{"id":"W4406168180","doi":"10.1016/j.nanoen.2025.110662","title":"The anodic chlorine ion repelling mechanisms of Fe/Co/Ni-based nanocatalysts for seawater electrolytic hydrogen production","year":2025,"lang":"en","type":"article","venue":"Nano Energy","topic":"Electrocatalysts for Energy Conversion","field":"Energy","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Education and Child Care","funders":"Ministry of Education of the People's Republic of China; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Nanomaterial-based catalyst; Seawater; Materials science; Anode; Chlorine; Electrolyte; Hydrogen production; Hydrogen; Ion; Inorganic chemistry; Cathodic protection; Chemical engineering; Metallurgy; Electrode; Physical chemistry; Metal; Chemistry; Organic chemistry; Oceanography","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":[0.0008462254,0.0003392328,0.0003973613,0.0003454577,0.001028324,0.00005526514,0.0005645747,0.0001798213,0.000005660183],"category_scores_gemma":[0.0001349809,0.0002759009,0.0002787723,0.0009034153,0.00009485767,0.0001275539,0.0000851022,0.0001516438,0.00000981596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004716514,"about_ca_system_score_gemma":0.0003474548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001645078,"about_ca_topic_score_gemma":0.001181854,"domain_scores_codex":[0.9972839,0.0001415842,0.0006173832,0.0006869064,0.0005794192,0.0006907883],"domain_scores_gemma":[0.9981745,0.0001477228,0.0003202245,0.0009193326,0.0003699972,0.0000682362],"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":[0.0006244886,0.00009211009,0.000009525811,0.00005927434,0.0001777017,0.000002109931,0.00001593198,0.01391213,0.909808,0.06708075,0.0007522669,0.007465733],"study_design_scores_gemma":[0.0008854994,0.0002848742,0.000002374696,0.00004877496,0.0001253732,0.000008554299,0.00002119081,0.005717644,0.9068347,0.01410114,0.07172705,0.0002428057],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9141394,0.00255837,0.06242899,0.004622687,0.004629188,0.001386278,0.0000106936,0.0009253193,0.009299015],"genre_scores_gemma":[0.9818587,0.00006079289,0.0002143121,0.0002382119,0.0001815054,0.0003195814,0.0002710202,0.0000712224,0.01678468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07097478,"threshold_uncertainty_score":0.9999693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006638739381931977,"score_gpt":0.2231610063399395,"score_spread":0.2165222669580075,"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."}}