RICE-RAMSPERGER-KASSEL-MARCUS THEORETICAL PREDICTION OF HIGH-PRESSURE ARRHENIUS PARAMETERS BY NONLINEAR-REGRESSION - APPLICATION TO SILANE AND DISILANE DECOMPOSITION
RICE-RAMSPERGER-KASSEL-MARCUS THEORETICAL PREDICTION OF HIGH-PRESSURE ARRHENIUS PARAMETERS BY NONLINEAR-REGRESSION - APPLICATION TO SILANE AND DISILANE DECOMPOSITION
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DOI：
10.1021/j100306a043
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发表时间：
1987-10-22
影响因子：
--
通讯作者：
CARR, RW
中科院分区：
文献类型：
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作者：
ROENIGK, KF;JENSEN, KF;CARR, RW
结论证明了一种分析单分子动力学数据的通用计算方法。该方法利用RRKM理论和有效的回归技术从沉降数据中确定极限高压Arrhenius参数。在这个过程中，许多涉及到预测阿伦尼乌斯参数的随意性被消除了。考虑到它的优点和高速计算机的日益可用性，该方法应该在未来的单分子动力学RRKM分析中得到优先考虑。甲基异氰化物异构化的Arrhenius参数已被重新评估，并与先前发表的值一致。由此得出的RRKM预测结果与实验数据吻合良好。在此应用中，碰撞能量传递的指数分布假设似乎可以令人满意地预测实验数据。碰撞中传递的平均能量对温度的依赖性似乎很弱，如果存在的话，尽管实验数据的不准确性使其无法区分其值。通过本文提出的最优实验设计技术，证明了更准确的参数估计应该来自定义脱落膝盖区域曲率的数据的回归，而不是来自高压或低压极限的数据。这项工作得到了NSF DMR 83 07924的部分支持。
Conclusions A general computational approach to analysis of unimolecular kinetic data has been demonstrated. The approach utilizes RRKM theory and an efficient regression technique in determining limiting high-pressure Arrhenius parameters from data in the falloff. Much of the arbitrariness involved in prediction of Arrhenius parameters is eliminated in this procedure. Considering its advantages and the increasing availability of high-speed computers, the method should find preference infuture RRKM analysis of unimolecular kinetics. Arrhenius parameters for methyl isocyanide isomerization have been reevaluated and agree well with previously published values. Resulting RRKM predictions are shown to agree wellwith experimental data. The assumption of exponential distribution of collisionalenergy transfer appears to allow satisfactory pre-diction of experimental data in this application. The temperature dependence of average energytransferred in collisions appears to be weak, if present at all, although inaccuracies in the ex-perimental data preclude distinguishing its value. With the optimal experimental design technique presentedhere, it is demonstrated that more accurate parameter estimates should come from re-gression of data defining the curvature of the knee region of the falloff rather than data at the high-or low-pressure limits.Acknowledgment. This work was partially supported by NSF DMR 83 07924.