{"id":"W4393097462","doi":"10.1021/acssuschemeng.3c07908","title":"Insights into a Nitrogen-Doped Cobalt Oxide Catalyst for Enhanced Hydrogenation of CO<sub>2</sub> to C<sub>2+</sub> Hydrocarbons","year":2024,"lang":"en","type":"article","venue":"ACS Sustainable Chemistry & Engineering","topic":"Carbon dioxide utilization in catalysis","field":"Chemical Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Light Source (Canada)","funders":"Yulin University; Dalian National Laboratory for Clean Energy; National Natural Science Foundation of China; State Key Laboratory of High-efficiency Utilization of Coal and Green Chemical Engineering, Ningxia University; Fundamental Research Funds for the Central Universities; National Science Foundation","keywords":"Catalysis; Selectivity; Cobalt; Inorganic chemistry; Formate; Chemistry; Hydrocarbon; Transition metal; Cobalt oxide; Oxide; Photochemistry; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002568868,0.0006496842,0.0007123094,0.0004436451,0.0001009405,0.0001279601,0.0005265989,0.0003761798,0.00000652465],"category_scores_gemma":[0.001198729,0.0007936503,0.0003805448,0.001932502,0.0000572159,0.0004007293,0.0002448174,0.0004091102,0.00001998154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001287103,"about_ca_system_score_gemma":0.000264129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000341237,"about_ca_topic_score_gemma":0.000003247076,"domain_scores_codex":[0.9965685,0.00001210625,0.0009640617,0.0009383726,0.0005694426,0.0009475304],"domain_scores_gemma":[0.9978575,0.0003539526,0.0001499012,0.0008171752,0.0004068616,0.0004146512],"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":[0.00003084836,0.00004455672,0.000004346585,0.002957543,0.0003150342,0.00004240316,0.0007315075,0.110972,0.8837548,0.0008062922,0.0001459146,0.0001947572],"study_design_scores_gemma":[0.0004520703,0.00001996669,0.000002071756,0.0002612578,0.0002036924,0.00001406455,0.0005198215,0.08112375,0.9150242,0.0003483781,0.001331112,0.0006996693],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.906886,0.001270295,0.08984131,0.00008080062,0.0001178992,0.0006714559,0.00003414301,0.0007599986,0.0003380808],"genre_scores_gemma":[0.9975255,0.00006359063,0.0004521814,0.00002734293,0.0002342589,0.000781677,0.0005244203,0.0002409383,0.0001501599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09063942,"threshold_uncertainty_score":0.9994515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004563835116010865,"score_gpt":0.2154459403938953,"score_spread":0.2108821052778844,"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."}}