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A design strategy applied to sulfur resistant lean NOx̳ automotive catalysts

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dc.contributor.advisor Bernhardt L. Trout. en_US Tang, Hairong en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Chemical Engineering. en_US 2006-07-31T15:26:55Z 2006-07-31T15:26:55Z 2005 en_US 2005 en_US
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2005. en_US
dc.description In title on t.p. double-underscored "x" appears as subscript. en_US
dc.description Includes bibliographical references (p. 123-135). en_US
dc.description.abstract Catalyst poisoning due to sulfur compounds derived from fuel sulfur presents a major challenge, intractable thus far, to development of many advanced technologies for automotive catalysts such as the lean NOx, trap. Under lean conditions, sulfur will be oxidized to S0₃ and then form sulfate on the trap. The sulfate on the trap is thermodynamically very stable and, thus, difficult to purge. The NOx trap will then be deactivated over time. Our objective has been to build up a framework for the design of selective, sulfur resistant, oxidation automotive catalysts, which are active for the oxidation of NO to NO₂ but relatively inactive for the oxidation of SO₂ to S0₃. It is well known that the catalytic properties of alloys are often superior to those of pure metals, because of either the electronic effect or the ensemble effect or both. The ensemble effect is due to a change in distribution and availability of surface reaction sites, while the electronic effect is due to a change in electronic structure, leading to a change in rate constants of elementary steps. However, a very large number of possible compositions of alloys exist for any particular application. en_US
dc.description.abstract (cont.) Therefore, a fundamental understanding of the relationship between the electronic structure, the composition, and the activity of alloys, which could aid in catalyst design, is first developed. This is accomplished by constructing a generalized weighted d-band center model for the prediction of the binding strength of chemisorbed molecules, in which the various atoms in the molecules bind unequally to multiple types of surface atoms. This model is then applied to estimate the adsorption energies of SO₂ and NO at both initial states and transition states on various surfaces. Both energetic data and electronic structure data are obtained from first principles density functional theory calculations. Our model is found to predict well the relative stability of adsorbates on surfaces and can be used to predict the effects of different compositions on the energy of adsorption. A strong linear correlation is found between our new weighting of the positions of the d band of the surface and the molecular adsorption energy. These linear relationships are then used together with energy decomposition scheme for a coadsorbed system on surfaces, to predict the reactivity of SO₂ and NO oxidation on different surfaces. en_US
dc.description.abstract (cont.) A catalyst which is selective for the oxidation of NO over SO₂ is then developed. This study should aid in the development of more effective catalysts for an extremely important environmental application. en_US
dc.description.statementofresponsibility by Hairong Tang. en_US
dc.format.extent 135 p. en_US
dc.format.extent 6891556 bytes
dc.format.extent 6897188 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.subject Chemical Engineering. en_US
dc.title A design strategy applied to sulfur resistant lean NOx̳ automotive catalysts en_US
dc.type Thesis en_US Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Chemical Engineering. en_US
dc.identifier.oclc 64707793 en_US

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