School of Engineering and Materials Sciencehttps://qmro.qmul.ac.uk/xmlui/handle/123456789/34772023-11-29T02:47:16Z2023-11-29T02:47:16ZSelf-Diffusiophoresis and Symmetry-Breaking of a Janus Dimer: Analytic SolutionAvital, EJMiloh, Thttps://qmro.qmul.ac.uk/xmlui/handle/123456789/923542023-11-28T15:52:58Z2023-01-01T00:00:00ZSelf-Diffusiophoresis and Symmetry-Breaking of a Janus Dimer: Analytic Solution
Avital, EJ; Miloh, T
A self-diffusiophoretic problem is considered for a chemically active dimer consisting of two equal touching spherical colloids that are exposed to different fixed-flux and fixed-rate surface reactions. A new analytic solution for the autophoretic mobility of such a catalytic Janus dimer is presented in the limit of a small Péclet number and linearization of the resulting Robin-type boundary value problem for the harmonic solute concentration. Explicit solutions in terms of the physical parameters are first obtained for the uncoupled electrostatic and hydrodynamic problems. The dimer mobility is then found by employing the reciprocal theorem depending on the surface slip velocity and on the normal component of the shear stress acting on the inert dimer. Special attention is given to the limiting case of a Janus dimer composed of an inert sphere and a chemically active sphere where the fixed-rate reaction (Damköhler number) is infinitely large. Examples are given, comparing the numerical and approximate analytic solutions of the newly developed theory. Singular points arising in the model are discussed for a dimer with a fixed-rate reaction, and the flow field around the dimer is also analysed. The new developed theory introduces a fast way to compute the mobility of a freely suspended dimer and the induced flow field around it, and thus can also serve as a sub grid scale model for a multi-scale flow simulation.
2023-01-01T00:00:00ZSynthesis of a Graphene-Encapsulated Fe 3 C/Fe Catalyst Supported on Sporopollenin Exine Capsules and Its Use for the Reverse Water–Gas Shift ReactionMalik, WVictoria Tafoya, JPDoszczeczko, SJorge Sobrido, ABSkoulou, VKBoa, ANZhang, QRamirez Reina, TVolpe, Rhttps://qmro.qmul.ac.uk/xmlui/handle/123456789/923162023-11-28T13:52:46Z2023-01-01T00:00:00ZSynthesis of a Graphene-Encapsulated Fe 3 C/Fe Catalyst Supported on Sporopollenin Exine Capsules and Its Use for the Reverse Water–Gas Shift Reaction
Malik, W; Victoria Tafoya, JP; Doszczeczko, S; Jorge Sobrido, AB; Skoulou, VK; Boa, AN; Zhang, Q; Ramirez Reina, T; Volpe, R
Bioderived materials have emerged as sustainable catalyst supports for several heterogeneous reactions owing to their naturally occurring hierarchal pore size distribution, high surface area, and thermal and chemical stability. We utilize sporopollenin exine capsules (SpECs), a carbon-rich byproduct of pollen grains, composed primarily of polymerized and cross-linked lipids, to synthesize carbon-encapsulated iron nanoparticles via evaporative precipitation and pyrolytic treatments. The composition and morphology of the macroparticles were influenced by the precursor iron acetate concentration. Most significantly, the formation of crystalline phases (Fe3C, α-Fe, and graphite) detected via X-ray diffraction spectroscopy showed a critical dependence on iron loading. Significantly, the characteristic morphology and structure of the SpECs were largely preserved after high-temperature pyrolysis. Analysis of Brunauer–Emmett–Teller surface area, the D and G bands from Raman spectroscopy, and the relative ratio of the C═C to C–C bonding from high-resolution X-ray photoelectron spectroscopy suggests that porosity, surface area, and degree of graphitization were easily tuned by varying the Fe loading. A mechanism for the formation of crystalline phases and meso-porosity during the pyrolysis process is also proposed. SpEC-Fe10% proved to be highly active and selective for the reverse water–gas shift reaction at high temperatures (>600 °C).
