Authors
Raluca-Ioana Stefan-van Staden, Ramona Georgescu-State, Ionela Raluca Comnea-Stancu, Catalina Cioates Negut, Bianca-Maria Tuchiu-Stanca, Damaris-Cristina Gheorghe, Ruxandra-Maria Ilie-Mihai, Andrada-Ioana Stefan, Ciprian Brisc, Alexandra Orăşeanu
Published in
Analytical chemistry. Jul 03, 2026. Epub Jul 03, 2026.
Abstract
A novel stochastic electrochemical sensing platform based on a β-cyclodextrin/silver oxide nanoparticle-decorated multiwalled carbon nanotube composite (β-CD/Ag2ONPs@MWCNT/CPE) was developed for the molecular enantiorecognition and enantioanalysis of 12 amino acids (l/d-glutamine, l/d-tryptophan, l/d-cysteine, l/d-methionine, l/d-lysine, l/d-phenylalanine, l/d-valine, l/d-serine, l/d-leucine, l/d-ornithine, l/d-pipecolic acid, and l/d-alanine) in biological samples. The Ag2ONPs were synthesized via an environmentally friendly approach using green tea extract as a reducing and stabilizing agent, enabling a sustainable fabrication process with good experimental reproducibility under controlled conditions. Structural and morphological analyses, along with electrochemical characterization, confirmed the uniform incorporation of β-CD and Ag2ONPs onto MWCNTs, thereby enhancing both electron-transfer kinetics and molecular recognition efficiency. The sensing interface exhibited low limits of quantification, a wide linear concentration range, and high sensitivity, enabling simultaneous enantioselective detection of amino acids in complex biological matrices, including whole blood, tumor tissue, saliva, and urine. Biological samples were collected from patients confirmed with gastric cancer as well as from healthy volunteers. Recovery values exceeded 97.0% (N = 10), with standard deviations (SDs) from the mean value of less than 0.10 (the recovery value represents the average of 10 determinations performed for the same sample) when a refreshed surface of the sensor was used for a new determination; without refreshing, the surface the mean value was the same but the deviations from the mean varied from 1.7 to 3.4. The platform has demonstrated good potential as a screening tool for clinical diagnostics and metabolic studies.
PMID:
42397718
Bibliographic data and abstract were imported from PubMed on 04 Jul 2026.
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