Generalized composite degradation kinetics for polymeric systems under isothermal and nonisothermal conditions

A composite degradation methodology is extended to the conversion-dependence function in order to explain the importance of multiple reaction mechanisms which might be considered to be involved in degradation processes. Based on two elementary reaction mechanisms, a specific form of the model equation is derived, which is capable of describing various types of degradation behavior showing sigmoidal rate as well as deceleratory rate. The conversion-dependence function is derived to be independent of the Arrhenius-type reaction constant or temperature, and thus the kinetic parameters are determined by analytic methods that have been developed for isothermal and dynamic-heating experiments without any modification or additional assumptions. The developed model equation is tested by predicting the isothermal master curve of polyether-ether-ketone (PEEK), which is used as a model system in this study. The activation energies of the model system are analyzed using comparable methods for isothermal and dynamic experiments, which compare favorably in terms of the activation energy as a function of conversion. The resulting model equation, based on the kinetic parameters determined by isothermal experiments, can accurately predict both isothermal and dynamic-heating thermogravimetry utilizing the same constants and identical reaction mechanisms without additional assumption.     

How noise statistics impact models of enzyme cycles

Theoretical tools for adequately treating stochastic effects are important for understanding their role in biological processes. Although master equations provide rigorous means for investigating effects associated with fluctuations of discrete molecular copy numbers, they can be very challenging to treat analytically and numerically. Approaches based on the Langevin approximation are often more tractable, but care must be used to ensure that it is justified in each situation. Here, we examine a model of an enzyme cycle for which noise qualitatively alters the behavior of the system: fluctuations in the population of an enzyme can result in amplification and multistability in the distribution of its product. We compare master equation and Langevin treatments of this system and show that results derived previously with a white noise Langevin equation [M. Samoilov et al., Proc. Natl. Acad. Sci. U.S.A. 102, 2310 (2005)] are inconsistent with the master equation. A colored noise Langevin equation captures some, but not all, of the essential physics of the system. The advantages and disadvantages of the Langevin approximation for modeling biological processes are discussed.     

Reduction and solution of the chemical master equation using time scale separation and finite state projection

The dynamics of chemical reaction networks often takes place on widely differing time scales--from the order of nanoseconds to the order of several days. This is particularly true for gene regulatory networks, which are modeled by chemical kinetics. Multiple time scales in mathematical models often lead to serious computational difficulties, such as numerical stiffness in the case of differential equations or excessively redundant Monte Carlo simulations in the case of stochastic processes. We present a model reduction method for study of stochastic chemical kinetic systems that takes advantage of multiple time scales. The method applies to finite projections of the chemical master equation and allows for effective time scale separation of the system dynamics. We implement this method in a novel numerical algorithm that exploits the time scale separation to achieve model order reductions while enabling error checking and control. We illustrate the efficiency of our method in several examples motivated by recent developments in gene regulatory networks.     

Exact solutions for kinetic models of macromolecular dynamics

Dynamic biological processes such as enzyme catalysis, molecular motor translocation, and protein and nucleic acid conformational dynamics are inherently stochastic processes. However, when such processes are studied on a nonsynchronized ensemble, the inherent fluctuations are lost, and only the average rate of the process can be measured. With the recent development of methods of single-molecule manipulation and detection, it is now possible to follow the progress of an individual molecule, measuring not just the average rate but the fluctuations in this rate as well. These fluctuations can provide a great deal of detail about the underlying kinetic cycle that governs the dynamical behavior of the system. However, extracting this information from experiments requires the ability to calculate the general properties of arbitrarily complex theoretical kinetic schemes. We present here a general technique that determines the exact analytical solution for the mean velocity and for measures of the fluctuations. We adopt a formalism based on the master equation and show how the probability density for the position of a molecular motor at a given time can be solved exactly in Fourier-Laplace space. With this analytic solution, we can then calculate the mean velocity and fluctuation-related parameters, such as the randomness parameter (a dimensionless ratio of the diffusion constant and the velocity) and the dwell time distributions, which fully characterize the fluctuations of the system, both commonly used kinetic parameters in single-molecule measurements. Furthermore, we show that this formalism allows calculation of these parameters for a much wider class of general kinetic models than demonstrated with previous methods.     

