{"id":"W2547056041","doi":"10.1021/acs.analchem.6b03227","title":"Maximizing the Signal Gain of Electrochemical-DNA Sensors","year":2016,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Office; National Institute of Allergy and Infectious Diseases; Fonds de recherche du Québec – Nature et technologies; Institute for Collaborative Biotechnologies; National Institutes of Health","keywords":"Chemistry; Biosensor; Electron transfer; Dynamic range; SIGNAL (programming language); Nucleic acid; Square wave; Analyte; Redox; DNA; Biophysics; Photochemistry; Biochemistry; Inorganic chemistry; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001712826,0.000157999,0.000182616,0.0000128733,0.00004877099,0.000008367627,0.0002299939,0.0001714541,0.00003212049],"category_scores_gemma":[0.0002319208,0.00008501502,0.0002029673,0.0001292044,0.0003279716,0.000002312141,0.0000898096,0.00009289782,0.000002489914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001756767,"about_ca_system_score_gemma":0.00003487202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001743683,"about_ca_topic_score_gemma":6.81788e-7,"domain_scores_codex":[0.9989506,0.00002608422,0.0002594511,0.0003217495,0.0001750795,0.0002670521],"domain_scores_gemma":[0.9992303,0.00005874087,0.0001010449,0.0004183089,0.0001140973,0.00007754387],"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":[0.00004781938,0.00003034014,0.0002654104,0.00001021023,0.00007222558,0.000001888295,0.000001941184,7.033577e-7,0.9975336,0.000031739,0.0009377833,0.001066302],"study_design_scores_gemma":[0.0001586903,0.00003833497,0.00003438405,0.00002389098,0.00005307986,0.00001906992,0.00002052529,0.00009381196,0.9951378,0.0002117831,0.004060946,0.0001476841],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891403,0.0001025791,0.005363436,0.001118729,0.000007516862,0.00005638592,0.00001280756,0.00003454897,0.004163693],"genre_scores_gemma":[0.997074,0.00008961224,0.0006660407,0.0001293494,0.0001690352,0.000002921188,0.00001820475,0.00001458492,0.00183627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007933678,"threshold_uncertainty_score":0.3466812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007937169127475757,"score_gpt":0.2500619347492013,"score_spread":0.2421247656217256,"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."}}