Sebelas Maret University

This study investigates the effect of Fe metal loading and support type on the characteristics and catalytic behavior of Fe-based catalysts to produce gasoline-like hydrocarbons (GHCs) via the hydrodeoxygenation (HDO) of oleic acid. The catalysts were prepared by the wet impregnation method using HZSM-5, mordenite (MOR), and amorphous silica-alumina (SiAl) supports, with Fe loadings of 3, 6, and 9 wt%. The HDO reaction was conducted under mild operating conditions at atmospheric pressure. Characterization results indicate that Fe impregnation does not significantly alter the crystalline structure of the supports, as confirmed by XRD analysis. However, SEM observations reveal an increase in surface roughness after metal loading. An increase in Fe content leads to a decrease in surface area and total acidity, likely due to partial pore blockage and reduced accessibility of acid sites. Catalytic performance evaluation shows that both catalytic and non-catalytic experiments achieve high oleic acid conversion (>95%), indicating a significant contribution from thermal decomposition under the applied reaction conditions. Among all catalysts, Fe6/HZSM-5 exhibits the highest GHCs selectivity of 73%, outperforming Fe6/MOR (46.60%) and Fe9/SiAl (56.20%). The superior performance of Fe6/HZSM-5 is attributed not solely to acidity, but rather to the combined effects of pore structure, metal-support interaction, and the presence of Fe species that facilitate deoxygenation pathways and subsequent hydrocarbon formation. Overall, this study provides valuable insights into optimizing support selection and Fe metal loading for designing efficient catalysts for fatty acid-based gasoline-like hydrocarbons production under mild operating conditions.

Nano-enhanced bio-based phase change materials have emerged as promising thermal management materials for solar cooling applications because of their ability to improve heat transfer and thermal energy storage performance. This study presents a systematic review and quantitative synthesis of 50 peer-reviewed studies published between 2019 and 2025, focusing on thermodynamic mechanisms, thermophysical characterization, and cooling applications in photovoltaic and photovoltaic/thermal systems. The results demonstrate that nanoparticle incorporation significantly enhances thermal conductivity by approximately 15–400%, particularly with carbon-based nanofillers such as graphene and multi-walled carbon nanotubes, whereas latent heat retention generally decreases by 5–15% due to nanoparticle volume displacement and molecular interactions. The synthesized datasets indicate that an optimal nanoparticle loading of 2–4 weight percent provides a balanced enhancement of thermal conductivity and latent heat storage capacity. The correlation analysis reveals a minimal linear relationship between dispersion quality and latent heat retention, with a coefficient of r = 0.0935. This indicates that dispersion quality alone does not adequately account for the variations in latent heat retention observed across the reviewed studies. Instead, it suggests that factors such as nanoparticle morphology and interfacial compatibility are likely to have a more significant impact on thermodynamic behavior. The synthesis further shows that photovoltaic operating temperature can be reduced by up to 11°C under peak solar irradiance, resulting in an approximately 7.2% improvement in electrical efficiency. Exergy evaluation indicates that the latent phase contributes the highest useful exergy, approximately 42%, while demonstrating lower thermal irreversibility. Despite substantial material-level improvements, the aggregated system-level performance remains constrained by thermal resistance, methodological inconsistencies, and integration challenges in practical PV and PV/T configurations.

Fused deposition modeling (FDM) is governed by process–structure–property (P–S–P) relationships, in which nominal process settings shape the realized as-built structure, and that structure, in turn, governs the final properties and manufacturing quality. This systematic literature review examines how artificial intelligence (AI) and machine learning (ML) have been used to model, optimize, and operationalize these relationships in FDM. Using Scopus and Web of Science with a PRISMA-guided screening procedure, we reviewed English-language journal articles published between 2021 and 2025 and identified 65 studies for final synthesis. The evidence converges around four linked themes: direct process–property prediction, structure-aware data and feature representation, surrogate-assisted optimization and inverse design, and robustness-oriented deployment for quality assurance and real-time monitoring. The most important advances arise when nominal process variables are interpreted as latent drivers of the realized infill, porosity, bonding quality, anisotropy, and geometric deviation, rather than as flat predictors of performance. Tree-based ensembles, boosting methods, NNs, Gaussian process-based models, and hybrid deep learning architectures dominate predictive benchmarks, whereas explainable learning, multimodal sensing, and measured or inferred structural descriptors improve both interpretability and process insight. A recurring methodological pattern is the sequence design of experiments, ML surrogate construction, and downstream optimization, compensation, or decision support. Despite this progress, the literature remains constrained by mismatches between nominal and realized structures, limited cross-printer transferability, underdeveloped treatment of uncertainty, small and heterogeneous datasets, and inconsistent external validation. Overall, the review shows that AI contributes most in FDM when it makes the structure-forming role of process variables explicit and turns learned P–S–P mappings into more reliable manufacturing decisions.

PurposeThis paper examines the relationship between financial development, green energy security, and income inequality for emerging and developing economies.Design/methodology/approachThe authors use dynamic panel quantile regression with nonadditive fixed effects for a sample of 112 emerging and developing economies spanning from 2001 to 2021.FindingsThe findings demonstrate that financial development has a nonlinear relationship with income inequality. The interaction term between financial development and green energy security indicates that there is an inverted U-shaped relationship.Originality/valueLimited studies have investigated the combined impact of financial development and green energy security on inequality. The majority of previous research has focused on the relationship between financial development and inequality or the relationship between energy security and inequality. The combined impact of these two variables on income inequality has not been examined in previous studies.

Coal's conventional use causes environmental problems and threatens future energy security. This research presents a photothermal method for coal use, integrating a granular coal photothermal absorber into a solar thermal collector for solar water heater. This method offers a novelty in utilizing heat energy from coal, which is usually burned coal, but this photothermal method converts light into heat. Therefore, the photothermal method will not produce emissions and can be used repeatedly. The solar thermal collector combines a Fresnel lens and a parabolic reflector. Scanning electron microscope and energy dispersive spectroscopy analysis shows the granular coal photothermal absorber's light-trapping carbon structure, with optimal sizes of 10-20 mesh generating temperatures of 378.15 K and an absorbance of 1.04. Thermogravimetric analysis and differential scanning calorimetry reveal a working temperature range of 336.31-684.31 K, with an optimal light-to-heat conversion temperature of 399.25 K, achieving 86.64% energy efficiency and 21.29% exergy efficiency. Performance tests show optimal solar water heater storage temperatures of 317.88 K and 316.75 K. The water temperature in the storage corresponds to the application of warm water bathing. The solar thermal collector's maximum energy and exergy efficiencies are 33.06% and 5.51%, respectively, with average efficiencies of 22.90% and 3.44%. The solar water heater system peaks at 27.85% energy and 5.90% exergy efficiency, with average efficiencies of 20% and 3.09%. This approach highlights potential sustainable energy use and reduced environmental impacts of coal utilization.