{"id":"W4230521072","doi":"10.1002/ange.201702998","title":"Poly‐cytosine DNA as a High‐Affinity Ligand for Inorganic Nanomaterials","year":2017,"lang":"en","type":"article","venue":"Angewandte Chemie","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Education of the People's Republic of China","keywords":"Nanomaterials; DNA; Graphene; Carbon nanotube; Ligand (biochemistry); Nanotechnology; Oxide; Materials science; Cytosine; Chemistry; Combinatorial chemistry; Drug delivery; Nanoparticle; Organic chemistry; Biochemistry","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":[0.0001141728,0.0002942299,0.00007351089,0.0001622906,0.0001813512,0.000169042,0.0002241617,0.0003367494,0.00105735],"category_scores_gemma":[0.0001219279,0.0001065017,0.00008411772,0.00009306789,0.0002237169,0.0001585683,0.0001586432,0.0002772678,0.0004783652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004871816,"about_ca_system_score_gemma":0.0002171035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007527074,"about_ca_topic_score_gemma":0.001220752,"domain_scores_codex":[0.9999121,0.00001955321,0.000003359911,0.00002194949,0.00002997365,0.00001314551],"domain_scores_gemma":[0.9999269,0.00002036712,0.0000102419,0.000005188256,0.00001632681,0.00002092361],"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":[0.00002209469,0.000007933862,0.00004241418,0.00002799396,0.000001931242,0.00002022199,0.000006318546,0.0001330498,0.9976965,0.0002535195,0.0000856955,0.001702359],"study_design_scores_gemma":[0.000006828627,0.00005308318,0.0001758354,0.000002372833,0.000002792514,0.00005423442,0.000002853752,0.001058337,0.9956183,0.0000447459,0.002977666,0.000002858982],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9431256,0.00515873,0.03821884,0.0006317341,0.000208889,0.0001021954,0.0002270635,0.0004307671,0.01189619],"genre_scores_gemma":[0.9699754,0.000781441,0.02189143,0.0001303104,0.00002775937,0.00004061669,0.0001460459,0.00002272565,0.006984247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00105735,"threshold_uncertainty_score":0.003537238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01522326576562326,"score_gpt":0.2848613379662789,"score_spread":0.2696380722006556,"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."}}