100 research outputs found
The role of oxygenated species in the growth of graphene, fullerenes and carbonaceous particles
No full textThis is the accepted manuscript version of the work published in its final form as Leon, Gustavo; Martin, Jacob W.; Bringley, Eric J.; Akroyd, Jethro; Kraft, Markus. Carbon; Volume: 182; Pages: 203-213; https://doi.org/10.1016/j.carbon.2021.05.052
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π-Diradical Aromatic Soot Precursors in Flames.
No full textSoot emitted from incomplete combustion of hydrocarbon fuels contributes to global warming and causes human disease. The mechanism by which soot nanoparticles form within hydrocarbon flames is still an unsolved problem in combustion science. Mechanisms proposed to date involving purely chemical growth are limited by slow reaction rates, whereas mechanisms relying on solely physical interactions between molecules are limited by weak intermolecular interactions that are unstable at flame temperatures. Here, we show evidence for a reactive π-diradical aromatic soot precursor imaged using non-contact atomic force microscopy. Localization of π-electrons on non-hexagonal rings was found to allow for Kekulé aromatic soot precursors to possess a triplet diradical ground state. Barrierless chain reactions are shown between these reactive sites, which provide thermally stable aromatic rim-linked hydrocarbons under flame conditions. Quantum molecular dynamics simulations demonstrate physical condensation of aromatics that survive for tens of picoseconds. Bound internal rotors then enable the reactive sites to find each other and become chemically cross-linked before dissociation. These species provide a rapid, thermally stable chain reaction toward soot nanoparticle formation and could provide molecular targets for limiting the emission of these toxic combustion products
Developing breakage models relating morphological data to the milling behaviour of flame synthesised titania particles
No full textA detailed population balance model is used to relate the reactor conditions of flame synthesised titanium dioxide particles to their milling behaviour. Breakage models are developed that utilise morphological data captured by a detailed particle model to relate the structure of aggregate particles to their size-reduction behaviour in the post-synthesis milling process. Simulations of a laboratory-scale hot wall reactor are consistent with experimental data and milling curves predicted by the breakage models exhibit features consistent with experimental observations. The selected breakage model considers the overall fractal structure of the aggregate particles as well as the neck size between neighbouring primary particles. Application of the model to particles produced under different reactor residence times and temperatures demonstrates that the model can be used to relate reactor conditions to the milling performance of titanium dioxide particles
A systematic method to estimate and validate enthalpies of formation using error-cancelling balanced reactions
No full textThe Combustion Institute This paper presents an automated framework that uses overlapping subsets of reference data to systematically derive an informed estimate of the standard enthalpy of formation of chemical species and assess the consistency of the reference data. The theory of error-cancelling balanced reactions (EBRs) is used to calculate estimates of the standard enthalpy of formation. Individual EBRs are identified using linear programming. The first part of the framework recursively identifies multiple EBRs for specified target species. A distribution of estimates can then be determined for each species from which an informed estimate of the enthalpy is derived. The second part of the framework iteratively isolates inconsistent reference data and improves the prediction accuracy by excluding such data. The application of the framework is demonstrated for test cases from organic and inorganic chemistry, including transition metal complexes. Its application to a set of 920 carbon, hydrogen and oxygen containing species resulted in a rapid decrease of the mean absolute error for estimates of the enthalpy of formation of each species due to the identification and exclusion of inconsistent reference data. Its application to titanium-containing species identified that the available reference values of TiOCl and TiO(OH) 2 are inconsistent and need further attention. Revised values are calculated for both species. A comparison with popular high-level quantum chemistry methods shows that the framework is able to use affordable density functional theory (DFT) calculations to deliver highly accurate estimates of the standard enthalpy of formation, comparable to high-level quantum chemistry methods for both hydrocarbons and transition metal complexes
Modelling treatment of deposits in particulate filters for internal combustion emissions
No full textInternal combustion in transport vehicles is still one of the biggest contributors to ultrafine particle emissions which have been proven to have many adverse effects on human health and the environment in general. To mitigate this problem a variety of particle filters have been developed and along with these filters a whole range of models aiming to optimise filter performance. This paper reviews a wide variety of particulate filter models for vehicular emission control and presents the volume of work in a unified and consistent notation. Particle filtration models are examined with respect to their filtration efficiency, the way they handle particle deposits within the filter wall, the formation of filter cake and the role of catalytic conversion and the effect of gaseous emission. Further, the impact of the chemical and physical properties of particulate deposits on the filter regeneration process is analysed and reaction pathways and rates are presented. In addition the accumulation of ash deposits and its impact on the filter behaviour is critically reviewed. Finally, various measures are identified that can potentially improve the current particle filter models.National Research Foundation (NRF)Published versionThis research was supported by the National Research Foundation, Singapore, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 814492. This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant 1622599. The authors would like to thank Royal Dutch Shell for their support. MK gratefully acknowledges the support of the Alexander von Humboldt Foundation, Germany
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A hybrid particle-number and particle model for efficient solution of population balance equations
