{"id":"W4390265026","doi":"10.1021/acsami.3c13199","title":"Integrated Pt<sub><i>x</i></sub>Co<sub><i>y</i></sub>-Hierarchical Carbon Matrix Electrocatalyst for Efficient Hydrogen Evolution Reaction","year":2023,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Electrocatalysts for Energy Conversion","field":"Energy","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Waterloo Institute for Nanotechnology, University of Waterloo; Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Materials science; Electrocatalyst; Catalysis; Dissociation (chemistry); Hydrogen; Chemical engineering; Desorption; Nanoparticle; Density functional theory; Water splitting; Hydrogen fuel; Nanotechnology; Electrochemistry; Physical chemistry; Fuel cells; Electrode; Computational chemistry; Adsorption","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000319385,0.0002109732,0.0001356884,0.0001083646,0.00008859779,0.0001567351,0.0002221171,0.0001996577,0.0007276037],"category_scores_gemma":[0.00008243975,0.0001134404,0.0001275515,0.0001105932,0.000102341,0.0002621783,0.0001609916,0.0002084599,0.0002511159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002899413,"about_ca_system_score_gemma":0.0001632993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001143164,"about_ca_topic_score_gemma":0.002847913,"domain_scores_codex":[0.9999418,0.000002003654,0.000003227491,0.00001523748,0.00002587841,0.00001181073],"domain_scores_gemma":[0.9999667,0.000003049709,0.000008560066,0.00000365071,0.00001098422,0.000006966841],"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":[0.00001784509,0.000007013996,0.0001192092,0.00001451319,0.000003119363,0.00002433326,0.000002985196,0.0001611354,0.9977732,0.00007774326,0.00006889517,0.001729963],"study_design_scores_gemma":[0.000002445899,0.00003394394,0.0008067943,7.082301e-7,0.000006704995,0.00005161086,0.00000465949,0.00306379,0.9951643,0.00001450615,0.0008483029,0.000002048246],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9731012,0.0007726838,0.01879623,0.00008486235,0.00006564468,0.00003726905,0.0002447183,0.0006127624,0.006284652],"genre_scores_gemma":[0.9888226,0.0002120966,0.008182504,0.00002601896,0.000005642708,0.00001569004,0.0001297481,0.00002263336,0.002583114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001143164,"threshold_uncertainty_score":0.002434015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008306293449582875,"score_gpt":0.2313552516203007,"score_spread":0.2230489581707178,"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."}}