In this study the as-deposited and heat treated at 423K of conductive blend graphene oxide (GO)/ poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) thin films was prepared with different PEDOT:PSS concentration (0, 0.25, 0.5, 0.75 and 1)w/w on pre-cleaned glass substrate by spin coater. The XRD analysis indicate the existence of the preffered peak (001) of GO around 2θ=8.24° which is domain in all GO/ PEDOT:PSS films characterized for GO, this result approve the good quality of the PEDOT:PSS dispersion in GO, this peak shifted to the lower 2θ with increasing PEDOT:PSS concentration and after annealing process. The scanning electron microscopy (SEM) images and atomic force microscopy (AFM) clearly sh

Reducing of ethyl 4-((2-hydroxy-3-methoxybenzylidene)amino)benzoate (1) afford ethyl 4-((2-hydroxy-3-methoxybenzyl)amino)benzoate (2). Reaction of this compound with Vilsmeier reagent affords novel 2-chloro-[1,3] benzoxazine ring (3). The corresponding acid hydrazide of compound 3 was synthesized from reaction of compound (3) with hydrazine hydrate. Newly series of hydrazones (5a–i) were synthesized from reaction of acid hydrazide with various aryl aldehydes. Antibacterial activity of the hydrazones was secerned utilizing gram-negative and gram-positive bacteria. Compound (5b) and (5c) exhibited significant antibacterial ability against both gram-negative and gram-positive bacteria, while the compounds (5a) showed mild antibacteri

This study shows that it is possible to fabricate and characterize green bimetallic nanoparticles using eco-friendly reduction and a capping agent, which is then used for removing the orange G dye (OG) from an aqueous solution. Characterization techniques such as scanning electron microscopy (SEM), Energy Dispersive Spectroscopy (EDAX), X-Ray diffraction (XRD), and Brunauer-Emmett-Teller (BET) were applied on the resultant bimetallic nanoparticles to ensure the size, and surface area of particles nanoparticles. The results found that the removal efficiency of OG depends on the G‑Fe/Cu‑NPs concentration (0.5-2.0 g.L-1), initial pH (2‑9), OG concentration (10-50 mg.L-1), and temperature (30-50 °C). The batch experiments showed

New Azo compounds containing an 1,8-naphthalimide moiety were synthesized from 1, 8-naphthalic anhydride by a reaction with p-phenylenediamine or benzidine to produce 1,8-naphthalimide derivatives (1 or 2), which were converted to diazonium salt derivatives by using sodium nitrite and acetic acid at 0-5 áµ’C. The diazonium salt was subjected to a coupling reaction with different substituted phenol in alkaline media at 0-5 áµ’c to produce azo compound derivatives (3-14).

The New Azo compound derivatives (3-14) were identified by ¹H-NMR, ¹³C-NMR, and FTIR and by measuring characteristic physical properties and specific reactions. Also, the ability of the prepared azo compounds to work as acid-

Promoting the production of industrially important aromatic chloroamines over transition-metal nitrides catalysts has emerged as a prominent theme in catalysis. This contribution provides an insight into the reduction mechanism of p-chloronitrobenzene (p-CNB) to p-chloroaniline (p-CAN) over the γ-Mo2N(111) surface by means of density functional theory calculations. The adsorption energies of various molecularly adsorbed modes of p-CNB were computed. Our findings display that, p-CNB prefers to be adsorbed over two distinct adsorption sites, namely, Mo-hollow face-centered cubic (fcc) and N-hollow hexagonal close-packed (hcp) sites with adsorption energies of −32.1 and −38.5 kcal/mol, respectively. We establish that the activation of nit

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F