{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003481283,0.000331442,0.0003297451,0.0003714286,0.0001915953,0.00028787,0.0004803632,0.0005501969,0.000814485],"category_scores_gemma":[0.000369686,0.0002493214,0.0003438932,0.0001933875,0.0002839556,0.0002590629,0.0001289112,0.0002451782,0.0003012165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003264202,"about_ca_system_score_gemma":0.0001610816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001656062,"about_ca_topic_score_gemma":0.00179104,"domain_scores_codex":[0.9997596,0.00003235514,0.00001816836,0.0000487505,0.00008851399,0.00005259928],"domain_scores_gemma":[0.9997537,0.00007447974,0.00003310539,0.00001917369,0.00008921824,0.00003026268],"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":[0.0004444412,0.00002864389,0.0004783259,0.00005498759,0.00002230382,0.00003896216,0.00002202609,0.0002122806,0.9969568,0.00005846944,0.000052009,0.001630636],"study_design_scores_gemma":[0.000005147715,0.0001938307,0.002149357,0.000002476009,0.00001810225,0.00004124632,0.00001821012,0.002104321,0.9951049,0.00001433274,0.0003416346,0.000006418421],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972548,0.001028945,0.0009537612,0.00002799163,0.00002671092,0.00000613683,0.00007959061,0.00003249955,0.0005895581],"genre_scores_gemma":[0.9982892,0.0002007709,0.0005634096,0.00002015337,0.000005347888,0.000004865492,0.0001184373,0.000006006065,0.0007918009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001656062,"threshold_uncertainty_score":0.003292859,"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."}}