National University of Engineering

The development of novel, highly efficient, ecological inhibitors against corrosion in acidic environments is of paramount importance in the oil industry because the oil producing companies industry strongly rely on acidic treatments for infrastructure maintenance and cleaning operations. This study employed grape stalk extract-mediated silver nanoparticles (GS/AgNps), synthesized using aqueous extract of grape stalks (AEGS), as a green inhibitor of corrosion in 304 stainless steel (304SS). The GS/AgNps were characterized using UV–visible spectroscopy (UV–Vis), Fourier–transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), scanning transmission electron microscopy (STEM), and dynamic light scattering (DLS). The GS/AgNps obtained exhibited a crystalline structure with spherical morphology, with an average diameter of 24.59 nm and a surface plasmon resonance centered at 412 nm. Through the application of SEM, weight loss, electrochemical impedance spectroscopy (EIS), potentiodynamic polarization (PDP), and energy-dispersive X-ray spectroscopy (EDS) analyses, the corrosion inhibition potential of GS/AgNps and AEGS was evaluated in 304SS using 0.5 M HCl as the supporting electrolyte at room temperature and at elevated temperatures. The proposed GS/AgNps recorded a higher maximum surface coverage (θ) of 97.91 ± 0.38

Recent research has emphasized the potential of combining different electrode materials to enhance the performance of asymmetric supercapacitors for energy storage applications. In this study, an asymmetric supercapacitor was developed using cobalt sulfide (CoS)-embedded activated carbon (AC), denoted as CoS@AC, as the positive electrode, while AC alone served as the negative electrode. Initially, micro-flower morphology of CoS was synthesized via a hydrothermal method, and layered morphology of AC was prepared through the carbonization of Acorus calamus. Then CoS@AC nanocomposite was fabricated using a wet impregnation method and its structural, morphological analysis was carried out. The morphological analysis of CoS@AC nanocomposites confirmed the presence of both micro-flower morphology of CoS and layered morphology of AC structures. The TEM analysis of CoS@AC nanocomposite revealed the presence of both micro-flower-like (CoS) and layered-like structure (AC), and the HRTEM analysis showed an interplanar spacing of 0.236 nm related to CoS (101) XRD diffraction. The BET analysis of CoS@AC nanocomposites shows a nearly type-I isotherm with a surface area of about 1145 m2/g, an average pore size of about 4.23 nm, and a pore volume of 0.451 cc/g. Finally, the CoS@AC‖AC electrode demonstrated enhanced electrochemical performance, achieving a specific capacitance of approximately 234 F/g, an energy density of 83.2 Wh/kg, and a power density of 16,089 W/kg. Further, the stability analysis was carried out for 2000 cycles, which showed better stability performance. These results strongly recommend that the CoS@AC‖AC system is favorable for asymmetric supercapacitor applications.

This research addresses the critical need to study human capital and organizational design in the construction industry. To overcome the scarcity of literature on the functionality and responsiveness of organizational charts, this study carries out a comparative analysis of large-scale construction projects in Chile and Peru. Using a multiple-case study approach and a Complex Adaptive Systems (CAS) framework, the research examined how organizational structures configure and adapt to local contexts through semi-structured interviews, exploring the influence of external factors such as regulations, the market, and culture on the design of organizational charts. The results reveal that while Peruvian structures tend toward centralization influenced by cultural factors, Chilean structures exhibit greater horizontality. Furthermore, Lean philosophy emerged as a key contextual factor that facilitates organizational adaptability in complex environments. The study concludes that organizational adaptability is not merely a product of formal design, but an emergent property driven by the interaction between formal roles and management practices. These findings contribute to a deeper understanding of how construction firms can configure their structures to better respond to project complexity. For future research, it is proposed to explore the correlation between the complexity of organizational charts and project performance, using quantitative and qualitative indicators, and analyzing the evolution of organizational structures throughout the project life cycle. In addition, it is suggested to investigate the impact of digitalization and agile methodologies, as well as to conduct longitudinal studies to assess the resilience and adaptability of organizational charts to regulatory and market changes.

Semi-empirical quantum-mechanical (QM) methods have become valuable tools for studying complex (bio)molecular systems due to their balance between computational efficiency and accuracy. A key aspect of these methods is their parameterization, which not only governs the reliability of the results but also provides an opportunity to enhance their overall performance. In our previous work [J. Phys. Chem. Lett., 2021, 11, 16], we advanced the third-order semi-empirical density functional tight-binding (DFTB3) method for computing multiple properties of small molecules by developing the machine learning (ML) potential NNrep to bridge the gap between DFTB3 electronic components and those of the hybrid DFT-PBE0 functional. To overcome the limitations of NNrep, we introduce the EquiDTB framework, which leverages physics-inspired equivariant neural networks (NN) to parameterize scalable and transferable many-body ΔTB potentials, replacing the standard pairwise DFTB repulsive potential. This advancement extends the applicability of our ML-corrected DFTB approach to larger molecules and non-covalent systems (including only C, N, O, and H atoms), going beyond the chemical space represented in the training QM datasets. The enhanced performance of EquiDTB over the standard TB methods is demonstrated by the accurate computation of the atomic forces of S66x8 molecular dimers, as well as their interaction energies. Moreover, EquiDTB can be effectively employed to explore the potential energy surfaces of large and flexible drug-like molecules-for example, to determine the minimum energy path between isomers, analyze structural transitions during dynamical simulations, compute vibrational modes, and investigate energetic rankings. The performance for single molecules slightly decreases when the DFTB electronic energy is reduced to first-order but remains superior to standard TB methods. Our work thus demonstrates that an optimal integration of an equivariant NN with QM datasets can advance the DFTB method while maintaining high efficiency, paving the way for reliable (bio)molecular simulations.

In previous work, we developed a method for computing two-point correlators by decomposing the mode degrees of freedom into fast and slow components. Building on this framework, we present a numerical implementation to study the evolution of primordial scalar perturbations under controlled state deformations induced by the simplest environment corrections from the Lindblad equation. The primary contribution of this work is the development of a numerically stable and flexible framework for implementing open-system effects in inflationary perturbations, rather than the extraction of concrete predictions for the primordial spectrum. Our approach generalizes to an arbitrary number of degrees of freedom and does not rely on the slow-roll approximation. The computational routine is numerically efficient and allows users to configure arbitrary sequences of decoherence events, with full control over their duration, shape, amplitude, and effective wavelength range. The resulting outputs are compatible with nonlinear numerical codes, enabling studies of how decoherence effects propagate during reheating.