A synergistic approach to light-free catalysis using zinc oxide embedded multi-walled carbon nanotube paper

Carbon ◽  
2014 ◽  
Vol 77 ◽  
pp. 705-709 ◽  
Author(s):  
Yulai Gao ◽  
Shengjuan Li ◽  
Bingge Zhao ◽  
Qijie Zhai ◽  
Adrian Lita ◽  
...  
2019 ◽  
Vol 9 (3) ◽  
pp. 207-222 ◽  
Author(s):  
Soha Mohajeri ◽  
Abolghassem Dolati ◽  
Khashayar Yazdanbakhsh

Novel polyaniline/zinc oxide/multi-walled carbon nanotube (PANI/ZnO/MWCNT) ternary nanocomposite was fabricated as a non-enzymatic glucose biosensor. Thermal chemical vapor deposition (CVD) process was employed to synthesize vertically aligned MWCNTs on stainless steel substrates coated by Co catalyst nanoparticles. In order to fabricate sensitive and reliable MWCNT-based biosensors, nanotubes density and alignment were adjusted by varying the CVD reaction time and cobalt sulfate concentration. The fabricated nanotubes were modified by ZnO particles through the potentiostatic electrodeposition technique. Optimal electrodeposition potential, electrodeposition time, and electrolyte concentration values were determined. The optimized ZnO/MWCNT nanocomposite was reinforced by polyaniline (PANI) nanofibers through the potential cycling technique, and the morphology, elemental composition, and phase structure of the fabricated nanocomposites were characterized by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD), respectively. The sensing mechanism of the PANI/ZnO/MWCNT electrode for the electrochemical detection of glucose was investigated, and the limit of detection and sensitivity values of the designed sensor were determined. The fast response time of the ternary nanocomposite-based sensor as well as its satisfactory stability and reproducibility, makes it a promising candidate for non-enzymatic detection of glucose in biomedical, environmental, and industrial applications.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Akshaya Kumar Aliyana ◽  
S. K. Naveen Kumar ◽  
Pradeep Marimuthu ◽  
Aiswarya Baburaj ◽  
Michael Adetunji ◽  
...  

AbstractWe report a machine learning approach to accurately correlate the impedance variations in zinc oxide/multi walled carbon nanotube nanocomposite (F-MWCNT/ZnO-NFs) to NH4+ ions concentrations. Impedance response of F-MWCNT/ZnO-NFs nanocomposites with varying ZnO:MWCNT compositions were evaluated for its sensitivity and selectivity to NH4+ ions in the presence of structurally similar analytes. A decision-making model was built, trained and tested using important features of the impedance response of F-MWCNT/ZnO-NF to varying NH4+ concentrations. Different algorithms such as kNN, random forest, neural network, Naïve Bayes and logistic regression are compared and discussed. ML analysis have led to identify the most prominent features of an impedance spectrum that can be used as the ML predictors to estimate the real concentration of NH4+ ion levels. The proposed NH4+ sensor along with the decision-making model can identify and operate at specific operating frequencies to continuously collect the most relevant information from a system.


The Analyst ◽  
2017 ◽  
Vol 142 (12) ◽  
pp. 2128-2135 ◽  
Author(s):  
Brince Paul K ◽  
Asisa Kumar Panigrahi ◽  
Vikrant Singh ◽  
Shiv Govind Singh

A flexible, lightweight and disposable chemiresistive biosensor for label free detection of the malaria biomarker.


2020 ◽  
Vol 32 (10) ◽  
pp. 2183-2192
Author(s):  
K. S. Siddegowda ◽  
B. Mahesh ◽  
N. A. Chamaraja ◽  
B. Roopashree ◽  
N. Kumara Swamy ◽  
...  

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