{"id":"W4402023975","doi":"10.1016/j.talanta.2024.126714","title":"Enhanced enzymatic electrochemical detection of an organophosphate Pesticide: Achieving Wide linearity and femtomolar detection via gold nanoparticles growth within polypyrrole films","year":2024,"lang":"en","type":"article","venue":"Talanta","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Centre de Compétences Nanosciences Ile-de-France; Universidade Federal do Rio Grande do Sul; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Financiadora de Estudos e Projetos","keywords":"Chemistry; Polypyrrole; Colloidal gold; Nanoparticle; Nanotechnology; Pesticide; Organophosphate; Electrochemistry; Linearity; Chromatography; Electrode; Electrical engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001299877,0.0002068366,0.0002355159,0.00007528752,0.00004891,0.00005913002,0.00008375302,0.0001481855,0.00001266987],"category_scores_gemma":[0.0001049001,0.0001904459,0.000046869,0.0003397165,0.00005244514,0.0001786611,0.00002393055,0.0003263988,0.000009674088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005358203,"about_ca_system_score_gemma":0.000008593759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005519346,"about_ca_topic_score_gemma":0.00006971,"domain_scores_codex":[0.9989074,0.00003024564,0.0003178191,0.0002711179,0.0001609136,0.0003125205],"domain_scores_gemma":[0.9995571,0.0001018512,0.00004997079,0.0001367926,0.00003501685,0.0001192371],"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":[0.00004478945,0.00002236505,0.00007790378,0.0002658721,0.00003513596,0.000008097129,0.0001782355,0.00004180621,0.997874,0.000008056089,0.000003357318,0.001440344],"study_design_scores_gemma":[0.0001165796,0.0001493762,0.0004412395,0.00007978796,0.00005763633,0.00007126291,0.00002487652,0.09213627,0.9065106,0.0002068054,0.000004622303,0.0002009765],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964723,0.0009225244,0.001866018,0.00001338585,0.0001243312,0.000130227,0.000004083629,0.0004365962,0.00003050924],"genre_scores_gemma":[0.9995431,0.00007106806,0.0002177986,0.00002010114,0.00006504147,0.000005498771,0.000008275799,0.00004505864,0.00002401326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09209447,"threshold_uncertainty_score":0.7766158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003619397250129606,"score_gpt":0.1903935105215638,"score_spread":0.1867741132714342,"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."}}