722,956 research outputs found
Finding the key transition states and intermediates controlling net reaction rates and selectivity
In this paper Campbell's degree of rate control is extended to introduce the concepts of degree of kinetic rate control, degree of kinetic selectivity control, degree of thermodynamic rate control and degree of thermodynamic selectivity control. It is demonstrated by applying hypothetical but realistic kinetic models of varying complexity that the new methods offers a rigorous framework to analyze the importance of kinetic and thermodynamic parameters i.e. establishing the critical parameters of the kinetic model. The methods are general and can be applied to complex reaction networks with multiple overall reactions not only in heterogeneous catalysis but for all sorts of chemical kinetic models
Prediction of enzyme kinetic parameters based on statistical learning
Values of enzyme kinetic parameters are a key requisite for the kinetic modelling of biochemical systems. For most kinetic parameters, however, not even an order of magnitude is known, so the estimation of model parameters from experimental data remains a major task in systems biology. We propose a statistical approach to infer values for kinetic parameters across species and enzymes making use of parameter values that have been measured under various conditions and that are nowadays stored in databases. We fit the data by a statistical regression model in which the substrate, the combination enzyme-substrate and the combination organism-substrate have a linear effect on the logarithmic parameter value. As a result, we obtain predictions and error ranges for unknown enzyme parameters. We apply our method to decadic logarithmic Michaelis-Menten constants from the BRENDA database and confirm the results with leave-one-out crossvalidation, in which we mask one value at a time and predict it from the remaining data. For a set of 8 metabolites we obtain a standard prediction error of 1.01 for the deviation of the predicted values from the true values, while the standard deviation of the experimental values is 1.16. The method is applicable to other types of kinetic parameters for which many experimental data are available
Kinetic parameter estimation from TGA: Optimal design of TGA experiments
This work presents a general methodology to determine kinetic models of solid thermal decomposition with thermogravimetric analysis (TGA) instruments. The goal is to determine a simple and robust kinetic model for a given solid with the minimum of TGA experiments. From this last point of view, this work can be seen as an attempt to find the optimal design of TGA experiments for kinetic modelling. Two computation tools were developed. The first is a nonlinear parameter estimation procedure for identifying parameters in nonlinear dynamical models. The second tool computes the thermogravimetric experiment (here, the programmed temperature profile applied to the thermobalance) required in order to identify the best kinetic parameters, i.e. parameters with a higher statistical reliability. The combination of the two tools can be integrated in an iterative approach generally called sequential strategy. The application concerns the thermal degradation of cardboard in a Setaram TGA instrument and the results that are presented demonstrate the improvements in the kinetic parameter estimation process
Kinetic Study of Gluconic Acid Batch Fermentation by Aspergillus niger
Gluconic acid is one of interesting chemical products
in industries such as detergents, leather, photographic, textile, and especially in food and pharmaceutical industries. Fermentation is an advantageous process to produce gluconic acid. Mathematical modeling is important in the design and operation of fermentation process. In fact, kinetic data must be available for modeling. The kinetic parameters of gluconic acid production by Aspergillus niger in batch culture was studied in this research at initial substrate concentration of 150, 200 and 250 g/l. The kinetic models used were logistic equation for growth, Luedeking-Piret equation for gluconic acid formation, and Luedeking-Piret-like equation for glucose
consumption. The Kinetic parameters in the model were obtained by minimizing non linear least squares curve fitting
Kinetic model identification and parameters estimation from TGA experiments
The presented work is a part of an ongoing research effort on the development of a general methodology for the determination of kinetic models of solid thermal decomposition under pyrolysis conditions with thermogravimetric analysis (TGA) devices. The goal is to determine a simple and robust kinetic model for a given solid with the minimum of TGA experiments. From the latter point of view, this work can be seen as the optimal design of TGA experiments for pyrolysis kinetic modelling. In this paper, a general procedure is presented and more precise results are given about the influence of the sensitivity matrix on the estimation of the kinetic parameters and about the important influence of the specific TGA runs used for parameter estimation on the precision of the fitted parameters. The first results are shown for simulated applications; in the final part, the presented results concern cellulose pyrolysis in a Setaram TGA device
Deducing correlation parameters from optical conductivity in the Bechgaard salts
Numerical calculations of the kinetic energy of various extensions of the
one-dimensional Hubbard model including dimerization and repulsion between
nearest neighbours are reported. Using the sum rule that relates the kinetic
energy to the integral of the optical conductivity, one can determine which
parameters are consistent with the reduction of the infrared oscillator
strength that has been observed in the Bechgaard salts. This leads to improved
estimates of the correlation parameters for both the TMTSF and TMTTF series.Comment: 12 pages, latex, figures available from the author
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