{"id":"W4412110933","doi":"10.1039/d5cp01094b","title":"High-throughput screening and DFT characterization of bimetallic alloy catalysts for the nitrogen reduction reaction","year":2025,"lang":"en","type":"article","venue":"Physical Chemistry Chemical Physics","topic":"Ammonia Synthesis and Nitrogen Reduction","field":"Chemical Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Québec Science (Canada); Institut National de la Recherche Scientifique; Université de Montréal; Concordia University","funders":"Fonds de Recherche du Québec-Société et Culture; Fonds de recherche du Québec – Nature et technologies; Alliance de recherche numérique du Canada","keywords":"Bimetallic strip; Catalysis; Characterization (materials science); Throughput; Alloy; Reduction (mathematics); Nitrogen; Materials science; Chemistry; Chemical engineering; Nanotechnology; Metallurgy; Computer science; Organic chemistry; Engineering; Mathematics","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":[0.0002813076,0.0004862688,0.0006018718,0.0005372436,0.0004427993,0.0003312461,0.0007792836,0.0006057656,0.00254419],"category_scores_gemma":[0.0005676556,0.0001917973,0.0004018375,0.0004790277,0.0001342444,0.0002959232,0.000255415,0.0004591262,0.0004154603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007154206,"about_ca_system_score_gemma":0.000392972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003254011,"about_ca_topic_score_gemma":0.007551478,"domain_scores_codex":[0.9998209,0.0000257619,0.00001012096,0.0000276735,0.00008713359,0.000028393],"domain_scores_gemma":[0.9998204,0.00009749992,0.00001018812,0.0000221666,0.00004095743,0.000008844456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009663437,0.0009103547,0.0114557,0.001622245,0.0004043352,0.001159499,0.0003011586,0.4605092,0.4119399,0.02240035,0.008067351,0.08026358],"study_design_scores_gemma":[0.0000394495,0.0002572723,0.002925913,0.00001108775,0.00003152309,0.00008473631,0.00007173804,0.8966932,0.09647333,0.001540368,0.001849303,0.00002208331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9719168,0.0006641205,0.01496745,0.0002299404,0.00004796739,0.00007560103,0.002053593,0.0006181355,0.00942633],"genre_scores_gemma":[0.9927913,0.0001751289,0.005217114,0.00002514019,0.000006733245,0.00005917127,0.0009237549,0.00002905955,0.0007726421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003254011,"threshold_uncertainty_score":0.008511186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01118306016987296,"score_gpt":0.2328708861470502,"score_spread":0.2216878259771773,"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."}}