No full textThis work presents a hybrid particle-number and particle model to improve efficiency in solving population balance equations for type spaces spanning spherical and aggregate particles. The particle-number model tracks simpler, spherical particles cheaply by storing only the number of particles with a given one-dimensional internal coordinate, while the particle model allows resolution of the detailed aggregate structure that occurs due to collision and coagulation between particles by storing distinct computational entries for each particle. This approach is exact if primary particles are defined by their monomer count and the particle-number model increments in single monomers. A stochastic method is used to solve the population balance equations for the combined type space. The hybrid method works well for large ensembles ( > 2 12 particles) with a detailed particle model, where performing a finite number of particle-number updates is demonstrated to be 40–50% cheaper than updating an equivalent ensemble of discrete particles. These savings can be traded for a larger sample volume to increase the resolution in the particle size distribution or more repeat runs to reduce the total error. Run time improvements are curtailed at very high surface growth and coagulation rates due to the fixed cost of growth updates on the large aggregates formed; however, the hybrid method is still attractive in this case as its primary purpose is to reduce error by preventing saturation of the ensemble with simple particles at high inception rates
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Extended first-principles thermochemistry for the oxidation of titanium tetrachloride
No full textA detailed first-principles investigation of the gas-phase precursor chemistry of titanium tetrachloride (TiCl4) in an O2 environment is used to identify the thermodynamically most stable oxidation products. Candidate species are systematically proposed based on twelve manually defined base moieties in combination with possible functional groups attached to each moiety. The ground state geometry and vibrational frequencies for each candidate species are calculated using density functional theory at the B97-1/6-311+G(d,p) level of theory. A set of 2,328 unique candidate species are found to be physically reasonable. Their thermochemical data are calculated by applying statistical thermodynamics. Standard enthalpies of formation are estimated, if unknown, by using a set of error-cancelling balanced reactions. An equilibrium composition analysis of a mixture of TiCl4/O2 (50 mol%) at 3 bar is performed to identify the thermodynamically stable products. At low temperatures, below approximately 700 K, trimer species are dominant. This is followed by a mid-temperature range of 700 to 1975 K where Ti2OCl6 is the most abundant species, before its thermodynamic stability decreases. Between 1200 and 1825 K TiCl4 is the most stable monomer. At temperatures above 1975 K TiOCl2 becomes the dominant species. This species has been measured experimentally. A structural analysis is used to suggest further potentially stable higher polymers and defines a starting point to investigate the mechanisms leading to the formation of titanium dioxide (TiO2) particles
Modelling TiO 2 formation in a stagnation flame using method of moments with interpolative closure
No full textThe stagnation flame synthesis of titanium dioxide nanoparticles from titanium tetraisopropoxide (TTIP) is modelled based on a simple one-step decomposition mechanism and one-dimensional stagnation flow. The particle model, which accounts for nucleation, surface growth, and coagulation, is fully-coupled to the flow and the gas phase chemistry and solved using the method of moments with interpolative closure (MoMIC). The model assumes no formation of aggregates considering the high temperature of the flame. In order to account for the free-jet region in the flow, the computational distance, H = 1.27 cm, is chosen based on the observed flame location in the experiment (for nozzle-stagnation distance, L = 3.4 cm). The model shows a good agreement with experimentally measured mobility particle size for stationary stagnation surface with varying TTIP loading, although the particle geometric standard deviation, GSD, is underpredicted for high TTIP loading. The particle size is predicted to be sensitive to the sampling location near the stagnation surface in the modelled flame. The sensitivity to the sampling location is found to increase with increasing precursor loading and stagnation temperature. Lastly, the effect of surface growth is evaluated by comparing the result with an alternative reaction model. It is found that surface growth plays an important role in the initial stage of particle growth which, if neglected, results in severe underprediction of particle size and overprediction of particle GSD.NRF (Natl Research Foundation, S’pore)Accepted versio
Detailed population balance modelling of TiO< synthesis in an industrial reactor
No full textThis paper uses a network of ideal flow reactors and a detailed population balance model to study the evolution of the size and shape distributions of pigmentary titanium dioxide, formed under industrial synthesis conditions. The industrial reactor has multiple reactant injections, a tubular working zone in which the exothermic reaction is completed, and a cooling zone. A network of continuously stirred tank reactors is used to model variation in composition around the feeds and plug flow reactors with prescribed temperature gradients are used to describe the working and cooling zones. The quality of the industrial product depends on its morphology, and this is influenced by factors including temperature and throughput. In this paper, a multivariate particle model is accommodated using a stochastic method and the particle morphology is characterised in terms of the distributions of primary and aggregate particle diameters, number of primary particles per particle and neck radii of connected primary particles. Increasing temperature or residence time is shown to produce larger particles. Qualitative similarities are highlighted between such findings and previous studies. The throughput studies are also in qualitative agreement with empirical industrial experience. There is scope for extending and improving the current model; however, it is suggested that insights of this type could be used to inform the design and operation of the industrial process
A big data framework to validate thermodynamic data for chemical species
No full textThe advent of large sets of chemical and thermodynamic data has enabled the rapid investigation of increasingly complex systems. The challenge, however, is how to validate such large databases. We propose an automated framework to solve this problem by identifying which data are consistent and recommending what future experiments or calculations are required. The framework is applied to validate data for the standard enthalpy of formation for 920 gas-phase species containing carbon, oxygen and hydrogen retrieved from the NIST Chemistry WebBook. The concept of error-cancelling balanced reactions is used to calculate a distribution of possible values for the standard enthalpy of formation of each species. The method automates the identification and exclusion of inconsistent data. We find that this enables the rapid convergence of the calculations towards chemical accuracy. The method can exploit knowledge of the structural similarities between species and the consistency of the data to identify which species introduce the most error and recommend what future experiments and calculations should be considered
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