{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008919101,0.000268452,0.0002623129,0.0000818735,0.0001504399,0.00004896101,0.0001624141,0.0001916281,0.000004831164],"category_scores_gemma":[0.0001616227,0.0002492863,0.0003009535,0.0002109586,0.00006390722,0.000007319802,0.00004710653,0.0001481951,0.000001330273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004349352,"about_ca_system_score_gemma":0.0001406709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003178764,"about_ca_topic_score_gemma":0.00008713074,"domain_scores_codex":[0.9985281,0.00003456785,0.0002609283,0.000588107,0.0001510293,0.0004372825],"domain_scores_gemma":[0.9990754,0.00002017514,0.0001397316,0.0004734983,0.0002106053,0.00008063171],"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":[0.0002641899,0.00006124823,0.00001440077,0.00001218082,0.00010831,0.00000287433,0.000002623831,4.788066e-7,0.9869594,0.00001086988,0.002046148,0.01051727],"study_design_scores_gemma":[0.0005467461,0.0003997291,0.00005910128,0.00001022942,0.0001002129,0.00005352942,0.0000087711,0.0003161251,0.9556146,0.0002972575,0.04227103,0.000322689],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9509029,0.0009210909,0.04552305,0.001115132,0.0002586735,0.000403963,0.0001021048,0.000211407,0.000561663],"genre_scores_gemma":[0.9872335,0.00008494868,0.009641633,0.000990451,0.0003558846,0.00002634147,0.0007734061,0.00004546467,0.0008483885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04022488,"threshold_uncertainty_score":0.9999959,"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."}}