Gerald Ensang Timuda

In this study, we employed phenyl-C ₆₁ -butyric acid methyl ester (PCBM)-coated ZnO nanorod arrays (ZnO/PCBM) as photoanodes to enhance water splitting in the photoelectrochemical (PEC) process.

The intricate competition between seed formation and the growth-limited area in 2D transition metal dichalcogenides has long hindered their potential applications. This study investigated the morphological and photophysical characteristics of few-layer tungsten disulfide (FL-WS₂) thin films deposited on SiO₂/Si substrates via pulsed laser deposition (PLD). To induce morphological changes, the number of laser pulses was varied (50, 150, and 300 shots), followed by a post-deposition annealing treatment. Atomic force microscopy (AFM) demonstrated three distinct morphological features: continuous films, particulates, and islands, indicative of a growth evolution consistent with the Stranski–Krastanov (layer-plus-island) mechanism. X-ray photoelectron spectroscopy (XPS) revealed the presence of a semiconducting phase of 2H-WS₂ and sulphur-deficient species (WS_x) in the films. While Raman modes characteristic of WS₂ were observed, the absence of photoluminescence (PL) emission suggested the presence of defect states in the as-grown thin films. Upon post-annealing treatment, we observed a reduction in WS_x species (150 and 300 laser shots) accompanied by a reduction of particulate density. This, in turn, triggers a defect state suppression, by which the PL spectra of all samples emerge, unveiling the near-resonance emission and excitonic features. This observation was confirmed by the density functional theory (DFT) simulation, pointing to the presence of an extra energy level around the K point of the Brillouin zone at high concentration of sulphur vacancy, which corresponds to the splitting of near-resonant PL emission. In addition, two pronounced Raman modes at 264 cm⁻¹ and 334 cm⁻¹ are associated with structural relaxation due to post-annealing. This relationship between morphological characteristics and emissive behaviour of large-area FL-WS₂ thin films provides valuable insights into their photophysical modulation, paving the way for advanced photonic applications.

The increasing adoption of electric vehicles (EVs) reflects growing global awareness of climate change and air pollution challenges. As a sustainable alternative to conventional internal combustion vehicles, EVs produce zero tailpipe emissions and can significantly reduce carbon emissions—particularly when powered by renewable energy sources. However, one of the primary barriers to widespread EV adoption remains the high cost of battery components, which are essential to vehicle performance and energy storage. In Indonesia, two dominant battery types used in EVs are Lithium Ferro Phosphate (LFP) and Nickel Manganese Cobalt (NMC), each offering distinct advantages. LFP batteries are recognized for their thermal stability and longer life cycles, making them suitable for everyday use, while NMC batteries offer higher energy density and are preferred for performance-focused and long-distance applications. This study aims to evaluate the correlation between battery capacity, driving range, and charging time for LFP and NMC batteries using Pearson correlation and regression analysis through MATLAB simulation. The results indicate a strong and statistically significant correlation among the key parameters, with a Pearson coefficient of 0.576 for battery capacity and range, and an R-square value of 0.99 for the regression model, demonstrating high predictive accuracy. Furthermore, the analysis reveals that LFP batteries have a higher average energy efficiency of 7.53 km/kWh compared to 6.84 km/kWh for NMC batteries, indicating more consistent performance in energy usage. These findings offer valuable insights for optimizing battery selection in EV applications and contribute to strategic planning for the development of more efficient electric vehicle systems. The combination of statistical and simulation-based analysis provides a robust foundation for future research and policy-making in the field of electric mobility.

This study investigates the mechanical properties of a stainless steel 304 linear guide rail using a combination of Autodesk Inventor and MATLAB. The primary objective is to analyze the von Mises stress distribution, displacement, and safety factor of the linear guide rail under varying load conditions, as well as to develop a model representing the relationship between stress and strain. A detailed 3D model of the guide rail was created using Autodesk Inventor, followed by finite element analysis (FEA) to evaluate stress and strain distribution across different sections of the rail. The simulation was conducted to assess the structural response under multiple loading scenarios, ensuring its reliability for real-world applications. Furthermore, a linear regression analysis was performed using MATLAB to establish a predictive model correlating stress and strain, enabling more accurate forecasting of the material's mechanical behavior. The results revealed that the maximum von Mises stress obtained from the simulation was 23.595 MPa, with a corresponding maximum displacement of 0.397 mm. The safety factor analysis confirmed the rail's structural integrity, with a minimum safety factor of 10.595, well above the failure threshold. These findings indicate that the linear guide rail meets the necessary mechanical performance requirements for its intended application.

Vehicle vibration is a critical factor influencing both passenger comfort and vehicle performance. In this study, we analyze the multi-degree-of-freedom (MDOF) vibrational behavior of a multi-purpose vehicle (MPV) using matrix eigenvalue and eigenvector methods. The vehicle’s dynamics are modeled by developing a set of equations of motion that account for the forces acting on the front and rear tires, car body, and pitch angle. MATLAB is utilized to numerically compute the system’s eigenvalues and eigenvectors, representing the natural frequencies and vibration modes of the vehicle, respectively. The analysis focuses on the vehicle’s response to a 50 mm displacement at the front tire, simulating the effect of road disturbances. The resulting vibrations in the front and rear tires, car body, and vehicle pitch are illustrated over a 1-second time frame. The findings show that the front tire experiences the largest oscillation amplitude of ±1 mm, while the rear tire exhibits a much smaller displacement of ±0.04 mm. The overall car body displacement reaches a maximum amplitude of ±1.3 mm, indicating partial damping of the front tire vibrations. However, the results reveal that the vehicle’s suspension system lacks effective damping, as the vibrations do not decrease over time. This behavior could negatively impact ride comfort and safety, particularly on uneven roads. The study concludes that improvements to the vehicle’s suspension system are necessary to enhance damping performance. The presented MATLAB-based approach offers a valuable tool for analyzing and optimizing vehicle vibration systems.