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A stable nanosilver decorated phosphorene nanozyme with phosphorus- doped porous carbon microsphere for intelligent sensing of 8-hydroxy-20- deoxyguanosine
Fecha
2021-08
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El artículo completo no puede ser publicado en el Repositorio Institucional debido a los permisos de copyright definidos por la editorial publicadora
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Autores
Sheng, Yingying
Yifu Zhu
Cerón, María Luisa
Yufu Yi
Peng Liu
Peng Wang
Ting Xue
Camarada, María Belén
Yangping Wen
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Editor
Elsevier
ISBN
ISSN
1572-6657
ISSNe
DOI
Resumen
Nanozyme based on the silver nanoparticles (AgNPs) decorated highly water-oxygen stable phosphorene (BP) nanohybrid with phosphorus-doped porous carbon microspheres (P-PCMs) is applied as an intelligent sensing platform for the electrochemical detection of 8-hydroxy-2′-deoxyguanosine (8-OHdG) as a biomarker of oxidative DNA damage in human urine sample. The density functional theory (DFT) is use for investigating the effect of silver on the conductivity level of BP and discussing a possible mechanism for the electrocatalytic oxidation of 8-OHdG. The Ag+ is in-situ reduced as AgNPs that grows onto the BP surface for the controllable preparation of BP-AgNPs with effective surface passivation among different metal ions decorated BP. P-PCMs with 4.9-fold enhancement in specific surface area (1636.73 m2 g−1) are prepared by hydrothermal carbonization of α-cyclodextrin as carbon sources, then calcinate in the presence of phosphoric acid as an activator and dopant. P-PCMs-BP-AgNPs are prepared by the ultrasound-assisted liquid-phase exfoliation with the addition of Ag+, and both P-PCMs and black phosphorus crystals are sufficiently grinded. The P-PCMs-BP-AgNPs nanohybrid displays good long-term water-oxygen stability, extraordinary specific surface area, superior electrocatalytic capacity with 303-fold enhancement, enzyme-like characteristics with Imax of 100 μA and Km of 29 μM. The machine learning (ML) model with artificial neural network (ANN) algorithm is employed for the intelligent output of 8-OHdG in real sample with acceptable recovery in work range from 0.2 to 125 μM.
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Nanomateriales
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Atribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL)