{"id":"W4401537485","doi":"10.1016/j.susmat.2024.e01090","title":"Highly efficient nanosized MoS2/MoP heterocatalyst for enhancing hydrogen evolution reaction over a wide pH range","year":2024,"lang":"en","type":"article","venue":"Sustainable materials and technologies","topic":"Electrocatalysts for Energy Conversion","field":"Energy","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning; Chip Unsworth Scholarship; Arcelor; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Australian Research Council; Tianjin Postdoctoral Science Foundation; R&D Systems","keywords":"Materials science; Range (aeronautics); Hydrogen; Chemistry; Chemical engineering; Nanotechnology; Composite material; Organic chemistry; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004979307,0.000293195,0.0003619335,0.0004359861,0.0003097939,0.0002197322,0.0001978282,0.0003316575,0.00001532156],"category_scores_gemma":[0.0003576966,0.0002540693,0.00009180661,0.0004323984,0.0001150552,0.000329724,0.0002304294,0.0001064119,0.00001577604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007278195,"about_ca_system_score_gemma":0.0000861373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001701585,"about_ca_topic_score_gemma":0.0001354588,"domain_scores_codex":[0.9981436,0.00003666664,0.0003608892,0.0005745081,0.0002141026,0.0006702337],"domain_scores_gemma":[0.9992335,0.0001147014,0.0001057125,0.0003691955,0.0001429097,0.00003400467],"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":[0.0001840439,0.00002646351,0.000008544099,0.0008667189,0.00008666681,0.00004210428,0.00009477555,0.0001157764,0.9262066,0.07063735,0.0002522523,0.001478666],"study_design_scores_gemma":[0.0005749397,0.0001655062,0.00002562958,0.0001322535,0.00009856001,0.00002171251,0.001888848,0.0005137024,0.957698,0.01738216,0.02119255,0.0003060695],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922026,0.002966287,0.0008470712,0.000503538,0.0003980016,0.000552823,0.00000818858,0.002277964,0.0002435661],"genre_scores_gemma":[0.9977107,0.0001778678,0.0001007319,0.0000172476,0.00006708977,0.000384587,0.00007528147,0.00005933058,0.001407188],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0532552,"threshold_uncertainty_score":0.9999912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004378497601085348,"score_gpt":0.2093292502778343,"score_spread":0.204950752676749,"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."}}