Machine learning trained poly (3,4-ethylenedioxythiophene) functionalized carbon matrix suspended Cu nanoparticles for precise monitoring of nitrite from pickled vegetables.
Food Chem
; 460(Pt 1): 140395, 2024 Dec 01.
Article
en En
| MEDLINE
| ID: mdl-39047486
ABSTRACT
Precise monitoring of nitrite from real samples has gained significant attention due to its detrimental impact on human health. Herein, we have fabricated poly(3,4-ethylenedioxythiophene) functionalized carbon matrix suspended Cu nanoparticles (PEDOT-C@Cu-NPs) through a facile green synthesis approach. Additionally, we have used machine learning (ML) to optimize experimental parameters such as pH, drying time, and concentrations to predict current of the designed electrochemical sensor. The ML optimized concentration of fabricated C@Cu-NPs was further functionalized by PEDOT (π-electron mediator). The designed PEDOT functionalized C@Cu-NPs (PEDOT-C@Cu-NPs) electrode has shown excellent electro-oxidation capability towards NO2- ions due to highly exposed Cu facets, defects rich graphitic C and high π-electron density. Additionally, the designed material has shown low detection limit (3.91 µM), high sensitivity (0.6372 µA/µM/cm2), and wide linear range (5-580 µM). Additionally, the designed electrode has shown higher electrochemical sensing efficacy against real time monitoring from pickled vegetables extract.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Polímeros
/
Verduras
/
Compuestos Bicíclicos Heterocíclicos con Puentes
/
Cobre
/
Nanopartículas del Metal
/
Aprendizaje Automático
/
Nitritos
Idioma:
En
Revista:
Food Chem
/
Food chem
/
Food chemistry
Año:
2024
Tipo del documento:
Article
País de afiliación:
Pakistán
Pais de publicación:
Reino Unido