{"id":"W4200163102","doi":"10.1016/j.electacta.2021.139718","title":"Poly-L-Lysine@gold nanostructured hybrid platform for Lysozyme aptamer sandwich-based detection","year":2021,"lang":"en","type":"article","venue":"Electrochimica Acta","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"European Social Fund; Energy Council of Canada; Ministry of Education and Research, Romania; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Ministero dell’Istruzione, dell’Università e della Ricerca","keywords":"Detection limit; Aptamer; Materials science; Electrochemistry; Polyethylene glycol; Electrode; Colloidal gold; PEG ratio; Scanning electron microscope; Linear range; Chemical engineering; Nanotechnology; Chemistry; Nanoparticle; Chromatography; Composite material","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":[0.00019297,0.0004046286,0.000309864,0.0002186803,0.0001592318,0.0003044255,0.0006888945,0.000671259,0.0009272397],"category_scores_gemma":[0.0001450365,0.000293541,0.0002488903,0.0001454721,0.0001594859,0.0003358134,0.0004232587,0.0003968452,0.0005450911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003805751,"about_ca_system_score_gemma":0.0002094828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003356887,"about_ca_topic_score_gemma":0.001024197,"domain_scores_codex":[0.9997975,0.00002206266,0.00001150439,0.00005774239,0.00007672879,0.00003435401],"domain_scores_gemma":[0.9999031,0.0000137338,0.00002238668,0.000009654357,0.00002960461,0.00002156288],"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":[0.00002236122,0.00001303911,0.00003807155,0.00003247965,0.000003806503,0.00003122647,0.000007055341,0.00008508641,0.9985127,0.00005673229,0.00004960534,0.001147759],"study_design_scores_gemma":[0.000006349997,0.0001136086,0.0004013863,0.000002248708,0.00001130284,0.0001038318,0.000008832078,0.004173313,0.9942678,0.00002377012,0.0008778809,0.000009562302],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9363584,0.002850404,0.0556903,0.0003043218,0.0001886415,0.00007205358,0.0002185552,0.0008014922,0.003515914],"genre_scores_gemma":[0.965382,0.0006424899,0.02846784,0.0001762675,0.00003177001,0.0000476258,0.0001345026,0.00001640388,0.005101046],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009272397,"threshold_uncertainty_score":0.003101945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00675301451411425,"score_gpt":0.2420025137024213,"score_spread":0.2352494991883071,"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."}}