{"id":"W3006805851","doi":"10.1002/adfm.201908467","title":"1D/2D Cobalt‐Based Nanohybrids as Electrocatalysts for Hydrogen Generation","year":2020,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Electrocatalysts for Energy Conversion","field":"Energy","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; University of Electronic Science and Technology of China; National Natural Science Foundation of China","keywords":"Tafel equation; Materials science; Cobalt; Chemical engineering; Electrode; Substrate (aquarium); Electrolysis; Nanotechnology; Electrochemistry; 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":[0.0002042935,0.0003299253,0.000380995,0.00009400612,0.0002616217,0.00006040669,0.0002107391,0.0001407449,0.0008762029],"category_scores_gemma":[0.0002876069,0.0003401762,0.0001513883,0.0002897973,0.00004116875,0.000403336,0.00004284167,0.00007790125,0.0005575013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002447415,"about_ca_system_score_gemma":0.000223326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001104657,"about_ca_topic_score_gemma":0.00004843972,"domain_scores_codex":[0.9978138,0.00007501888,0.0004844297,0.0006680662,0.0004666951,0.0004919581],"domain_scores_gemma":[0.9989054,0.0000951257,0.0002175132,0.0002921802,0.0002961255,0.0001937039],"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":[0.0009312431,0.00003806618,0.000005235875,0.00004212639,0.00008619359,0.000004461769,0.00001538836,0.0136407,0.9653055,0.0167574,0.001931883,0.001241791],"study_design_scores_gemma":[0.001690916,0.0005078525,0.00001577011,0.00001085117,0.00007199708,0.00001240864,0.000007901976,0.00177877,0.9392179,0.001499603,0.05483323,0.0003528521],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9758503,0.0001865693,0.01870039,0.001927308,0.001114769,0.0006030533,0.00005263943,0.0004948063,0.001070108],"genre_scores_gemma":[0.9888963,0.00001487482,0.001403584,0.003790316,0.001261596,0.0003339931,0.003736335,0.00009043294,0.0004725753],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05290135,"threshold_uncertainty_score":0.999905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02032322807888074,"score_gpt":0.235230111871441,"score_spread":0.2149068837925602,"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."}}