{"id":"W4413984504","doi":"10.1016/j.synthmet.2025.117952","title":"Advanced MnNi-P/ZIF8@CNT electrocatalyst for CO2 reduction: Achieving high efficiency, stability, and low energy demand","year":2025,"lang":"en","type":"article","venue":"Synthetic Metals","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"Deanship of Scientific Research, King Khalid University","keywords":"Electrocatalyst; Reduction (mathematics); Materials science; Nanotechnology; Carbon nanotube; Chemical engineering; Chemistry; Electrochemistry; Electrode; Engineering","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":[0.0006471127,0.000301935,0.0005317548,0.0002307737,0.0003185289,0.00008122117,0.0002585565,0.0001448532,0.0001845088],"category_scores_gemma":[0.0002418115,0.0002767035,0.0002037835,0.0004797499,0.0002059258,0.0001515939,0.0001259703,0.0001236713,0.000005200561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001121732,"about_ca_system_score_gemma":0.00009522091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002417645,"about_ca_topic_score_gemma":0.00005622397,"domain_scores_codex":[0.9979088,0.0001268372,0.0005561255,0.000732144,0.0002286489,0.000447452],"domain_scores_gemma":[0.9986866,0.0001857652,0.0001677201,0.0006969995,0.0001536121,0.0001093362],"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":[0.0003297605,0.0004742328,0.00001699546,0.0005177311,0.0003634566,0.000003502685,0.000333157,0.0005410464,0.5540449,0.2165725,0.000933002,0.2258697],"study_design_scores_gemma":[0.0005645883,0.0001742846,0.00004574938,0.000130405,0.0002347541,0.00006044951,0.0002108389,0.000859548,0.9452279,0.01862574,0.03349564,0.0003701099],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8629978,0.006592267,0.1163327,0.002232255,0.001377759,0.001123414,0.00002818838,0.0006601403,0.008655469],"genre_scores_gemma":[0.9942487,0.0003208022,0.001386699,0.00006803338,0.000106563,0.0003935547,0.0000436093,0.0000344866,0.003397557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.391183,"threshold_uncertainty_score":0.9999685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008705336897080819,"score_gpt":0.2502694015603068,"score_spread":0.241564064663226,"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."}}