{"id":"W2084067799","doi":"10.1016/j.talanta.2015.03.033","title":"Sensitive electrochemical detection of Salmonella with chitosan–gold nanoparticles composite film","year":2015,"lang":"en","type":"article","venue":"Talanta","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":97,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; The Scarborough Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Chemistry; Detection limit; Chitosan; Horseradish peroxidase; Colloidal gold; Linear range; Electrochemistry; Nanoparticle; Electrode; Composite number; Chromatography; Reproducibility; Nuclear chemistry; Biosensor; Nanotechnology; Materials science; Composite material; Organic chemistry; Biochemistry","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":[0.00005653764,0.0001120614,0.0001417468,0.00002914669,0.00002089534,0.000006889816,0.00005792501,0.00008274028,1.396983e-7],"category_scores_gemma":[0.0000194106,0.00008626876,0.00004471327,0.0001088353,0.0001021391,0.000002645405,0.00003865263,0.00005866192,0.000001061032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007406331,"about_ca_system_score_gemma":0.00002267547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001449143,"about_ca_topic_score_gemma":0.00003599829,"domain_scores_codex":[0.999364,0.00003284592,0.0001259333,0.0002110525,0.0001153272,0.0001508144],"domain_scores_gemma":[0.9995826,0.000006234536,0.00008069293,0.0001861216,0.00007642421,0.00006794067],"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":[0.0003368893,0.00004357494,0.0005786177,0.000004248028,0.00004502028,0.000004329736,0.00002088391,0.000006218932,0.9983947,0.000002592286,0.00006187647,0.0005010053],"study_design_scores_gemma":[0.0002680653,0.000479025,0.0002936163,0.00001390026,0.0000388492,0.000105102,0.00006833277,0.0001142541,0.9982644,0.00001702516,0.0002080814,0.0001293589],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980854,0.00008607149,0.001068407,0.0000259564,0.00001226284,0.00006538686,0.000007452092,0.00003206437,0.0006170093],"genre_scores_gemma":[0.998697,0.00003163945,0.001011482,0.00004908436,0.0000693306,0.000002160739,0.00006215845,0.00001223505,0.0000649191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006116003,"threshold_uncertainty_score":0.3517938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007094544537055007,"score_gpt":0.2341352821107747,"score_spread":0.2270407375737197,"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."}}