{"id":"W4406289243","doi":"10.1016/j.ijhydene.2025.01.085","title":"Nanoflower-engineered Co₃O₄@CoNi-LDO bimetallic oxide: A catalyst for revolutionizing hydrogen storage in LiAlH₄","year":2025,"lang":"en","type":"article","venue":"International Journal of Hydrogen Energy","topic":"Hydrogen Storage and Materials","field":"Materials Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China; Science and Technology Program of Hunan Province; Guangxi Key Laboratory of Information Materials","keywords":"Bimetallic strip; Nanoflower; Oxide; Catalysis; Hydrogen storage; Materials science; Hydrogen; Chemistry; Chemical engineering; Nanotechnology; Nanostructure; Metallurgy; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004873833,0.0001386172,0.00009341848,0.00009409276,0.0001340851,0.0002102759,0.0001795631,0.0002275603,0.001044152],"category_scores_gemma":[0.00007582323,0.00009108416,0.00006817465,0.00008411924,0.0001026169,0.0002254849,0.000161004,0.0002709047,0.0002589182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002475884,"about_ca_system_score_gemma":0.0001172067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005208471,"about_ca_topic_score_gemma":0.00139498,"domain_scores_codex":[0.9999669,0.000001191602,0.000001457636,0.000007313871,0.00001298573,0.00001026243],"domain_scores_gemma":[0.9999831,0.000001922379,0.000004174045,0.000001321765,0.000003471454,0.000006031939],"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":[0.00003572088,0.00001449453,0.0001086926,0.00006294545,0.000003069374,0.00003783084,0.00001717356,0.0001025929,0.9969098,0.0005182848,0.0003580902,0.001831298],"study_design_scores_gemma":[0.000007367452,0.00006470562,0.0009138515,0.000005731116,0.000008771891,0.00004354383,0.00002693019,0.002092736,0.9927451,0.00009641652,0.00398829,0.000006639752],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915093,0.000504331,0.002178967,0.0001391523,0.00007724061,0.00001072857,0.0001763579,0.0001241321,0.00527991],"genre_scores_gemma":[0.9967983,0.0001867295,0.001172192,0.00003455911,0.000009411327,0.000008055748,0.0001465367,0.00001999161,0.001624301],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001044152,"threshold_uncertainty_score":0.003493071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01246008871447233,"score_gpt":0.2613109121797255,"score_spread":0.2488508234652531,"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."}}