{"id":"W4404704477","doi":"10.1039/d4nr03486d","title":"Sustainable and energy-saving hydrogen production <i>via</i> binder-free and <i>in situ</i> electrodeposited Ni–Mn–S nanowires on Ni–Cu 3-D substrates","year":2024,"lang":"en","type":"article","venue":"Nanoscale","topic":"Electrocatalysts for Energy Conversion","field":"Energy","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Concordia University","funders":"Fundamental Research Funds for the Central Universities; Sichuan Province Science and Technology Support Program; Iran National Science Foundation","keywords":"Substrate (aquarium); Materials science; Nanowire; Hydrogen production; In situ; Hydrogen; Production (economics); Chemical engineering; Nanotechnology; Sustainable energy; Metallurgy; Renewable energy; Chemistry; Engineering; Electrical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003294161,0.00038482,0.0003262639,0.0004751022,0.0002578113,0.0001497757,0.000235188,0.0002641026,0.00001176434],"category_scores_gemma":[0.0001057443,0.000375918,0.00006986604,0.0009750217,0.0001102868,0.0005466855,0.0001503071,0.0003154931,0.00001393291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003184395,"about_ca_system_score_gemma":0.0001067276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005471115,"about_ca_topic_score_gemma":0.01288007,"domain_scores_codex":[0.9973801,0.0001119949,0.0003470343,0.0009367148,0.0004150366,0.0008091623],"domain_scores_gemma":[0.9990942,0.0001011183,0.00006558306,0.0005032648,0.00009363531,0.0001422238],"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":[0.0001901482,0.0001073048,0.001310811,0.0001945726,0.00006739056,0.0001905682,0.0002560163,0.0001823239,0.9779691,0.009601275,0.003003638,0.006926899],"study_design_scores_gemma":[0.0004915313,0.0002872922,0.0004734944,0.0001809094,0.00006776147,0.0001394523,0.0001279307,0.0005499354,0.9806529,0.004149354,0.01241675,0.0004627433],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850885,0.006685657,0.00001644644,0.001108999,0.000294641,0.0001383387,0.000001781811,0.0003720638,0.006293537],"genre_scores_gemma":[0.9834702,0.000576535,0.00004728599,0.0001826835,0.0001945995,0.00005399681,0.00006204466,0.00009003357,0.01532263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009413112,"threshold_uncertainty_score":0.9998693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004533907963681968,"score_gpt":0.1962781240898112,"score_spread":0.1917442161261292,"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."}}