A highly selective chemisensor for 2-nitrophenol detection was fabricated using ZnO/RuO<sub>2</sub> nanoparticles (NPs) synthesized by impregnation method. The as-synthesized NPs were characterized through UV-vis diffuse reflectance spectroscopy, X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FESEM), Energy dispersive X-ray spectroscopy (EDS), FTIR and X-ray diffraction (XRD). A glassy carbon electrode was modified with as-synthesized ZnO/RuO<sub>2</sub> nanoparticles and utilized as a chemical sensor for the detection of 2-nitrophenol. The fabricated sensor exhibited excellent sensitivity (18.20 μA μM<sup>-1</sup> cm<sup>-2</sup>), good reproducibility, short response time (8.0 s.), the lowest detection limit (52.20 ± 2.60 pM) and long-term stability in aqueous phase without interference effects. Finally, the fabricated sensor was validated as a 2-NP probe in various environmental water samples at room conditions.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Abstract — Among the vast range of off–line optical character recognition applications is the machine processing of forms. The objective is to imitate the human ability to read but at higher speed. This paper presents a neural network based system to recognise handwritten Arabic characters collected from more than 500 forms. Wavelet coefficients extracted from the character samples are used to tune the neural network weights. The performance of the system is assessed using a corpus that includes 15800 character samples.
Herein, we report the construction of a dynamic, highly sensitive, stable, reliable, and reproducible selective ethanol sensor based on a ternary metal oxide system of CdO/ZnO/Yb<sub>2</sub>O<sub>3</sub>nanosheets (NSs).
Machine learning and data analysis together represent a powerful force in producing analysis and forecasting models which in turn support the growth of different fields. In terms of safety and people, road traffic accidents have a negative impact on individuals, governments, and companies as well. Therefore, predicting traffic accidents on the road has become very important in the decision-making process that will lead to the safety of others. It is also important to find the causes of these accidents. This study aims to predict road traffic accidents and their causative factors based on a data set from Kaggle [1]. And the establishment of a vehicle accident prediction model on the road using machine learning and a decision tree algorithm, and the results showed that one of the most important factors causing accidents on the road is the lack of distance between vehicles, and that weather and age play an important role, and the model achieved an accuracy of 84.22% and a standard deviation of 1.08 %+/-. The tools used in data analysis and machine learning and prediction are Python and RapidMiner an integrated software platform for data science that provides an integrated environment for machine learning, deep learning, and predictive analysis.
In the title compound, C(13)H(12)N(2)O(3), the dihedral angle between the aromatic rings is 7.94 (12)°. In the crystal, inversion dimers linked by pairs of C-H⋯O hydrogen bonds generate R(2) (2)(6) loops. Weak π-π [centroid-centroid separations = 3.7480 (13) and 3.9047 (13) Å] and C-H⋯π inter-actions help to consolidate the packing.
Multiwalled carbon nanotubes (MWCNTs) and Vulcan carbon (VC) decorated with SnO2 nanoparticles were synthesized using a facile and versatile sonochemical procedure. The as-prepared nanocomposites were characterized by means of transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and Fourier transform infra red spectroscopy. It was evidenced that SnO2 nanoparticles were uniformly distributed on both carbon surfaces, tightly decorating the MWCNTs and VC. The electrochemical performance of the nanocomposites was evaluated by cyclic voltammetry and galvanostatic charge/discharge cycling. The as-synthesized SnO2/MWCNTs nanocomposites show a higher capacity than the SnO2/VC nanocomposites. Concretely, the SnO2/MWCNTs electrodes exhibit a specific capacitance of 133.33 F g(-1), whereas SnO2/VC electrodes exhibit a specific capacitance of 112.14 F g(-1) measured at 0.5 mA cm(-2) in 1 M Na2SO4.