Preparation and characterization of AuNPs/CNTs-ErGO electrochemical sensors for highly sensitive detection of hydrazine.

Talanta

Ecole Normale Supérieure, CNRS-ENS-UPMC UMR 8640, Paris 75005, France. Electronic address:

Published: September 2016

A highly sensitive electrochemical sensor of hydrazine has been fabricated by Au nanoparticles (AuNPs) coating of carbon nanotubes-electrochemical reduced graphene oxide composite film (CNTs-ErGO) on glassy carbon electrode (GCE). Cyclic voltammetry and potential amperometry have been used to investigate the electrochemical properties of the fabricated sensors for hydrazine detection. The performances of the sensors were optimized by varying the CNTs to ErGO ratio and the quantity of Au nanoparticles. The results show that under optimal conditions, a sensitivity of 9.73μAμM(-1)cm(-2), a short response time of 3s, and a low detection limit of 0.065μM could be achieved with a linear concentration response range from 0.3μM to 319μM. The enhanced electrochemical performances could be attributed to the synergistic effect between AuNPs and CNTs-ErGO film and the outstanding catalytic effect of the Au nanoparticles. Finally, the sensor was successfully used to analyse the tap water, showing high potential for practical applications.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.talanta.2016.05.065DOI Listing

Publication Analysis

Top Keywords

highly sensitive
8
preparation characterization
4
characterization aunps/cnts-ergo
4
electrochemical
4
aunps/cnts-ergo electrochemical
4
electrochemical sensors
4
sensors highly
4
sensitive detection
4
detection hydrazine
4
hydrazine highly
4

Similar Publications

Background: has developed resistance to almost all the antimalarial drugs currently in use. This resistance has been and remains one of the greatest threats to the control and elimination of malaria. The use of molecular markers of resistance to monitor the emergence and spread of antimalarial drug-resistant parasite strains has proved highly effective.

View Article and Find Full Text PDF

Climate change poses direct and indirect threats to public health, including exacerbating air pollution. However, the influence of rising temperature on air quality remains highly uncertain in the United States, particularly under rapid reduction in anthropogenic emissions. Here, we examined the sensitivity of surface-level fine particulate matter (PM) and ozone (O) to summer temperature anomalies in the contiguous US as well as their decadal changes using high-resolution datasets generated by machine learning.

View Article and Find Full Text PDF

The ability to convert light to higher energies through triplet-triplet annihilation upconversion (TTA-UC) is attractive for a range of applications including solar energy harvesting, bioimaging and anti-counterfeiting. Practical applications require integration of the TTA-UC chromophores within a suitable host, which leads to a compromise between the high upconversion efficiencies achievable in liquids and the durability of solids. Herein, we present a series of methacrylate copolymers as TTA-UC hosts, in which the glass transition temperature ( ), and hence upconversion efficiency can be tuned by varying the co-monomer ratios (-hexyl methacrylate (HMA) and 2,2,2-trifluoroethyl methacrylate (TFEMA)).

View Article and Find Full Text PDF

Highly effective batch effect correction method for RNA-seq count data.

Comput Struct Biotechnol J

December 2024

Department of Computer Science and Information Science, California State University San Marcos, 333 S. Twin Oaks Valley Rd, San Marcos, CA 92096, USA.

RNA sequencing (RNA-seq) has become a cornerstone of transcriptomics, providing detailed insights into gene expression across diverse biological conditions and sample types. However, RNA-seq data are often confounded by batch effects, systematic non-biological variations that compromise data reliability and obscure true biological differences. To address these challenges, we introduce ComBat-ref, a refined batch effect correction method designed to enhance the statistical power and reliability of differential expression analysis in RNA-seq data.

View Article and Find Full Text PDF

Dynamic functional connectivity (DFC) has shown promise in the diagnosis of Autism Spectrum Disorder (ASD). However, extracting highly discriminative information from the complex DFC matrix remains a challenging task. In this paper, we propose an ASD classification framework PSA-FCN which is based on time-aligned DFC and Prob-Sparse Self-Attention to address this problem.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!