A computational study of methanol-to-hydrocarbon conversion — Towards the design of a low-barrier process

2010 ◽  
Vol 88 (8) ◽  
pp. 866-876 ◽  
Author(s):  
Bun Chan ◽  
Leo Radom

Computational quantum chemistry has been employed to examine the production of ethylene with methanol-to-hydrocarbon (MTH) processes via a carbon pool mechanism. We find that the M05-2X functional performs well for the types of reactions that are involved. The methylation reactions of the aromatic cocatalyst are the most energy-demanding steps in the process. For the subsequent production of C2H4, we have identified a low-energy pathway that involves multiple methyl shifts, followed by concerted deprotonation and C2H4 elimination. The substitutions of the Al and Si atoms in the participating Si–OH–Al moiety of zeolite catalysts with Ga and Ge do not lead to lower barriers for the methylation reactions, nor does the use of a more electron-rich aromatic cocatalyst. However, we find that the use of two cocatalysts, a nucleophile and an aromatic carbon pool, can provide an overall low-energy pathway for the MTH process.

Author(s):  
Brian Gentry ◽  
Tae Hoon Choi ◽  
William S. Belfield ◽  
John A. Keith

Rational design of molecular chelating agents requires a detailed understanding of physicochemical ligand-metal interactions in solvent phase. Computational quantum chemistry methods should be able to provide this, but computational reports...


2019 ◽  
Vol 17 (1) ◽  
pp. 653-667
Author(s):  
Zhongming Teng ◽  
Hong-Xiu Zhong

Abstract In the linear response eigenvalue problem arising from computational quantum chemistry and physics, one needs to compute a few of smallest positive eigenvalues together with the corresponding eigenvectors. For such a task, most of efficient algorithms are based on an important notion that is the so-called pair of deflating subspaces. If a pair of deflating subspaces is at hand, the computed approximated eigenvalues are partial eigenvalues of the linear response eigenvalue problem. In the case the pair of deflating subspaces is not available, only approximate one, in a recent paper [SIAM J. Matrix Anal. Appl., 35(2), pp.765-782, 2014], Zhang, Xue and Li obtained the relationships between the accuracy in eigenvalue approximations and the distances from the exact deflating subspaces to their approximate ones. In this paper, we establish majorization type results for these relationships. From our majorization results, various bounds are readily available to estimate how accurate the approximate eigenvalues based on information on the approximate accuracy of a pair of approximate deflating subspaces. These results will provide theoretical foundations for assessing the relative performance of certain iterative methods in the linear response eigenvalue problem.


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