{"id":"W2266934901","doi":"10.1039/c6an00044d","title":"Au nanoparticle decorated graphene nanosheets for electrochemical immunosensing of p53 antibodies for cancer prognosis","year":2016,"lang":"en","type":"article","venue":"The Analyst","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Graphene; Biosensor; Cyclic voltammetry; Raman spectroscopy; Nanoparticle; X-ray photoelectron spectroscopy; Electrode; Chemistry; Materials science; Colloidal gold; Oxide; Nanotechnology; Electrochemistry; Chemical engineering; Organic chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001734378,0.0004357263,0.0002927227,0.0006135608,0.0001126774,0.0002269999,0.0005610925,0.0009235439,0.0005591092],"category_scores_gemma":[0.0003620768,0.0002461948,0.0002875628,0.0003620248,0.0001342281,0.0003021009,0.0002747903,0.0003161227,0.0002740104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002643681,"about_ca_system_score_gemma":0.0001149078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006149711,"about_ca_topic_score_gemma":0.00152293,"domain_scores_codex":[0.9997646,0.00003846694,0.00001494532,0.00003694545,0.0001201131,0.00002498801],"domain_scores_gemma":[0.9998885,0.00003230874,0.00001927161,0.00001029281,0.00003533818,0.0000142948],"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":[0.00008404198,0.00004717926,0.0003673515,0.0001027533,0.00002316134,0.0001896601,0.00001698053,0.0009699902,0.9837488,0.0001261423,0.0002749451,0.01404885],"study_design_scores_gemma":[0.0000191916,0.0003482284,0.002874289,0.00001442094,0.00004880234,0.000381935,0.0000287224,0.01380885,0.9790998,0.0001964848,0.003142791,0.00003653335],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9091874,0.016776,0.06645851,0.0006966873,0.0006795806,0.0002722498,0.0009247497,0.0008202937,0.004184457],"genre_scores_gemma":[0.9432235,0.003518115,0.04859506,0.0004017113,0.00007771191,0.00009978416,0.0003963459,0.00002477009,0.003663039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009235439,"threshold_uncertainty_score":0.001918077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01371449730542052,"score_gpt":0.2976725279135483,"score_spread":0.2839580306081277,"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."}}