{"id":"W4363646479","doi":"10.1002/smtd.202300071","title":"Synergistic Tuning of CoO/CoP Heterojunction Nanowire Arrays as Efficient Bifunctional Catalysts for Alkaline Overall Water Splitting","year":2023,"lang":"en","type":"article","venue":"Small Methods","topic":"Electrocatalysts for Energy Conversion","field":"Energy","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; China Scholarship Council","keywords":"Bifunctional; Water splitting; Alkaline water electrolysis; Oxygen evolution; Materials science; Heterojunction; Electrocatalyst; Nanowire; Chemical engineering; Catalysis; Durability; Electrolysis; Nanotechnology; Electrode; Chemistry; Electrochemistry; Optoelectronics; Composite material; Electrolyte; Physical chemistry; Photocatalysis","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":[0.002494611,0.0003311661,0.000480713,0.0004812651,0.0002776237,0.00003028061,0.0002753138,0.00021497,0.00009330042],"category_scores_gemma":[0.0008341996,0.0002790148,0.0003268107,0.000569873,0.00008181018,0.00009384655,0.0001676135,0.0001947124,0.0001432329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002430504,"about_ca_system_score_gemma":0.00009223786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001205844,"about_ca_topic_score_gemma":0.000113512,"domain_scores_codex":[0.9972598,0.0003398696,0.000645076,0.0006289228,0.0004200163,0.0007063375],"domain_scores_gemma":[0.9980661,0.0007032866,0.0002268461,0.0005066922,0.0003628178,0.0001342946],"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":[0.000278163,0.00005763564,0.00003684745,0.0001823581,0.0001866579,0.000008107082,0.0002711084,0.06172142,0.9174854,0.007683578,0.0003548642,0.01173384],"study_design_scores_gemma":[0.001047413,0.0002612846,0.0000809752,0.00009289809,0.0001577512,0.0000144229,0.0001024789,0.06774184,0.9051439,0.002617576,0.02238928,0.0003502351],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7784433,0.00008119525,0.216022,0.0002057724,0.002050725,0.0003130346,0.000007106618,0.0004607605,0.002416032],"genre_scores_gemma":[0.9424623,0.00001175551,0.05121508,0.0001315558,0.0004268008,0.000126104,0.0009213234,0.0001212719,0.004583774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.164807,"threshold_uncertainty_score":0.9999662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03292992343636908,"score_gpt":0.3112865728728914,"score_spread":0.2783566494365223,"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."}}