{"id":"W2113342617","doi":"10.1073/pnas.0501030102","title":"Graphene nanostructures as tunable storage media for molecular hydrogen","year":2005,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Hydrogen Storage and Materials","field":"Materials Science","cited_by":641,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Steacie Institute for Molecular Sciences","funders":"","keywords":"Physisorption; Hydrogen storage; Graphene; Hydrogen; Graphite; Nanotechnology; Materials science; Carbon fibers; Carbon nanotube; Energy storage; Chemical physics; Adsorption; Chemistry; Thermodynamics; Physics; Physical chemistry; Organic chemistry","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":[0.00007066534,0.0001692533,0.0001221487,0.0002585008,0.0002262915,0.0003349265,0.0003034547,0.0003795045,0.0009028182],"category_scores_gemma":[0.0001163891,0.0001361366,0.0000917751,0.0001573798,0.0002766011,0.0004322398,0.0002743317,0.0002271222,0.0003065559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000256855,"about_ca_system_score_gemma":0.00006981649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002125559,"about_ca_topic_score_gemma":0.0005366157,"domain_scores_codex":[0.9999535,0.000009712841,0.00000210129,0.000008380262,0.00001919,0.000007017153],"domain_scores_gemma":[0.9999731,0.000009953521,0.000002874109,0.000005530931,0.000003735975,0.000004936551],"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":[0.00009701168,0.00001581272,0.0001999985,0.0001606003,0.00001034247,0.0002335215,0.00007683835,0.001974056,0.9627805,0.01732505,0.0009000005,0.01622633],"study_design_scores_gemma":[0.00002282114,0.00009880954,0.0007706977,0.00001936915,0.0000201904,0.000264924,0.00004590905,0.01843275,0.9501833,0.007925042,0.02218208,0.00003417643],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9131449,0.01946369,0.02538371,0.001347968,0.000465971,0.00005358158,0.0005814895,0.001070678,0.03848793],"genre_scores_gemma":[0.9864048,0.002569247,0.007990046,0.00007714974,0.00003240444,0.00001347783,0.00009874474,0.00002822912,0.002785913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009028182,"threshold_uncertainty_score":0.003020287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02359927306834788,"score_gpt":0.2902951381882509,"score_spread":0.2666958651199031,"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."}}