{"id":"W4410741260","doi":"10.1016/j.electacta.2025.146546","title":"New insights on efficient electrochemical production of hydrogen peroxide","year":2025,"lang":"en","type":"article","venue":"Electrochimica Acta","topic":"Advanced oxidation water treatment","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"NextGenerationEU; Agencia Estatal de Investigación; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Next Generation Manufacturing Canada; Ministerio de Ciencia e Innovación; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Hydrogen peroxide; Electrochemistry; Production (economics); Chemistry; Hydrogen production; Hydrogen; Electrode; Organic chemistry; Physical chemistry; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004446571,0.0001707678,0.000163433,0.00007840614,0.00007781191,0.000007278878,0.0002120425,0.00005813438,0.0002025934],"category_scores_gemma":[0.0000528398,0.0001465277,0.00006206565,0.000428245,0.00005483052,0.00006503908,0.00007288468,0.0001426632,0.0001126246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004403857,"about_ca_system_score_gemma":0.00004060674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001210482,"about_ca_topic_score_gemma":0.00001343833,"domain_scores_codex":[0.9987484,0.0000333729,0.0002242759,0.0004406769,0.0002651282,0.0002881862],"domain_scores_gemma":[0.9994531,0.00003015698,0.00008366783,0.0003503227,0.00000926249,0.00007345005],"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":[0.0001054186,0.000176258,0.0001922712,0.00000342943,0.00002372822,4.380023e-7,0.000136325,0.0005509013,0.9956298,0.0002970108,0.001820323,0.001064055],"study_design_scores_gemma":[0.0002878369,0.0001441424,0.00159507,0.00001597709,0.0000229603,0.000003181819,0.000006178127,0.0001073189,0.991481,0.00161735,0.004603001,0.0001159572],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905925,0.00003325781,0.002037999,0.001244365,0.0000736071,0.0003052717,3.204673e-7,0.00006735237,0.005645346],"genre_scores_gemma":[0.9937685,0.0000150197,0.002571095,0.0001655821,0.00003240943,0.00001881543,0.00001224031,0.00001264248,0.003403718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004148815,"threshold_uncertainty_score":0.5975227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003949725068628033,"score_gpt":0.2164620159926853,"score_spread":0.2125122909240573,"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."}}