{"id":"W4410349326","doi":"10.1016/j.jallcom.2025.180971","title":"Modulating the electronic structure of flexible self-supported MoP carbon nanofibers for enhanced hydrogen evolution","year":2025,"lang":"en","type":"article","venue":"Journal of Alloys and Compounds","topic":"Electrocatalysts for Energy Conversion","field":"Energy","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Materials science; Electronic structure; Carbon nanofiber; Nanotechnology; Carbon fibers; Hydrogen; Nanofiber; Chemical physics; Chemical engineering; Chemistry; Composite material; Carbon nanotube; Composite number; Engineering; Computational chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005682716,0.0001499133,0.00007077547,0.0001236082,0.0001461706,0.000162193,0.0002051113,0.0003065159,0.0009597075],"category_scores_gemma":[0.000134556,0.00007253006,0.00007815633,0.00007822933,0.0001158732,0.0002283655,0.0001147136,0.0002318022,0.000110042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001855388,"about_ca_system_score_gemma":0.00007596247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004384782,"about_ca_topic_score_gemma":0.001645985,"domain_scores_codex":[0.9999576,0.000002702788,0.00000201954,0.00001047894,0.00001686042,0.00001046318],"domain_scores_gemma":[0.9999444,0.00001284144,0.0000153036,0.000005564145,0.00001143467,0.00001038429],"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":[0.00003806129,0.0000165009,0.00006976123,0.00002927118,0.000004398399,0.00004499933,0.00001336347,0.0002559984,0.9972825,0.0001975789,0.00007251572,0.001975038],"study_design_scores_gemma":[0.000008651553,0.00008671985,0.001199815,0.000004174096,0.000004351029,0.00003722294,0.00001851136,0.003552231,0.994197,0.00004878308,0.0008364914,0.000005981707],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971632,0.0002601698,0.001189088,0.00005132251,0.00002403846,0.000006511808,0.00005376696,0.00003437624,0.001217604],"genre_scores_gemma":[0.9984512,0.00009795483,0.0006755958,0.00001320194,0.000004307079,0.000005970578,0.00003581295,0.000006090045,0.0007098322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009597075,"threshold_uncertainty_score":0.003210485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003719421736769472,"score_gpt":0.2150312122699248,"score_spread":0.2113117905331553,"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."}}