The development of impedimetric, non-faradaic label-free sensors for the detection of α-amino acids constitutes a trailblazing technology for the fast and inexpensive quantification of such biomarkers. Since α-amino acids, such as glycine and sarcosine, are basic constituents in biological processes, a variation in their concentration may be an indicator of cardiovascular diseases and metabolic disorders or neurological conditions. The unique properties, including maze-like porosity along with excellent electron transfer behavior, make boron-doped carbon nanowalls (BCNW) an ideal transducer for electrochemical sensing. In order to realize a non-faradaic impedimetric sensor for the detection of α-amino acids, 1,8-diazafluoren-9-one (DFO), a fluorophore commonly used in forensic science, was dispersed into Ti-sol precursor and deposited over a BCNW substrate by spin-coating. Data mining tools have been applied to the raw impedimetric data to directly predict the glycine concentration and to support the underlying material-interface interaction. The developed sensor revealed high selectivity and reproducibility toward glycine and other α-amino acids (phenylamine, sarcosine and tryptophan) and no selectivity toward β-alanine, γ-aminobutyric acid or taurine. The application of density-functional theory (DFT) studies supported the higher affinity with the highest adsorption energy for the reaction product of DFO with glycine. A detection limit of 51 nM was found for glycine.
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Informacje dodatkowe
- DOI
- Cyfrowy identyfikator dokumentu elektronicznego link otwiera się w nowej karcie 10.1016/j.snb.2022.132459
- Kategoria
- Publikacja w czasopiśmie
- Typ
- artykuły w czasopismach
- Język
- angielski
- Rok wydania
- 2022