{"id":"W2333031853","doi":"10.1021/nl502366e","title":"Direct, Rapid, and Label-Free Detection of Enzyme–Substrate Interactions in Physiological Buffers Using CMOS-Compatible Nanoribbon Sensors","year":2014,"lang":"en","type":"article","venue":"Nano Letters","topic":"Nanowire Synthesis and Applications","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Laboratory; Air Force Research Laboratory; Army Research Office; Natural Sciences and Engineering Research Council of Canada","keywords":"Substrate (aquarium); Chemistry; Biosensor; Acetylcholinesterase; Urease; Enzyme; Kinetics; Michaelis–Menten kinetics; Detection limit; Materials science; Nanotechnology; Enzyme assay; Combinatorial chemistry; Chromatography; Biochemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.0001133554,0.0001320657,0.000215568,0.0001247944,0.00005482185,0.00001646507,0.00009199226,0.00004648155,0.00001118706],"category_scores_gemma":[0.00003014041,0.0001270583,0.00004027124,0.0002302425,0.00005041801,0.00009938794,0.00001998992,0.00009951158,0.00000442306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005153093,"about_ca_system_score_gemma":0.000003173453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001183993,"about_ca_topic_score_gemma":0.00006182375,"domain_scores_codex":[0.9992805,0.0000531589,0.0002303322,0.0001746067,0.00008010532,0.0001812805],"domain_scores_gemma":[0.9995683,0.0001116528,0.00005094165,0.0002151423,0.00001523947,0.00003870508],"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":[0.000006636544,0.00002179221,0.0001498341,0.00002502308,0.00001305713,3.732667e-7,0.0000628578,0.005893857,0.9882245,0.00003008691,0.0001426808,0.005429322],"study_design_scores_gemma":[0.0004049539,0.00002307494,0.008315297,0.00006544419,0.00001913739,0.000003973553,0.0000388692,0.02702113,0.9622948,0.00004319362,0.001572524,0.0001975909],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970619,0.00006447459,0.001993921,0.000148934,0.0001479658,0.0001198952,0.000007253801,0.00009006605,0.0003656092],"genre_scores_gemma":[0.9988442,0.00003293078,0.0009458785,0.00007196487,0.00005959889,0.00002001192,0.000002058311,0.00001852539,0.000004823844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02592967,"threshold_uncertainty_score":0.5181287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02015825243960158,"score_gpt":0.2302120697134662,"score_spread":0.2100538172738647,"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."}}