In this paper, we report the results of Monte Carlo simulations of the adsorption of neon, argon, methane and carbon dioxide in carbon nanohorns. We model the nanohorns as an array of carbon cones and obtained adsorption isotherms and isosteric heats. The main sites of adsorption are inside the cones and in the interstices between three cones. We also calculated the selectivity of carbon dioxide/methane, finding that nanohorns are a suitable substrate for gas separation. Our simulations are compared to available experimental data.
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http://dx.doi.org/10.3390/molecules21050662 | DOI Listing |
Heliyon
January 2025
Department of Petroleum Engineering, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran.
Purpose: Biodiesel is a non-toxic, renewable, and environmentally friendly fuel used in compression ignition engines. This work aimed to develop FeO/SiO as a cheap, magnetic, and easy separable catalyst for biodiesel production from waste oil by sono-catalytic transesterification.
Methods: Fe₃O₄-SiO₂ was prepared using a modified Stober method and used as a heterogeneous catalyst in an ultrasound-assisted transesterification reaction to produce biodiesel.
Mar Syst Ocean Technol
January 2025
CMMI-Cyprus Marine & Maritime Institute, CMMI House-Vasileos Pavlou Square, P.O. Box 40930, 6023 Larnaca, Cyprus.
In response to the growing demand of reducing greenhouse gas (GHG) emissions within maritime sector, Onboard Carbon Capture and Storage (OCCS) technologies provide as key solutions for tackling carbon dioxide (CO) emissions from ships. This review paper offers a comprehensive overview of recent developments, challenges, and prospects of Carbon Capture and Storage (CCS) technologies considering specifically for onboard ship applications. Various Carbon Capture (CC) methods, ranging from post-combustion and pre-combustion capture to oxy-fuel combustion, are critically analysed concerning their operating principles, advantages, disadvantages and applicability in the maritime context.
View Article and Find Full Text PDFSmall
January 2025
State Key Laboratory of Fine Chemicals, Frontier Science Center for Smart Materials, School of Chemical Engineering, Dalian University of Technology, Liaoning, Dalian, 116024, China.
Membrane technology has been explored for separating helium from hydrogen in natural gas reservoirs, a process that remains extremely challenging due to the sub-Ångstrom size difference between H and He molecules. Reverse-selective H/He separation membranes offer multiple advantages over conventional helium-selective membranes, which, however, suffer from low H/He selectivity. To address this hurdle, a novel approach is proposed to tune the ultra-micropores of carbon molecular sieves (CMS) membranes through fluorination of the polymer precursor.
View Article and Find Full Text PDFJ Am Chem Soc
January 2025
MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, GBRCE for Functional Molecular Engineering, School of Chemistry, IGCME, Sun Yat-Sen University, Guangzhou 510275, China.
Exploring the interactions between oxygen molecules and metal sites has been a significant topic. Most previous studies concentrated on enzyme-mimicking metal sites interacting with O to form M-OO species, leaving the development of new types of O-activating metal sites and novel adsorption mechanisms largely overlooked. In this study, we reported an Fe(II)-doped metal-organic framework (MOF) [FeZnH(bibtz)] (, Hbibtz = 1,1'-5,5'-bibenzo[][1,2,3]triazole), featuring an unprecedented tetrahedral Fe(II)HN site.
View Article and Find Full Text PDFSci Rep
January 2025
College of Safety Science and Engineering, Anhui University of Science and Technology, Huainan, 232001, People's Republic of China.
The construction of a predictive model that accurately reflects the spontaneous combustion temperature of coal in goaf is fundamental to monitoring and early warning systems for thermodynamic disasters, including coal spontaneous combustion and gas explosions. In this paper, on the basis of programming temperature experiment and industrial analysis, 381 data sets of 9 coal types are established, and feature selection was executed through the utilization of the Pearson correlation coefficient, ultimately identifying O, CO, CO, CH, CH, CH/CH, CH/CH, CH/CH, CO/CO, and CO/O as input indicators for the prediction model. The chosen indicator data were divided into training and testing sets in a 4:1 ratio, the Particle Swarm Optimization (PSO) methodology was applied to optimize the parameters of the XGBoost regressor, and a universal PSO-XGBoost prediction model is proposed.
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