This study examines the removal of ciprofloxacin in an aqueous solution using green tea silver nanoparticles (Ag-NPs). The synthesized Ag-NPs have been classified by the different techniques of SEM, AFM, BET, FTIR, and Zeta potential. Spherical nanoparticles with average sizes of 32 nm and a surface area of 1.2387m2/g are found to be silver nanoparticles. The results showed that the ciprofloxacin removal efficiency depends on the initial pH (2.5-10), CIP (2-15 mg/L), temperature (20-50°C), time (0-180 min), and Ag-NPs dosage (0.1-1g/L). Batch experiments revealed that the removal rate with ratio (1:1) (w/w) were 52%, and 79.8% of the 10 mg/L of CIP at 60, and 180 minutes, respectively with optimal pH=4. Kinetic models for adsorpti

Antibacterial substances belong to a group of compounds that attack dangerous microorganisms. Therefore, killing bacteria or reducing their metabolic activity will lessen their adverse effects on a biological system. They originated from either synthetic materials, microbes, or mold. Many of these medications treat the gram-negative bacteria from the critical precedence group, such as pseudomonas, carbapenem-resistant acinetobacter, and enterobacterales. This study aims to investigate the simultaneous analysis of specific antibacterial spectrophotometrically. The WHO maintains this list of priority infections with antibiotic resistance. Drug combinations in single dosage forms are becoming increasingly popular in the pharmaceutical industry

 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the

 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the others

HCl is separated from HCl –H₂SO₄  solution by membrane distillation process(MD). The flat –sheet membranes made from polyvinylidene fluoride (PVDF) and polypropylene (pp.). Plate and frame these types of membrane where used in the process. The feed is a mixture of HCl and H₂SO₄ acids compositions depended on metals treated object.HCl concentration increased in the permeate during the process but sulfuric acid increased gradually in the feed .During the concentration of solution acids concentrations in the feed at the beginning were 50 g/dm³ of sulfuric acid and 50 g/dm³ of hydrochloric acid at 333K feed temperature the permeate flux was 71 dm

In this paper a new idea was introduced which is finding a new distribution from other distributions using mixing parameters; w_i  where  0 < w_i < 1 ­and . Therefore we can get many mixture distributions with a number of parameters. In this paper I introduced the idea of a mixture Weibull distribution which is produced from mixing two Weibull distributions; the first with two parameters, the scale parameter , and the shape parameter,  and the second also has the scale parameter , and the shape parameter,  in addition to the location parameter, . These two distributions were mixed using a new parameter which is the mixing parameter w which represents the proportion

A sensitivity-turbidimetric method at (0-180^o) was used for detⁿ. of mebeverine in drugs by two solar cell and six source  with C.F.I.A.. The method was based on the formation  of  ion pair for the pinkish banana color  precipitate by the reaction of Mebeverine hydrochloride with Phosphotungstic acid. Turbidity was measured via the reflection of incident light that collides on the surface particles of   precipitated at 0-180^o. All variables were optimized. The linearity ranged of Mebeverine hydrochloride was 0.05-12.5mmol.L^-1, the L.D. (S/N= 3)(3S_B) was 521.92 ng/sample depending on dilution for the minimum concentration , with correlation coefficient r = 0.9966while was R.S.D%

A new simple and sensitive spectrophotometric method is described for quantification of Nifedipine (NIF) and their pharmaceutical formulation. The selective method was performed by the reduction of NIF nitro group to yield primary amino group using zinc powder with hydrochloric acid. The produced aromatic amine was submitted to oxidative coupling reaction with pyrocatechol and ammonium ceric nitrate to form orange color product measured spectrophotometrically with maximum absorption at 467nm. The product was determined through flow injection analysis (FIA) system and all the chemical and physical parameters were optimized. The concentration range from 5.0 to 140.0 μg.mL-1 was obeyed Beer’s law with a limit of detection and quantitatio