Simultaneous determination of V, Ni, Ga and Fe in fuel fly ash using solid sampling high resolution continuum source graphite furnace atomic absorption spectrometry

A green and simple method has been proposed in this work for the simultaneous determination of V, Ni, Ga and Fe in fuel ash samples by solid sampling high resolution continuum source graphite furnace atomic absorption spectrometry (SS HR CS GFAAS). The application of fast programs in combination with direct solid sampling allows eliminating pretreatment steps, involving minimal manipulation of sample. Iridium treated platforms were applied throughout the present study, enabling the use of aqueous standards for calibration. Correlation coefficients for the calibration curves were typically better than 0.9931. The concentrations found in the fuel ash samples analyzed ranged from 0.66 to 4.2 % for V, 0.23 to 0.7 % for Ni, 0 to 5.4 mg/Kg for Ga and 0.10 to 0.60 % for Fe. Precision (%RSD) were 5.2, 10.0, 20.0 and 9.8% for V, Ni, Ga and Fe, respectively, obtained as the average of the %RSD of six replicates of each fuel ash sample. The optimum conditions established were applied to the determination of the target analytes in fuel ash samples. In order to test the accuracy and applicability of the proposed method in the analysis of samples, five ash samples from the combustion of fuel in power stations, were analysed. The method accuracy was evaluated by comparing the results obtained using the proposed method with the results obtained by ICP OES previous acid digestion. The results showed good agreement between them

APPLICATION OF SIGN LANGUAGE RECOGNITION USING MACHINE LEARNING

Many persons with illnesses including those that render them dumb, deaf, or blind are seen daily. Interacting with people is tough for them. Sensor-based approaches that have been previously developed all failed to provide the overall answer. The proposed project's main objective is to develop a system that employs machine learning to give voiceless communication through a system that is both affordable and effective. The recommended technique translates sign language into text by utilizing OpenCV and CNN. There will be easier communication between the two groups as a result.</jats:p