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This study introduces novel phenothiazine-based organic dyes, 2-LBH-100, 2-LBH-44, and 2-Ryu-4, specifically designed for quasi-solid-state dye-sensitized solar cells (QsDSSCs). Employing a donor-π-acceptor architecture, these dyes incorporate varying electron-donating moieties, including bis(3-(hexyloxy)phenyl)amine and diphenylamino, coupled with a cyanoacrylic acid acceptor. Alkoxy substitutions in 2-LBH-100 and 2-LBH-44 enhanced solubility and dye loading on TiO2, leading to improved performance in QsDSSCs. 2-LBH-100 exhibited a power conversion efficiency (PCE) exceeding 5% with excellent stability, while 2-LBH-44 demonstrated a PCE of over 3%, increasing to 4% over time. 2-Ryu-4, with its diphenylamino donor, achieved an initial PCE of over 6%. This research highlights the crucial role of donor–acceptor interactions in optimizing organic dye design for high-performance QsDSSCs, paving the way for efficient and stable next-generation solar energy technologies.

The focus of this research lies on fundamental research to provide guidelines for the design of new nanocatalyst toward improvement of the performance of proton exchange membrane fuel cells (PEMFCs). To achieve this overarching goal, several specific steps were taken with aims to: (1) provide guidelines for the design of new catalysts; (2) promote nanocatalyst applications towards alternative energy applications; and (3) integrate advanced instrumentation into nanocharacterization and fuel cell (FC) electrochemical behavior. In tandem with these goals, the cathode catalysts were extensively refined to improve the performance of PEMFCs and minimize noble metal usage. In this study, the major accomplishment was producing aligned carbon nanotubes (ACNTs), which were then modified by platinum (Pt) nanoparticles via a post-functionalization colloidal chemistry approach. The Pt-ACNTs demonstrated improved cathodic catalycity, by building better device endurance and decreased Pt loading. It was also determined that surface mechanical properties, such as elastic modulus and hardness were increased. Collectively, these enhancements provided an improved FC device. The electrochemical analyses indicated that the power density of the PEMFCs was increased to 900 mW/cm2 and current density to 3000 mA/cm2, respectively. The Pt loading was controlled at lower than 0.2 mg/cm2 to decrease the manufacturing expenses.

Recycled polylactic acid (PLAr) was reinforced with treated nanocellulosic hemp fibers for biocomposite fabrication. Cellulosic fibers were extracted from hemp fibers chemically and treated enzymatically. Treated nanocellulosic fibers (NCF) were analyzed by Fourier-transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy. Biocomposite fabrication was done with PLAr and three concentrations of treated NCF (0.1%, 0.25%, and 1% (v/v)) and then studied for thermal stability and mechanical properties. Increased thermal stability was observed with increasing NCF concentrations. The highest value for Young’s modulus was for PLAr + 0.25% (v/v) NCF (250.28 ± 5.47 MPa), which was significantly increased compared to PLAr (p = 0.022). There was a significant decrease in the tensile stress at break point for PLAr + 0.25% (v/v) NCF and PLAr + 1% (v/v) NCF as compared to control (p = 0.006 and 0.002, respectively). No significant difference was observed between treatments for tensile stress at yield.

Inter-turn winding faults in five-phase ferrite-permanent magnet-assisted synchronous reluctance motors (fPMa-SynRMs) can lead to catastrophic consequences if not detected in a timely manner, since they can quickly progress into more severe short-circuit faults, such as coil-to-coil, phase-to-ground or phase-to-phase faults. This paper analyzes the feasibility of detecting such harmful faults in their early stage, with only one short-circuited turn, since there is a lack of works related to this topic in multi-phase fPMa-SynRMs. Two methods are tested for this purpose, the analysis of the spectral content of the zero-sequence voltage component (ZSVC) and the analysis of the stator current spectra, also known as motor current signature analysis (MCSA), which is a well-known fault diagnosis method. This paper compares the performance and sensitivity of both methods under different operating conditions. It is proven that inter-turn faults can be detected in the early stage, with the ZSVC providing more sensitivity than the MCSA method. It is also proven that the working conditions have little effect on the sensitivity of both methods. To conclude, this paper proposes two inter-turn fault indicators and the threshold values to detect such faults in the early stage, which are calculated from the spectral information of the ZSVC and the line currents.

The paper presents a preliminary technical-economic comparison between a 3 kV DC railway and the use of trains with on-board storage systems. Numerical simulations have been carried out on a real railway line, which presents an electrified section at 3 kV DC and a non-electrified section, currently covered by diesel-powered trains. Different types of ESS have been analyzed, implementing the models in Matlab/Simulink environment. A preparatory economic investigation has been carried out.

Lithium-ion batteries exhibit a dynamic voltage behaviour depending nonlinearly on current and state of charge. The modelling of lithium-ion batteries is therefore complicated and model parametrisation is often time demanding. Grey-box models combine physical and data-driven modelling to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling. Differential equations given by physical laws and NODEs can be combined in a single modelling framework. Here we demonstrate the use of NODEs for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis and represents the physical part of the model. The voltage drop over the resistor–capacitor circuit, including its dependency on current and state of charge, is implemented as a NODE. After training, the grey-box model shows good agreement with experimental full-cycle data and pulse tests on a lithium iron phosphate cell. We test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.

Mixing under high pressure conditions plays a central role in several engineering applications, such as direct-injection engines and liquid rocket engines. Numerical flow simulations have become a complementary tool to study the mixing process under these conditions but require complex thermodynamic modeling as well as validation with accurate experimental data. For this reason, we use experiments of supercritical single-phase jet mixing from the literature, where the mixing is quantified by the mixture speed of sound, as a reference for our work. We here focus on the thermodynamic modeling of multi-component flows under high pressure conditions and the analytical calculation of the mixture speed of sound. Our thermodynamic model is based on cubic equations of state extended for multi-components. Using an extension of OpenFOAM, we perform large-eddy simulations of hexane and pentane injections and compare our results with the experimentally measured mixture speed of sound at specific positions. The simulation results show the same characteristic trends, indicating that the mixing effects are well reproduced in the simulations. Additionally, the effect of the sub-grid scale modeling is assessed by comparing results using different models (Smagorinsky, Vreman, and Wall-Adapting Local Eddy-viscosity). The comprehensive simulation data presented here, in combination with the experimental data, provide a benchmark for numerical simulations of jet mixing in high pressure conditions.