{"id":"W1991981932","doi":"10.1016/j.msea.2006.04.074","title":"Mechanical alloying and electronic simulations of 2Mg–Fe mixture powders for hydrogen storage","year":2006,"lang":"en","type":"article","venue":"Materials Science and Engineering A","topic":"Hydrogen Storage and Materials","field":"Materials Science","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Hunan Province; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Hydrogen storage; Dehydrogenation; Materials science; Magnesium hydride; Hydride; Dissociation (chemistry); Solid solution; Hydrogen; Pseudopotential; Desorption; Microstructure; Chemical engineering; Phase (matter); Thermodynamics; Physical chemistry; Catalysis; Inorganic chemistry; Metallurgy; Chemistry; Organic chemistry; Adsorption; Alloy","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":[],"consensus_categories":[],"category_scores_codex":[0.001126222,0.0001663257,0.0002943429,0.0001432716,0.0001934616,0.000173115,0.0001865731,0.00007749296,0.00005258319],"category_scores_gemma":[0.0001392576,0.0001510083,0.00002326909,0.0001926526,0.0001510606,0.0003285548,0.00009310654,0.00003370082,0.000002970869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000483736,"about_ca_system_score_gemma":0.00008681775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001093415,"about_ca_topic_score_gemma":0.000007285412,"domain_scores_codex":[0.9985535,0.0000199864,0.0003190286,0.0003596208,0.0002502988,0.000497551],"domain_scores_gemma":[0.9994844,0.00006611775,0.00009009893,0.0001793624,0.00009312729,0.00008691177],"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":[0.00001460769,0.00001286642,0.000003372106,0.00008250364,0.000003064033,0.00000129401,0.00007223938,0.002536251,0.9938694,0.00335383,0.00002009611,0.00003053846],"study_design_scores_gemma":[0.0002673278,0.00007201132,0.00004727861,0.00002986254,0.00002137397,0.00001652501,0.00001622563,0.003470151,0.9942134,0.0007030315,0.0009626637,0.0001801476],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968365,0.0001625955,0.002206994,0.00005705983,0.0002866174,0.0002750272,0.00008962426,0.00007707878,0.000008446224],"genre_scores_gemma":[0.9977853,0.00001142903,0.001980062,0.00002871567,0.0001168368,0.00002702923,0.000008371156,0.00001975992,0.0000224607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002650799,"threshold_uncertainty_score":0.6157939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006267916321674041,"score_gpt":0.2146710400458861,"score_spread":0.208403123724212,"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."}}