{"id":"W2179867003","doi":"10.1021/acsami.5b08004","title":"Comprehensive Screen of Metal Oxide Nanoparticles for DNA Adsorption, Fluorescence Quenching, and Anion Discrimination","year":2015,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":138,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Adsorption; Anatase; Oxide; Molecule; Quenching (fluorescence); Inorganic chemistry; Fluorescence; Metal; DNA; Arsenate; Metal ions in aqueous solution; Desorption; Nanoparticle; Photochemistry; Nanotechnology; Arsenic; Photocatalysis; Chemistry; Physical chemistry; Organic 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":[0.0002957684,0.0006104955,0.0003465373,0.000528432,0.0001895596,0.0001947244,0.000245783,0.0003457824,0.0002474305],"category_scores_gemma":[0.000300584,0.000197292,0.000292778,0.0002256471,0.0001270254,0.000180425,0.0003048036,0.0001957921,0.000115376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001925393,"about_ca_system_score_gemma":0.0002531139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005950601,"about_ca_topic_score_gemma":0.002110538,"domain_scores_codex":[0.9997254,0.00003303101,0.00002305496,0.00006308857,0.0001171359,0.00003831931],"domain_scores_gemma":[0.9998839,0.00002523588,0.00002212211,0.000009945119,0.00004352334,0.00001523358],"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":[0.00001231905,0.000009515408,0.0001554413,0.00003119783,0.000004469563,0.00001567426,0.000005039278,0.00004197334,0.9980433,0.0000128693,0.0000138208,0.001654304],"study_design_scores_gemma":[0.000002486843,0.0001328853,0.001337678,0.000003001894,0.00001377847,0.00006867968,0.00001128141,0.0006451782,0.9969782,0.00001563183,0.0007872354,0.00000399968],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9585389,0.002862687,0.03537233,0.0001013663,0.00003267172,0.0002696335,0.0005175655,0.0002408932,0.002063908],"genre_scores_gemma":[0.9458859,0.002049042,0.04773822,0.0001320388,0.000009709368,0.0002199493,0.0009034143,0.00002964588,0.003032045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006104955,"threshold_uncertainty_score":0.001564205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02336656913122725,"score_gpt":0.2831763592562991,"score_spread":0.2598097901250719,"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."}}