EPDM accelerated sulfur vulcanization: a kinetic model based on a genetic algorithm

A simple closed form equation for the prediction of crosslinking of EPDM during accelerated sulfur vulcanization is presented. Such a closed form solution is derived from a second order non homogeneous differential equation, deduced from a kinetic model. The kinetic model is based on the assumption that, during vulcanization, a number of partial reactions occurs, both in series and in parallel, which determine the formation of intermediate compounds, including activated and matured polymer. Once written standard first order differential equations for each partial reaction, the differential equation system so obtained is rearranged and, after few considerations, a single second order non homogeneous differential equation with constant coefficients is derived, for which a solution may be found in closed form, provided that the non-homogeneous term is approximated with an exponential function. To estimate numerically the degree of crosslinking, kinetic model constants are evaluated through a simple data fitting, performed on experimental rheometer cure curves. The fitting procedure is a new one, and is achieved using an ad-hoc genetic algorithm, provided that a few points, strictly necessary to estimate model unknown constants with sufficient accuracy, are selected from the whole experimental cure curve. To assess the results obtained with the model proposed, a number of different compounds are analyzed, for which experimental or numerical data are available from the literature. The important cases of moderate and strong reversions are also considered, experiencing a convincing convergence of the analytical model proposed. For the single cases analyzed, partial reaction kinetic constants are also provided.     

Evidence for the time-temperature superposition principle from Monte-Carlo simulations of the glass transition in two-dimensional polymer melts

The bond fluctuation model on a square lattice with a bond-length dependent potential exhibits in simulations of slow cooling a kinetic glass transition where the system falls out of equilibrium. Extending previous work, the relaxation functions of gyration radius and end-to-end distance, and the bond autocorrelation function of the polymers are presented and related to the time-dependent displacements of inner monomeric units and center of gravity of the whole chains, respectively. Over a wide temperature range the data can be collapsed on master curves satisfying the time-temperature superposition principle for Rouse dynamics.     

Effective closed form starting point determination for kinetic model interpreting NR vulcanized with sulphur

In this paper, a closed form analytical approach for a recently presented kinetic model proposed in Milani and Milani (Polym Test, 2013, under review) to interpret NR sulphur vulcanization in presence of either experimental or surrogate rheometer curves is proposed. The model has kinetic base and is aimed at predicting, by means of a very refined approach, the vulcanization degree of NR vulcanized with sulphur. It needs as input only rheometer curves to fit and provides as output kinetic constants of the single reactions occurring during the crosslink process. In Milani and Milani (Polym Test, 2013, under review) a cure chemical scheme constituted by five reactions occurring in series and parallel was adopted. The chemical scheme, translated mathematically into a differential equations system, was suitably re-arranged and a single analytical equation was derived, representing rubber crosslink degree evolution upon time. The main drawback of such procedure is that the five kinetic constants corresponding to each reaction were determined through a standard non-linear least squares procedure, trying to minimize the deviation of the analytical cure curve from experimental data. Such a limitation is here superseded and a major improvement is proposed utilizing (1) a closed form solution which does not require any optimization algorithm and (2) finding analytically a starting point for the unknown kinetic constants, very near to the actual solution and thus very convenient for a successive least squares minimization. In the model, it is shown how the analytical condition deduced from the scorch point (second derivative of the rheometer curve equal to zero) and two further conditions, e.g. the time at 90 % of vulcanization and the reversion percentage, allow the simple direct evaluation of kinetic constants, providing a closed form analytical formula to predict well the state of cure of the rubber under consideration. To assess the results obtained with the model proposed, several examples on two different NRs are discussed. The approach proved to be extremely robust and much faster when compared with the model proposed by Milani and Milani (Polym Test, 2013, under review).     

