For aspirin estimated, a molecularly imprinted polymer MIP-ASP electrodes were generated by electro-polymerization process, the electrodes were prepared by combining the template (aspirin) with (vinyl acetate (VA), 1-vinylimidizole (VIZ) as a functional monomer and N, N-methylene bisacrylamide (MBAA) as crosslinkers using benzoyl peroxide (BPO) as an initiator. The efficiency of the membrane electrodes was analyzed by differential pulse voltammetry (DPV). Four electrodes were synthesized using two different plasticizers, di-butyl sebacate (DBS), di-octyl phthalate (DOP) in PVC matrix. Scanning electron microscopy (SEM) was used to describe the generated MIP, studying the electrodes properties, the slope, detection limit, and life time and linearity range. The effect of PH and interferes on the efficiency of the MIP electrode was investigated. The study has shown that the molecularly imprinted electrodes have high sensitivity and responsiveness to aspirin. The DPV value was linearly dependence on the aspirin concentration and a linear curve was obtained within the range of (1×10−1-5×10-4) M of aspirin with correlation coefficients are about (0.9974, 0.9966, 0.9938 and 0.9961) with slops value of (-21.41, -17.67, -17.47 and -18.67) and the detection limit for all electrodes ranging from (7.5×10-5-1×10-4) M. The molecularly imprinted electrode exhibited a good response with highly reproducible and no effect on interferes frequently available in pharmaceutical formulations. The approach employed is easy and fast. Also ASP membranes get a limited time of response, excellent mechanical stability, removable and are easy to construct.
Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob
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Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de
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In this research we investigated the corrosion behavior of the commertialy pure titanium and Ti-6Al-4V alloy that coated with hydroxyapatite by electrochemical deposition with applied voltage (6,9,12) Volt from aqueous solution containing Ca(NO3)2.H2O =7.0 gm/l , (NH4)2HPO4 =3.5 gm/l , Na(NO3)2 = 8.5 gm/l in order to improve the bonding strength of hydroxyapetite and medical metals and alloys and increasing the biocompatibility. The coating layer morphology was investigated by XRD, Optical microscope , and SEM tests, the corrosio tests was made by use senthesys simulated body fluid (SBF) , and we found that the propreate voltage for coatint on Ti was 9 Volt and for Ti-6Al-4Vwas12Volt.
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