{"id":"W749252135","doi":"10.1088/0957-4484/26/31/315703","title":"Dynamic imaging of Au-nanoparticles via scanning electron microscopy in a graphene wet cell","year":2015,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Graphene research and applications","field":"Materials Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Materials science; Graphene; Scanning electron microscope; Resolution (logic); Scanning transmission electron microscopy; Nanotechnology; Nanoscopic scale; Nanoparticle; Transmission electron microscopy; Silicon nitride; Energy filtered transmission electron microscopy; Analytical Chemistry (journal); Silicon; Optoelectronics; Composite material; 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":[0.0001123678,0.000249758,0.0001630858,0.0002413147,0.000189421,0.0002244072,0.0005474999,0.0004182977,0.0008441113],"category_scores_gemma":[0.0001047355,0.000110227,0.000107094,0.0001908752,0.0002418006,0.000340936,0.000200444,0.0002932408,0.0002448922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002518933,"about_ca_system_score_gemma":0.0001352902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008900384,"about_ca_topic_score_gemma":0.001870045,"domain_scores_codex":[0.9999019,0.000008742778,0.000005257664,0.0000226602,0.00004737113,0.00001418824],"domain_scores_gemma":[0.9998966,0.00004069975,0.00001763667,0.00001608363,0.00001504836,0.00001390517],"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":[0.00001436873,0.00001015398,0.00008471114,0.00001705608,0.000002075788,0.00006323296,0.00001176579,0.0001758504,0.9982004,0.0001433813,0.0000405811,0.001236368],"study_design_scores_gemma":[0.000004998715,0.00006690145,0.001135607,0.000002644535,0.00000512505,0.0001034502,0.0000184582,0.0107921,0.9865638,0.00009515254,0.001201037,0.00001061776],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765733,0.0004257616,0.02013792,0.0001167974,0.00005010197,0.00003131981,0.0004734141,0.0003770145,0.001814362],"genre_scores_gemma":[0.9505656,0.0005112612,0.04631902,0.00006184378,0.0000226104,0.0000410326,0.000239879,0.00002679547,0.002211987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008900384,"threshold_uncertainty_score":0.002823889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0310557424325325,"score_gpt":0.2243518799387936,"score_spread":0.1932961375062611,"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."}}