{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002312413,0.0001318567,0.0002075728,0.0000343981,0.00003468693,0.00002996599,0.00009359053,0.00008254466,7.212172e-7],"category_scores_gemma":[0.00005339798,0.0001081701,0.00001422978,0.00003466843,0.0001339203,0.00000944871,0.00009506063,0.00001980632,6.230389e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008066489,"about_ca_system_score_gemma":0.00001575309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003619464,"about_ca_topic_score_gemma":0.00001736811,"domain_scores_codex":[0.9992403,0.00004114376,0.0002458596,0.0002550946,0.00008786516,0.0001296798],"domain_scores_gemma":[0.999471,0.00001356562,0.0001680751,0.0001582108,0.0001532131,0.00003591386],"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":[0.0002789576,0.00002399376,0.00001497204,0.00003175799,0.00003734171,9.836285e-8,0.00008338047,0.000003396721,0.9979603,0.0003563595,0.0001762607,0.001033148],"study_design_scores_gemma":[0.0002594662,0.0001989403,0.0001835813,0.00002259412,0.00004909388,0.000003901757,0.0004474455,0.000001905117,0.9975157,0.000972337,0.0002140945,0.0001309811],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988601,0.0001614562,0.0005384493,0.00005153025,0.00004097923,0.0002284131,0.00007072192,0.00002361599,0.00002475245],"genre_scores_gemma":[0.9920359,0.00009092738,0.00758364,0.00003863947,0.00004996858,0.00003103768,0.0001342614,0.00001287266,0.00002280488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00704519,"threshold_uncertainty_score":0.4411049,"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."}}