chem.202501897","title":"High Dispersion of Copper Nanoparticles on Carbon Black in Minimal Loadings Enhances the Electroreduction of CO <sub>2</sub> to CO","year":2025,"lang":"en","type":"article","venue":"Chemistry - A European Journal","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Financiadora de Estudos e Projetos; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Catalysis; Carbon black; Materials science; Copper; Electrochemistry; Chemical engineering; Nanoparticle; Electrolysis; Dispersion (optics); Carbon monoxide; Faraday efficiency; Carbon fibers; Diffusion; Nanotechnology; Electrode; Metallurgy; Chemistry; Composite material; Organic chemistry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00009893683,0.0004150184,0.0002049281,0.0002489259,0.0001489751,0.0003640136,0.000206528,0.0003190469,0.0006871694],"category_scores_gemma":[0.0002223532,0.0001649213,0.0001072689,0.0001321755,0.0002726694,0.0002068514,0.0001716832,0.0002732508,0.0002820335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003981724,"about_ca_system_score_gemma":0.0001425446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001090645,"about_ca_topic_score_gemma":0.002917969,"domain_scores_codex":[0.9998782,0.0000132226,0.000010007,0.00003550124,0.00003748916,0.00002553544],"domain_scores_gemma":[0.9998933,0.0000251279,0.00002973433,0.00001087983,0.00002385937,0.00001716348],"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":[0.00003962654,0.00001329175,0.00004148527,0.00002037289,0.000003476813,0.00002108485,0.000007376941,0.0001111173,0.9986645,0.00005178097,0.00004567808,0.0009801855],"study_design_scores_gemma":[0.000002797115,0.00003274051,0.0002725798,0.000001918114,0.000004143095,0.00001152747,0.000003186138,0.0006898044,0.9987061,0.000008035399,0.0002656679,0.000001487728],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943842,0.0005148806,0.002482988,0.00007681893,0.00003117566,0.00001244585,0.00006148557,0.0001730641,0.002262966],"genre_scores_gemma":[0.9967867,0.0001722555,0.001989215,0.00001668577,0.000006053099,0.00000739794,0.00004988371,0.0000299658,0.000941805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001090645,"threshold_uncertainty_score":0.002888978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006610195064886503,"score_gpt":0.2298804583023448,"score_spread":0.2232702632374583,"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."}}