Numerical solution methods for large,difficult kinetic master equations

The kinetics of gas-phase reactions, including pressure-dependent weak collision and non-equilibrium effects, can be modelled using a master equation. In this paper, we address the practical computational problem of finding solutions to such kinetic master equations. The mathematical structure of the master equation can be utilised to develop a number of specialised numerical techniques that are capable of solving the master equation in the presence of difficult numerics and for large problems. The former is important for modelling low temperature and pressure systems, and the latter is important for modelling the large networks of isomerising species common in combustion chemistry applications. We focus on numerical methods that exhibit particular practical use because of their robust nature or scalability to many isomers, or both. Recent developments in linear-scaling methods are highlighted.     

Time–temperature superposition method for predicting the permanence of paper by extrapolating accelerated ageing data to ambient conditions

In this paper we present a time-temperature superposition method for predicting the permanence of paper by extrapolating accelerated paper ageing data to ambient conditions. The presented method includes a test for the presence of shift factors to superpose all of the raw accelerated ageing data over the temperature range studied to obtain a master curve, a numerical fit of the master curve for producing a master equation representing the kinetics of paper degradation, a critical examination of applying Arrhenius equation for explaining the relationship between the empirically determined shift factors and the accelerated ageing temperature, and a verification of the Arrhenius activation energy extrapolation assumption. Unlike the usual approach that extrapolates the Arrhenius relationship between lifetime and temperature, without corroborating evidence, to ambient temperatures, we test the Arrhenius activation energy extrapolation assumption by determining the influence of acidity on cellulose hydrolysis reactions, and have found that detection and identification of the acid-sensitive linkages in cellulose substances is an ultra-sensitive and reliable method to measure degradation of cellulose and paper in what is normally the extrapolation region (ambient temperatures). Taking the examples of natural ageing data in literature from 18 bleached kraft dry-lap pulps for 22 years under ambient conditions and three handsheet samples for 22 years under controlled conditions, comparison of the predictions with natural ageing results has been addressed.     

Stochastic simulation of catalytic surface reactions in the fast diffusion limit

The master equation of a lattice gas reaction tracks the probability of visiting all spatial configurations. The large number of unique spatial configurations on a lattice renders master equation simulations infeasible for even small lattices. In this work, a reduced master equation is derived for the probability distribution of the coverages in the infinite diffusion limit. This derivation justifies the widely used assumption that the adlayer is in equilibrium for the current coverages and temperature when all reactants are highly mobile. Given the reduced master equation, two novel and efficient simulation methods of lattice gas reactions in the infinite diffusion limit are derived. The first method involves solving the reduced master equation directly for small lattices, which is intractable in configuration space. The second method involves reducing the master equation further in the large lattice limit to a set of differential equations that tracks only the species coverages. Solution of the reduced master equation and differential equations requires information that can be obtained through short, diffusion-only kinetic Monte Carlo simulation runs at each coverage. These simulations need to be run only once because the data can be stored and used for simulations with any set of kinetic parameters, gas-phase concentrations, and initial conditions. An idealized CO oxidation reaction mechanism with strong lateral interactions is used as an example system for demonstrating the reduced master equation and deterministic simulation techniques.     

Effects of the strain rate and temperature in stress–strain tests: study of the glass transition of a polyamide-6

In this work new insights are presented on the measurement of the tangent and secant moduli from stress–strain curves in polymeric systems. Expressions for the strain-rate and strain dependence of both moduli are derived for systems characterised by a distribution of relaxation times. The equivalent frequency of the stress–strain experiments is shown to be dependent on the strain rate and on the strain at which the measurements are carried out. Such considerations enable using quasi-static tensile stress–strain tests to study relaxational processes in polymeric materials. The tensile behaviour of a 30% glass fibre reinforced polyamide 6 was characterised at different strain rates and temperatures, covering the glass transition region. A master curve of the tangent modulus as a function of strain rate was successfully constructed by simple horizontal shifting of the isothermal data. The temperature dependence of the shift factors was well described by the WLF equation. It was also possible to fit the master curve considering a polymeric system with a distribution of relaxation times, relevant parameters such as the KWW β parameter being extracted. The results were found to be consistent with dynamic mechanical analysis results.     

