{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001137078,0.001149693,0.001318089,0.0006106267,0.0003629765,0.001619244,0.0008131687,0.000505753,0.0001976291],"category_scores_gemma":[0.00008591933,0.001036799,0.0002132855,0.000619637,0.0002305252,0.0003830242,0.000269005,0.0002345267,0.0006115915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001590555,"about_ca_system_score_gemma":0.0001735884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005965668,"about_ca_topic_score_gemma":0.00001697717,"domain_scores_codex":[0.9942207,0.0002738408,0.001556098,0.001832219,0.0006718268,0.001445275],"domain_scores_gemma":[0.9976724,0.0002887752,0.0005182884,0.0009566418,0.000289406,0.0002745263],"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":[0.002695888,0.0002382353,6.003396e-7,0.00045809,0.0002304102,0.00001965542,0.00009679712,0.00001714218,0.993811,0.0009879704,0.0002297511,0.00121446],"study_design_scores_gemma":[0.001571963,0.00045889,0.000004563278,0.0002363811,0.0004365075,0.00002620016,0.000025723,0.00004238089,0.9941982,0.001612261,0.0002655508,0.00112136],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890553,0.0007628048,0.003419032,0.0001774477,0.002170786,0.002168806,0.0009196362,0.001181426,0.0001448053],"genre_scores_gemma":[0.9954597,0.0001454345,0.0006927206,0.0008448598,0.0005276548,0.001660587,0.0003596063,0.0002909545,0.00001849862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006404428,"threshold_uncertainty_score":0.9994172,"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."}}