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-

The present work reports the performance of three types of polyethersulfone (PES) membrane in the removal of highly polluting and toxic lead Pb2+ and cadmium Cd2+ ions from a single salt. This study investigated the effect of operating variables, including pH, types of PES membrane, and feed concentration, on the separation process. The transport parameters and mass transfer coefficient (k) of the membranes were estimated using the combined film theory-solution-diffusion (CFSD), combined film theory-Spiegler-Kedem (CFSK), and combined film theory-finely-porous (CFFP) membrane transport models. Various parameters were used to estimate the enrichment factors, concentration polarization modulus, and Péclet number. The pH values signif

Histone deacetylase inhibitors with zinc binding groups often exhibit drawbacks like non-selectivity or toxic effects. Thus, there are continuous efforts to modify the currently available inhibitors or to discover new derivatives to overcome these problems. One approach is to synthesize new compounds with novel zinc binding groups. The present study describes the utilization of acyl thiourea functionality, known to possess the ability to complex with metals, to be a novel zinc binding group incorporated into the designed histone deacetylase inhibitors. N-adipoyl monoanilide thiourea (4) and N-pimeloyl monoanilide thiourea (5) have been synthesized and characterized successfully. They showed inhibition of growth of human colon adenoc

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