An equation-free probabilistic steady-state approximation: dynamic application to the stochastic simulation of biochemical reaction networks

Stochastic chemical kinetics more accurately describes the dynamics of "small" chemical systems, such as biological cells. Many real systems contain dynamical stiffness, which causes the exact stochastic simulation algorithm or other kinetic Monte Carlo methods to spend the majority of their time executing frequently occurring reaction events. Previous methods have successfully applied a type of probabilistic steady-state approximation by deriving an evolution equation, such as the chemical master equation, for the relaxed fast dynamics and using the solution of that equation to determine the slow dynamics. However, because the solution of the chemical master equation is limited to small, carefully selected, or linear reaction networks, an alternate equation-free method would be highly useful. We present a probabilistic steady-state approximation that separates the time scales of an arbitrary reaction network, detects the convergence of a marginal distribution to a quasi-steady-state, directly samples the underlying distribution, and uses those samples to accurately predict the state of the system, including the effects of the slow dynamics, at future times. The numerical method produces an accurate solution of both the fast and slow reaction dynamics while, for stiff systems, reducing the computational time by orders of magnitude. The developed theory makes no approximations on the shape or form of the underlying steady-state distribution and only assumes that it is ergodic. We demonstrate the accuracy and efficiency of the method using multiple interesting examples, including a highly nonlinear protein-protein interaction network. The developed theory may be applied to any type of kinetic Monte Carlo simulation to more efficiently simulate dynamically stiff systems, including existing exact, approximate, or hybrid stochastic simulation techniques.     

A derivation of the master equation from path entropy maximization

The master equation and, more generally, Markov processes are routinely used as models for stochastic processes. They are often justified on the basis of randomization and coarse-graining assumptions. Here instead, we derive nth-order Markov processes and the master equation as unique solutions to an inverse problem. We find that when constraints are not enough to uniquely determine the stochastic model, an nth-order Markov process emerges as the unique maximum entropy solution to this otherwise underdetermined problem. This gives a rigorous alternative for justifying such models while providing a systematic recipe for generalizing widely accepted stochastic models usually assumed to follow from the first principles.     

A systematic study of the microwave and thermal cure kinetics of the DGEBA/DDS and DGEBA/DDM epoxy‐amine resin systems

The microwave and thermal cure processes for the epoxy-amine systems (epoxy resin diglycidyl ether of bisphenol A, DGEBA) with 4,4′-diaminodiphenyl sulphone (DDS) and 4,4′-diaminodiphenyl methane (DDM) have been investigated for 1 : 1 stoichiometries by using fiber-optic FT-NIR spectroscopy. The DGEBA used was in the form of Ciba-Geigy GY260 resin. The DDM system was studied at a single cure temperature of 373 K and a single stoichiometry of 20.94 wt% and the DDS system was studied at a stoichiometry of 24.9 wt% and a range of temperatures between 393 and 443 K. The best values of the kinetic rate parameters for the consumption of amines have been determined by a least squares curve fit to a model for epoxy/amine cure. The activation energies for the polymerization of the DGEBA/DDS system were determined for both cure processes and found to be 66 and 69 kJ mol⁻¹ for the microwave and thermal cure processes, respectively. No evidence was found for any specific effect of the microwave radiation on the rate parameters, and the systems were both found to be characterized by a negative substitution effect. Copyright © 2002 John Wiley & Sons, Ltd.     

OH-initiated oxidation of toluene. 2. Master equation simulation of toluene oxide isomerization

In this work we continue our investigation of the toluene-OH-O2 system. We describe master equation modeling of the isomerization of toluene oxide, focusing on the formation of the cresols. A 15 isomer model is used. Simulations of both thermally activated processes and photolysis processes are described. In accord with experiment, it is found that photolysis with a high-energy light source should be expected to give a high yield of the thermal distribution of cresol products (dominated by the para isomer). Photolysis with a low-energy light source, on the other hand, favors formation of the thermally disfavored ortho isomer. Though the 15 isomer system is potentially an excellent test bed for the development of scalable master equation solution methods, existing scalable solution methods were found to fail on this system.     