2023-01-01T00:00:00ZExtracting Multi-objective Multigraph Features for the Shortest Path Cost Prediction: Statistics-based or Learning-based?Liu, SWang, XWeiszer, MChen, Jhttps://qmro.qmul.ac.uk/xmlui/handle/123456789/923072023-11-28T12:06:10Z2023-10-01T00:00:00ZExtracting Multi-objective Multigraph Features for the Shortest Path Cost Prediction: Statistics-based or Learning-based?
Liu, S; Wang, X; Weiszer, M; Chen, J
Efficient airport airside ground movement (AAGM) is key to successful operations of urban air mobility. Recent studies have introduced the use of multi-objective multigraphs (MOMGs) as the conceptual prototype to formulate AAGM. Swift calculation of the shortest path costs is crucial for the algorithmic heuristic search on MOMGs, however, previous work chiefly focused on single-objective simple graphs (SOSGs), treated cost enquires as search problems, and failed to keep a low level of computational time and storage complexity. This paper concentrates on the conceptual prototype MOMG, and investigates its node feature extraction, which lays the foundation for efficient prediction of shortest path costs. Two extraction methods are implemented and compared: a statistics-based method that summarises 22 node physical patterns from graph theory principles, and a learning-based method that employs node embedding technique to encode graph structures into a discriminative vector space. The former method can effectively evaluate the node physical patterns and reveals their individual importance for distance prediction, while the latter provides novel practices on processing multigraphs for node embedding algorithms that can merely handle SOSGs. Three regression models are applied to predict the shortest path costs to demonstrate the performance of each. Our experiments on randomly generated benchmark MOMGs show that (i) the statistics-based method underperforms on characterising small distance values due to severe overestimation, (ii) a subset of essential physical patterns can achieve comparable or slightly better prediction accuracy than that based on a complete set of patterns, and (iii) the learning-based method consistently outperforms the statistics-based method, while maintaining a competitive level of computational complexity.
2023-10-01T00:00:00ZA multi-objective memetic algorithm with adaptive local search for airspace complexity mitigationLi, BGuo, TMei, YLi, YChen, JZhang, YTang, KDu, Whttps://qmro.qmul.ac.uk/xmlui/handle/123456789/923062023-11-28T12:02:34Z2023-12-01T00:00:00ZA multi-objective memetic algorithm with adaptive local search for airspace complexity mitigation
Li, B; Guo, T; Mei, Y; Li, Y; Chen, J; Zhang, Y; Tang, K; Du, W
Airspace complexity is a paramount safety metric to measure the difficulty and effort required to safely manage air traffic. The continuing growth in air traffic demand results in increasing airspace complexity and unprecedented safety concerns. Most existing methods treat the minimization of airspace complexity as the sole objective, ignoring the path deviation cost induced by the re-scheduled aircraft. In this paper, regarding reduction of airspace complexity and path deviation cost as two conflicting objectives, a multi-objective airspace complexity mitigation model is proposed to simultaneously ensure the safety and efficiency of air transport by optimizing flight trajectories. To effectively solve this multi-objective and non-linear optimization problem, a novel Memetic Algorithm with Adaptive Local Search (called MA-ALS) is developed. Specifically, we design a new crossover and three new local search operators under the flight trajectory representation. MA-ALS conducts exploration by crossover, and exploitation by a hill-climbing local search process. Moreover, we proposed an adaptive local search selection mechanism which facilitates the dynamic collaboration of different local search operators during evolution. A comprehensive comparison with the most recently developed algorithms on Chinese air traffic dataset is conducted. The Pareto front generated by the proposed algorithm dominates that of the compared baselines. Moreover, compared with a real flight schedule, the flight plan obtained by the proposed algorithm can significantly reduce the airspace complexity.
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