{"id":"W4396731734","doi":"10.1021/acsami.4c01973","title":"Molecularly Imprinted Polymer Biosensor Based on Nitrogen-Doped Electrochemically Exfoliated Graphene/Ti<sub>3</sub> CNT<i><sub>X</sub></i> MXene Nanocomposite for Metabolites Detection","year":2024,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"MXene and MAX Phase Materials","field":"Materials Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canadian Institutes of Health Research; Mitacs; Canada Research Chairs; Natural Sciences and Engineering Research Council of Canada; CMC Microsystems; University of Calgary","keywords":"Biosensor; Materials science; Prussian blue; Molecularly imprinted polymer; Graphene; Nanotechnology; Nanocomposite; Chemiresistor; Detection limit; Polymer; Nanoparticle; Electrode; Chromatography; Chemistry; Electrochemistry; 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":[0.0001875868,0.0006932436,0.000309822,0.0002500947,0.0001123965,0.0002144052,0.0006178439,0.0006382795,0.0007550321],"category_scores_gemma":[0.0003714623,0.0002664207,0.0002909981,0.0002040525,0.0002488242,0.000515704,0.0002767061,0.0004472447,0.0004580498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003367148,"about_ca_system_score_gemma":0.0001397212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003858147,"about_ca_topic_score_gemma":0.0009083906,"domain_scores_codex":[0.9997131,0.00004042064,0.00001508125,0.00009878624,0.0001132388,0.00001931795],"domain_scores_gemma":[0.9998057,0.00005897835,0.00007086006,0.00001321466,0.00003625949,0.0000149239],"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":[0.00002186771,0.000009472344,0.00007351753,0.00003569047,0.000004473377,0.00003768595,0.000006250545,0.0001271722,0.9974253,0.0000341695,0.00002381229,0.00220058],"study_design_scores_gemma":[0.000001652692,0.00006926516,0.0004438027,0.000002087067,0.000006315327,0.00008655502,0.000004348653,0.001832084,0.9970209,0.00001215338,0.0005158699,0.000004994558],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8239151,0.005965222,0.1612331,0.0004749816,0.0002675221,0.0001996882,0.0006743819,0.002185281,0.005084789],"genre_scores_gemma":[0.8345534,0.003185682,0.154898,0.0002669939,0.00005636485,0.0001264021,0.0003446683,0.00004804045,0.006520505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007550321,"threshold_uncertainty_score":0.002525866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007318647493039701,"score_gpt":0.2251701746863355,"score_spread":0.2178515271932958,"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."}}