Simulating the proton transfer in gramicidin A by a sequential dynamical Monte Carlo method

The large interest in long-range proton transfer in biomolecules is triggered by its importance for many biochemical processes such as biological energy transduction and drug detoxification. Since long-range proton transfer occurs on a microsecond time scale, simulating this process on a molecular level is still a challenging task and not possible with standard simulation methods. In general, the dynamics of a reactive system can be described by a master equation. A natural way to describe long-range charge transfer in biomolecules is to decompose the process into elementary steps which are transitions between microstates. Each microstate has a defined protonation pattern. Although such a master equation can in principle be solved analytically, it is often too demanding to solve this equation because of the large number of microstates. In this paper, we describe a new method which solves the master equation by a sequential dynamical Monte Carlo algorithm. Starting from one microstate, the evolution of the system is simulated as a stochastic process. The energetic parameters required for these simulations are determined by continuum electrostatic calculations. We apply this method to simulate the proton transfer through gramicidin A, a transmembrane proton channel, in dependence on the applied membrane potential and the pH value of the solution. As elementary steps in our reaction, we consider proton uptake and release, proton transfer along a hydrogen bond, and rotations of water molecules that constitute a proton wire through the channel. A simulation of 8 mus length took about 5 min on an Intel Pentium 4 CPU with 3.2 GHz. We obtained good agreement with experimental data for the proton flux through gramicidin A over a wide range of pH values and membrane potentials. We find that proton desolvation as well as water rotations are equally important for the proton transfer through gramicidin A at physiological membrane potentials. Our method allows to simulate long-range charge transfer in biological systems at time scales, which are not accessible by other methods.     

Stochastic self-assembly of incommensurate clusters

Nucleation and molecular aggregation are important processes in numerous physical and biological systems. In many applications, these processes often take place in confined spaces, involving a finite number of particles. Analogous to treatments of stochastic chemical reactions, we examine the classic problem of homogeneous nucleation and self-assembly by deriving and analyzing a fully discrete stochastic master equation. We enumerate the highest probability steady states, and derive exact analytical formulae for quenched and equilibrium mean cluster size distributions. Upon comparison with results obtained from the associated mass-action Becker-D?ring equations, we find striking differences between the two corresponding equilibrium mean cluster concentrations. These differences depend primarily on the divisibility of the total available mass by the maximum allowed cluster size, and the remainder. When such mass "incommensurability" arises, a single remainder particle can "emulsify" the system by significantly broadening the equilibrium mean cluster size distribution. This discreteness-induced broadening effect is periodic in the total mass of the system but arises even when the system size is asymptotically large, provided the ratio of the total mass to the maximum cluster size is finite. Ironically, classic mass-action equations are fairly accurate in the coarsening regime, before equilibrium is reached, despite the presence of large stochastic fluctuations found via kinetic Monte-Carlo simulations. Our findings define a new scaling regime in which results from classic mass-action theories are qualitatively inaccurate, even in the limit of large total system size.     

Coarse master equation from Bayesian analysis of replica molecular dynamics simulations

We use Bayesian inference to derive the rate coefficients of a coarse master equation from molecular dynamics simulations. Results from multiple short simulation trajectories are used to estimate propagators. A likelihood function constructed as a product of the propagators provides a posterior distribution of the free coefficients in the rate matrix determining the Markovian master equation. Extensions to non-Markovian dynamics are discussed, using the trajectory "paths" as observations. The Markovian approach is illustrated for the filling and emptying transitions of short carbon nanotubes dissolved in water. We show that accurate thermodynamic and kinetic properties, such as free energy surfaces and kinetic rate coefficients, can be computed from coarse master equations obtained through Bayesian inference.