{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000171103,0.0002083438,0.0002602609,0.00002731421,0.0003670834,0.0001173363,0.0003272723,0.0002235189,0.000006801413],"category_scores_gemma":[0.0003541763,0.0001796232,0.0001382726,0.00003100255,0.0001405405,0.000009388662,0.000174088,0.00004577962,0.000006507834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000160221,"about_ca_system_score_gemma":0.00005279222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003667342,"about_ca_topic_score_gemma":0.00003158179,"domain_scores_codex":[0.9990076,0.00001194375,0.0002039309,0.000410521,0.00009077935,0.0002752689],"domain_scores_gemma":[0.9987743,0.00001233635,0.0002229985,0.0007859825,0.000120621,0.00008368464],"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":[0.0001167011,0.00004214564,0.0001548377,0.0000186687,0.00006820718,0.000003145193,0.000007599781,1.220083e-8,0.9958332,0.00003856527,0.002954327,0.0007625583],"study_design_scores_gemma":[0.000528576,0.0002069585,0.0003137066,0.00001932856,0.00006277851,0.00001759761,0.00001020097,2.603153e-7,0.9761115,0.000602637,0.02187234,0.0002541709],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973295,0.0004149835,0.0001677719,0.0004566491,0.0001289251,0.0001824024,0.0001235699,0.0000572689,0.001138965],"genre_scores_gemma":[0.9940597,0.0002784725,0.002598431,0.0003559127,0.0006567645,0.00002338538,0.0003096031,0.00003157256,0.001686113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01972179,"threshold_uncertainty_score":0.7324821,"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."}}