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Evaluation and characterization of the symbiotic effect of benzylidene derivative with titanium dioxide nanoparticles on the inhibition of the chemical corrosion of mild steel
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A new benzylidene derivative, namely N-benzylidene-5-phenyl-1,3,4-thiadiazol-2-amine (BPTA), has been synthesized and instrumentally confirmed with Elemental Analysis (CHN), Nuclear Magnetic Resonance (NMR), and Fourier Transform Infrared Spectroscopy (FT-IR). Titanium Dioxide (TiO2) nanoparticles (NPs) were synthesized and characterized by X-ray. The mutualistic complementary dependence of BPTA with TiO2 nanoparticles as anti-corrosive inhibitor on mild steel (MS) in 1.0 M hydrochloric acid has been tested at various concentrations and various temperatures. The methodological work was achieved by gravimetric measurement methods complemented with surface analysis. The synthesized inhibitor concentrations were 0.1 mM to 0.5 mM and the temperatures ranging from 303–333 K. The BPTA with TiO2-NP as a synergistic inhibitor becomes superior inhibitive effects with more than 96% inhibition competence of MS coupons in a harsh acidic medium. The efficiency of the inhibition improved with increasing BPTA content and also increase with the Synergistic effects of BPTA with TiO2-NP. The excellent effectiveness was performed with the 0.5 mM concentration of BPTA and become higher with adding of TiO2-NP rising to the maximum inhibition efficiency (IE). However, the inhibition efficacy declined as the temperature rises. Results of BPTA as corrosion inhibitor indicated the obedience of the adsorption of the inhibitor of mixed type on the surface of MS to Langmuir adsorption isotherm. It was found that the BPTA and performance depend on the Synergistic effects, concentrations of the TiO2-NP and BPTA, in addition to the solution temperature. Nevertheless, the quantum calculations have confirmed the direct correlation of the electronic characteristics of BPTA with the corrosive inhibitive influence.

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Publication Date
Thu Jul 02 2026
Journal Name
Innovative Construction And Petrochemical Technologies
Improving Arabic Text Classification Accuracy Using Lightweight NLP Techniques
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Arabic text classification is a challenging task because of the complex morphology of the language, the existence of different writing forms and a multitude of dialects, which can result in sparser common text representations. While transformer models such as AraBERT have obtained superior results on many Arabic NLP tasks, their high computational requirements make them difficult to deploy in environments with limited hardware resources. In some cases this can also make the model less practical for researchers working with basic computer systems. This study focuses on a more practical issue: how much accuracy a simple classifier may lose when the amount of required computation is reduced. We use a combined TF-IDF representation based on bo

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