{"id":"W2061767265","doi":"10.1016/j.jelechem.2006.06.012","title":"Electrocatalytic reduction of nitrate on copper electrodes prepared by high-energy ball milling","year":2006,"lang":"en","type":"article","venue":"Journal of Electroanalytical Chemistry","topic":"Ammonia Synthesis and Nitrogen Reduction","field":"Chemical Engineering","cited_by":154,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; World Health Organization","keywords":"Copper; Chemistry; Argon; Crystallite; Ball mill; Adsorption; Nitrate; Electrode; Inorganic chemistry; Cyclic voltammetry; Particle size; Analytical Chemistry (journal); Electrochemistry; Metallurgy; Materials science; Crystallography; Chromatography; Organic chemistry; Physical chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002594101,0.0002996033,0.0006058631,0.00009911125,0.00005732419,0.00002801211,0.0002662237,0.0002330429,0.0001092981],"category_scores_gemma":[0.0001391869,0.0002658936,0.0003652432,0.000365803,0.00008312005,0.0001333496,0.00001457248,0.0005439254,0.00000323799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003320424,"about_ca_system_score_gemma":0.0001300612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006329529,"about_ca_topic_score_gemma":0.000001064825,"domain_scores_codex":[0.9976029,0.00003210424,0.000991391,0.0002920193,0.0005590874,0.000522478],"domain_scores_gemma":[0.9986369,0.0001122429,0.0005637666,0.0002406771,0.0002889337,0.0001574784],"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":[0.000435138,0.0002942325,0.00001972831,0.00005215302,0.0002701544,0.000006395861,0.000004330937,0.0009879495,0.9926425,0.001948689,0.003152165,0.0001865326],"study_design_scores_gemma":[0.0004688276,0.0002650212,0.00001079797,0.00007425471,0.000296481,0.0002527503,0.00001549648,0.002710835,0.9936007,0.001793588,0.00027157,0.0002396722],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917068,0.001713249,0.004067737,0.0002658931,0.00005654886,0.00004410743,0.000007107119,0.00003988374,0.002098659],"genre_scores_gemma":[0.9977381,0.0001110053,0.0004115928,0.00001544237,0.0006801834,0.000003631772,0.00002949187,0.00004796241,0.0009626047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00603127,"threshold_uncertainty_score":0.9999793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004145077403883658,"score_gpt":0.1965073199609062,"score_spread":0.1923622425570226,"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."}}