{"id":"W2035126047","doi":"10.1007/s00894-014-2566-0","title":"Computational study of interaction of alkali metals with C3N nanotubes","year":2015,"lang":"en","type":"article","venue":"Journal of Molecular Modeling","topic":"Graphene research and applications","field":"Materials Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"University of Toronto; Government of Ontario; Compute Canada","keywords":"Alkali metal; Adsorption; Graphene; Lithium (medication); Nanotube; Atom (system on chip); Carbon nanotube; Vacancy defect; Chemical physics; Materials science; Lithium atom; Metal; Fullerene; Work function; Density functional theory; Reactivity (psychology); Computational chemistry; Chemistry; Physical chemistry; Nanotechnology; Crystallography; Ion; Organic chemistry; Ionization","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":[0.0006369075,0.00005535071,0.0001866679,0.000143738,0.0000233425,0.00001762753,0.0001491695,0.00001515267,0.000009213486],"category_scores_gemma":[0.0000592856,0.00004056991,0.00005053501,0.000146768,0.00002725264,0.0001595856,0.00002813889,0.00007338906,9.530334e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001727394,"about_ca_system_score_gemma":0.0001255742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005165639,"about_ca_topic_score_gemma":0.000004988282,"domain_scores_codex":[0.9987516,0.00009026362,0.0003966217,0.00007415476,0.0006049559,0.00008242131],"domain_scores_gemma":[0.9985956,0.00002980568,0.0002964413,0.00009727149,0.0009028962,0.00007799885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007345682,0.0002066293,0.00006903009,0.000006071486,0.00003262884,0.000004590898,0.0002804376,0.6246358,0.3744641,0.0001450052,0.000008303271,0.00007394802],"study_design_scores_gemma":[0.002491312,0.00227521,0.0001101237,0.0001566952,0.0001492768,0.0001262641,0.007754345,0.4461887,0.5329843,0.007599859,0.00001736471,0.0001465656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7432002,0.0001185707,0.2565053,0.00004258871,0.00001951281,0.00007507042,0.000001605892,0.000002012385,0.0000351397],"genre_scores_gemma":[0.9847058,0.000003295655,0.0152583,0.00000767542,0.00001393629,0.000002565748,7.872354e-7,0.000006109417,0.000001504195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2415057,"threshold_uncertainty_score":0.1654393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05997368883948431,"score_gpt":0.335981474848338,"score_spread":0.2760077860088537,"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."}}