Physical Chemistry 3: — Chemical Kinetics — - Christian-Albrechts ...
Physical Chemistry 3: — Chemical Kinetics — - Christian-Albrechts ... Physical Chemistry 3: — Chemical Kinetics — - Christian-Albrechts ...
3.4 Generalized first-order kinetics* 49 I Ansatz: The ansatz for solving Eq. 3.58 assumes that we can express A in terms of another vector B, 25 A = P · B (3.59) where P · B means the dot product, using a rotation matrix P which is defined so that it diagonalizes K via the eigenvalue equation K·P = P · Λ , which by multiplication from the left with P −1 yields P −1 · K · P = Λ (3.60) The eigenvalue matrix Λ is a diagonal matrix of the form ⎛ ⎞ 1 0 0 Λ = ⎝ 0 2 0 0 0 . ⎠ (3.61) .. As defined by Eq. 3.60, its elements on the diagonal { 1 2 } are the eigenvalues of the rate constant matrix. ThematrixP is called the eigenvector matrix associated with K. The columns of P are the respective eigenvectors associated with the respective eigenvalues . I Solution: Inserting the ansatz A = P · B (3.62) into our matrix equation 26 · A = K · A (3.63) we have or (P · B) Muliplication of this equation from the left by P −1 gives = K · P · B (3.64) P · · B = K · P · B (3.65) P −1 · P · · B = P −1 · K · P · B (3.66) or · B = Λ · B (3.67) This DE can be immediately integrated since Λ is diagonal. The solution is B = Λ B 0 (3.68) where Λ is a diagonal matrix with elements © 1 2 ª , and Λ B 0 means element-wise multiplication. The result gives the time dependence of B, i.e., B() starting from the initial values B 0 . 25 The components of B will be seen to be linear combinations of the components of the concentration vector A. B is immediately obtained once we have the matrix of eigenvectors P (see below). 26 The dot above a concentration vector indicates differentiation by .
3.4 Generalized first-order kinetics* 50 Having B(), we can transform back into the original concentration basis by using B = P −1 · A (3.69) and B 0 = P −1 · A 0 (3.70) InsertingintooursolutionforB (Eq. 3.68), we obtain P −1 · A = Λ P −1 · A 0 (3.71) The final step is now a multiplication from the left with P which yields the solution for Eq. 3.58 A = P · Λ P −1 · A 0 (3.72) I Note: This may look complicated to someone who is not used to it. However, once we have set up K, which is straightforward, everything is done by the computer: The computer computes the • eigenvalues of the rate constant matrix K, • the eigenvector matrix P oftherateconstantmatrixK, • all necessary inverse matrices, etc., • and does all the matrix multiplications. All you usually have to do is to program the K matrix. I Application to our example (solution “by hand”): • Evaluation of the eigenvalue matrix Λ by solution of the eigenvalue equation det (K − Λ · I) =0 (3.73) with I as unity matrix: y ¯ − 1 − + 2 + 1 − 2 − ¯ =0 (3.74) (− 1 − ) · (− 2 − ) − ( 1 · 2 )=0 (3.75) 1 2 + 1 + 2 + 2 − 1 2 =0 (3.76) ( +( 1 + 2 )) = 0 (3.77) y 1 =0 (3.78) 2 = − ( 1 + 2 ) (3.79) y Λ = µ 0 0 0 − ( 1 + 2 ) (3.80)
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3.4 Generalized first-order kinetics* 50<br />
Having B(), we can transform back into the original concentration basis by using<br />
B = P −1 · A (3.69)<br />
and<br />
B 0 = P −1 · A 0 (3.70)<br />
InsertingintooursolutionforB (Eq. 3.68), we obtain<br />
P −1 · A = Λ P −1 · A 0 (3.71)<br />
The final step is now a multiplication from the left with P which yields the solution for<br />
Eq. 3.58<br />
A = P · Λ P −1 · A 0 (3.72)<br />
I Note: This may look complicated to someone who is not used to it. However, once<br />
we have set up K, which is straightforward, everything is done by the computer: The<br />
computer computes the<br />
• eigenvalues of the rate constant matrix K,<br />
• the eigenvector matrix P oftherateconstantmatrixK,<br />
• all necessary inverse matrices, etc.,<br />
• and does all the matrix multiplications.<br />
All you usually have to do is to program the K matrix.<br />
I<br />
Application to our example (solution “by hand”):<br />
• Evaluation of the eigenvalue matrix Λ by solution of the eigenvalue equation<br />
det (K − Λ · I) =0 (3.73)<br />
with I as unity matrix:<br />
y<br />
¯ − 1 − + 2<br />
+ 1 − 2 − ¯ =0 (3.74)<br />
(− 1 − ) · (− 2 − ) − ( 1 · 2 )=0 (3.75)<br />
1 2 + 1 + 2 + 2 − 1 2 =0 (3.76)<br />
( +( 1 + 2 )) = 0 (3.77)<br />
y<br />
1 =0 (3.78)<br />
2 = − ( 1 + 2 ) (3.79)<br />
y<br />
Λ =<br />
µ <br />
0 0<br />
0 − ( 1 + 2 )<br />
(3.80)