{"id":"W1971283181","doi":"10.1016/j.aca.2009.09.002","title":"Zinc oxide–potassium ferricyanide composite thin film matrix for biosensing applications","year":2009,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"University of Delhi; University Grants Committee; Department of Science and Technology, Ministry of Science and Technology, India; Council of Scientific and Industrial Research, India; University of Alberta","keywords":"Biosensor; Glucose oxidase; Chemistry; Amperometry; Cyclic voltammetry; Potassium ferricyanide; Ferricyanide; Prussian blue; Matrix (chemical analysis); Composite number; Substrate (aquarium); Electrode; Immobilized enzyme; Chemical engineering; Analytical Chemistry (journal); Inorganic chemistry; Chromatography; Electrochemistry; Materials science; Composite material; Organic chemistry; Biochemistry; Enzyme","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002150552,0.0003737407,0.0002603922,0.0003127738,0.0003071938,0.0003559513,0.0004078864,0.0004468641,0.0008801427],"category_scores_gemma":[0.0002098081,0.000312409,0.0001524827,0.000161179,0.0001507099,0.0002354542,0.0001642204,0.0003621094,0.0003292798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003778693,"about_ca_system_score_gemma":0.0002896606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001200677,"about_ca_topic_score_gemma":0.003528661,"domain_scores_codex":[0.999877,0.00001420493,0.000005889472,0.00003196801,0.00005260244,0.00001842296],"domain_scores_gemma":[0.9999019,0.00002475506,0.000009704888,0.000006856666,0.00004160994,0.00001525726],"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":[0.00002662013,0.000003766597,0.00002299827,0.00002244982,0.000001558041,0.00001691823,0.000004570963,0.00002228584,0.9987622,0.00002693741,0.00003209702,0.001057554],"study_design_scores_gemma":[0.000004581585,0.00003586526,0.0002095422,0.000002749144,0.00001008881,0.00006334034,0.000008429244,0.0006093266,0.9981299,0.00001612999,0.0009078879,0.000002151997],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9340999,0.01223531,0.04621143,0.0005400932,0.000290768,0.00009891383,0.0002275355,0.0006393093,0.005656917],"genre_scores_gemma":[0.966642,0.002982454,0.02406644,0.0001053557,0.00003872252,0.0000328903,0.000156702,0.00003098082,0.005944401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001200677,"threshold_uncertainty_score":0.00294441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008137844914106749,"score_gpt":0.2387349478068882,"score_spread":0.2305971028927814,"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."}}