Fully synthetic jet fuel (FSJF) produced via Fischer-Tropsch (FT) technology was recently approved by the international aviation fuel authorities. To receive approval, comparison of FSJF and crude-derived fuel and blends on their qualitative and quantitative hydrocarbon composition was of utmost importance. This was performed by comprehensive two-dimensional gas chromatography (GC×GC) in the reversed phase mode. The hydrocarbon composition of synthetic and crude-derived jet fuels is very similar and all compounds detected in the synthetic product are also present in crude-derived fuels. Quantitatively, the synthetic fuel consists of a higher degree of aliphatic branching with less than half the aromatic content of the crude-derived fuel. GC×GC analyses also indicated the presence of trace levels of hetero-atomic impurities in the crude-derived product that were absent in the synthetic product. While clay-treatment removed some of the impurities and improved the fuel stability, the crude-derived product still contained traces of cyclic and aromatic S-containing compounds afterwards. Lower level of aromatics and the absence of sulphur are some of the factors that contribute to the better fuel stability and environmental properties of the synthetic fuel. GC×GC was further applied for the analysis of products during Jet Fuel Thermal Oxidation Testing (JFTOT), which measures deposit formation of a fuel under simulated engine conditions. JFTOT showed the synthetic fuel to be much more stable than the crude-derived fuel.
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http://dx.doi.org/10.1016/j.chroma.2011.05.009 | DOI Listing |
J Chem Inf Model
January 2025
Institute of Chemistry, Technische Universität Berlin, Straße des 17. Juni 135, Berlin 10623, Germany.
Machine learning (ML) is a powerful tool for the automated data analysis of molecular dynamics (MD) simulations. Recent studies showed that ML models can be used to identify protein-ligand unbinding pathways and understand the underlying mechanism. To expedite the examination of MD simulations, we constructed PathInHydro, a set of supervised ML models capable of automatically assigning unbinding pathways for the dissociation of gas molecules from [NiFe] hydrogenases, using the unbinding trajectories of CO and H from [NiFe] hydrogenase as a training set.
View Article and Find Full Text PDFPhotoreceptors in the retina of a vertebrate's eye are supported by a tissue adjacent to the retina, the retinal pigment epithelium (RPE). The RPE delivers glucose to the outer retina, consumes photoreceptor outer segments discs, and regenerates 11-cis-retinal. Here we address the question of whether photoreceptors also provide metabolic support to the RPE.
View Article and Find Full Text PDFHealth Promot J Austr
January 2025
Australian Institute of Primary Care and Ageing, La Trobe University, Bundoora, Victoria, Australia.
Energy hardship is increasingly recognised as a significant determinant of health, with evidence linking it to numerous negative health and wellbeing outcomes. The association between energy hardship and public health raises questions about what roles the health sector can or should play in addressing the issue. While there are limited examples in the literature, the role of health sectors to date has been predominantly as an intermediary connecting eligible individuals to energy hardship interventions, such as financial counselling and household upgrades to improve energy efficiency.
View Article and Find Full Text PDFSmall
January 2025
Advanced Catalytic Materials (ACM), KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
Photo-thermal catalysis, leveraging both thermal and non-thermal solar contributions, emerges as a sustainable approach for fuel and chemical synthesis. In this study, an Fe-based catalyst derived from a metal-organic framework is presented for efficient photo-thermal ammonia (NH) decomposition. Optimal conditions, under light irradiation without external heating, result in a notable 55% NH conversion.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Information Systems, College of Computing and Informatics, The University of Sharjah, Sharjah, UAE.
This study explores the integration of nanotechnology and Long Short-Term Memory (LSTM) machine learning algorithms to enhance the understanding and optimization of fuel spray dynamics in compression ignition (CI) engines with varying bowl geometries. The incorporation of nanotechnology, through the addition of nanoparticles to conventional fuels, improves fuel atomization, combustion efficiency, and emission control. Simultaneously, LSTM models are employed to analyze and predict the complex spray behavior under diverse operational and geometric conditions.
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