In the relentless pursuit of sustainable energy solutions, the petroleum industry faces the imperative challenge of mitigating sulfur emissions. This comprehensive review scrutinizes Titanium Dioxide (TiO₂) as an extraordinary catalyst, pushing the boundaries of desulfurization performance in petroleum refining. The abstract begins by underscoring the urgent need for advanced desulfurization technologies, driven by stringent environmental mandates and escalating global energy demands. The spotlight then shifts to the unparalleled physicochemical attributes of TiO₂, showcasing its inherent advantages such as exceptional surface area, stability, and photocatalytic process. A profound exploration of TiO₂’s catalytic mechanisms follows, unraveling its capacity to disintegrate stubborn sulfur–carbon bonds, thereby elevating desulfurization efficiency to unprecedented levels. This review meticulously dissects diverse forms of TiO₂, ranging from nanoparticles to mesoporous structures, and provides a critical analysis of their respective strengths and limitations in catalyzing sulfur removal. Delving into operational nuances, this review examines the impact of temperature, pressure, and catalyst loading on TiO₂ performance, offering crucial insights for optimizing desulfurization processes. The narrative then unfolds to explore cutting-edge developments in TiO₂-based catalysts, encompassing ingenious modifications, composites, and hybrid materials designed to augment catalytic activity and selectivity. Anticipating the road ahead, this review contemplates the challenges and prospects of deploying TiO₂ on an industrial scale, pointing toward avenues for future research and development. This abstract encapsulates a wealth of knowledge, serving as an indispensable resource for researchers, engineers, and policymakers navigating the dynamic landscape of sustainable petroleum refining. TiO₂ emerges as a transformative force, propelling the industry toward cleaner, greener, and more efficient energy production.

Regarding photocatalytic oxidative desulphurization (PODS), titanium oxide (TiO₂) is a promising contender as a catalyst due to its photocatalytic prowess and long-term performance in desulphurization applications. This work demonstrates the effectiveness of double-doping TiO₂ in silver (Ag) and molybdenum (Mo) for use as a novel catalyst in the desulphurization of light-cut hydrocarbons. FESEM, EDS, and AFM were used to characterize the morphology, doping concentration, surface features, grain size, and grain surface area of the Ag/Mo powder. On the other hand, XRD, FTIR spectroscopy, UV-Vis, and PL were used for structure and functional group detection and light absorption analysis based on TiO₂’s illumination properties. The microscopic images revealed nanoparticles with irregular shapes, and a 3D-AFM image was used to determine the catalyst’s physiognomies: 0.612 nm roughness and a surface area of 811.79 m²/g. The average sizes of the grains and particles were calculated to be 32.15 and 344.4 nm, respectively. The XRD analysis revealed an anatase structure for the doped TiO₂, and the FTIR analysis exposed localized functional groups, while the absorption spectra of the catalyst, obtained via UV-Vis, revealed a broad spectrum, including visible and near-infrared regions up to 1053.34 nm. The PL analysis showed luminescence with a lower emission intensity, indicating that the charge carriers were not thoroughly combined. This study’s findings indicate a desulphurization efficiency of 97%. Additionally, the promise of a nano-homogeneous particle distribution bodes well for catalytic reactions. The catalyst retains its efficiency when it is dried and reused, demonstrating its sustainable use while maintaining the desulphurization efficacy. This study highlights the potential of the double doping approach in enhancing the catalytic properties of TiO₂, opening up new possibilities for improving the performance of photo-oxidative processes.

Taguchi method and a regression model have been developed to study the effect of AL% filler contents on the glass/polyester honeycomb sandwich panel behavior subjected to impact load. Using the standard Taguchi’s array L9, analysis of means (ANOM) including main effects of S/N ratio and experimental response, ANOVA was used to determine the optimum process parameters with its significant factor. Three parameters as controlled variables have considered: AL%, mass, and height with three levels for each of them in destructive and non-destructive tests. The results show that for deflection: the mass has the highest contribution at (58.2439%) followed by the AL% filler (21.3718%). The AL% has the highest contribution for the deformation, with a percentage of (89.1144%) followed by the height and mass with a relative contribution to each other. The regression model has a good prediction, with a perfect correlation between the output and input variables. The coefficient of determination and the correlation coefficient are 89.88 % and 0.94, respectively, for deflection, while for deformation are 81.45 % and 0.9, respectively. The average error MAPE equals 6.19 % and 12.128 % for deflection and deformation, respectively. There is a strong relationship between the variables depending on the correlation coefficient, which presents as 0.94 for deflection and 0.9 for deformation. Confirmation results show that the experimental and predicted values were close to each other.

Additive manufacturing (AM), particularly Fused Deposition Modeling (FDM), has revolutionized polymer-based fabrication through design freedom and material efficiency. This work presents a comprehensive statical optimization of FDM parameters affecting the flexural properties of acrylonitrile/butadiene/styrene (ABS) specimens. The effects of layer thickness (0.15–0.25 mm), infill density (30–70%), printing speed (35–95 mm/s), and build orientation (Flat, On-edge, Vertical) were investigated following ASTM D790 standards. A Taguchi L9 orthogonal array coupled with ANOVA analysis was employed to quantity parameter significance. According to the ANOVA analysis, infill density was identified as the most influential parameter, accounting for 61.3% of the variation in flexural strength (σ_f) and 60.1% in flexural modulus (E_b). The optimal configuration (0.25 mm layer thickness, 70% infill, 65 mm/s speed, horizontal orientation) yielded a flexural strength of 84.9 MPa and modulus of 2.54 GPa. Microstructural observations confirmed that higher infill and moderate speed improved interlayer fusion and reduced void formation. The developed Taguchi–ANOVA framework offers quantitative insights for tailoring process–structure–property relationships in polymer-based additive manufacturing.