{"id":"W2093187582","doi":"10.1016/j.jpcs.2014.10.011","title":"Properties of SBA-15 modified by iron nanoparticles as potential hydrogen adsorbents and sensors","year":2014,"lang":"en","type":"article","venue":"Journal of Physics and Chemistry of Solids","topic":"Mesoporous Materials and Catalysis","field":"Materials Science","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; University of Queensland; Ministère du Développement Économique, de l’Innovation et de l’Exportation","keywords":"Adsorption; Hydrogen; Chemical engineering; Materials science; Nanoparticle; Mesoporous material; Desorption; Mesoporous silica; Thermal desorption; Inorganic chemistry; Nanotechnology; Chemistry; Catalysis; Physical chemistry; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002353745,0.0001040045,0.0003309814,0.000008626263,0.00004185551,0.00003187108,0.0001119608,0.00004649621,0.00003315903],"category_scores_gemma":[0.00001895671,0.00007893786,0.00005830515,0.00002844179,0.0001650948,0.000106609,0.00005247636,0.00004322713,8.00973e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005642079,"about_ca_system_score_gemma":0.00002184003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002270612,"about_ca_topic_score_gemma":8.443832e-8,"domain_scores_codex":[0.9991083,0.00002415817,0.0003856567,0.0001073538,0.0002531042,0.0001214383],"domain_scores_gemma":[0.9992311,0.000007831127,0.0004674201,0.0001082181,0.0001030818,0.00008233744],"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":[0.0000793006,0.00008627818,0.00006392877,0.0002636748,0.00002148013,0.000001089673,0.0001783578,0.0001250044,0.998749,0.00002133955,0.00005207057,0.0003585099],"study_design_scores_gemma":[0.0004194767,0.00008514878,0.00003022262,0.00007178204,0.0000766239,0.00002510899,0.0001340859,0.0002524204,0.9980521,0.000743847,0.00002595397,0.00008325643],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993662,0.0003950113,0.00001737299,0.00004126174,0.00004582855,0.00002401448,0.00001446118,0.000002899541,0.00009290087],"genre_scores_gemma":[0.9996312,0.00008679682,0.00004326208,0.000009656304,0.0001496507,6.167858e-7,0.000001575838,0.000008340716,0.0000689384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007225074,"threshold_uncertainty_score":0.3218992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007075862216459146,"score_gpt":0.1979797037538404,"score_spread":0.1909038415373813,"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."}}