Different Sets Of Kinetic Parameters Research Articles

Correlation between figure of merit function and deviation in the assessment of kinetic parameters in realistic TLD-100 behavior using fully-connected neural networks

In this study, a realistic model was employed to research the LiF:Mg:Ti behavior (commercially known as TLD-100), a widely employed as thermoluminescent (TL) material in dosimetry services. The research was focused on exploring the association between the Figure of Merit (FOM) function and deviations from the true kinetic parameter values, that were obtained through fitting experimental measures. FOM function is the most used magnitude to measure the quality of a fit achieved between a mathematical model and experimental data. Indeed, FOM function is particularly relevant when the TL glow curve deconvolution method is implemented to obtain the kinetic parameters: Activation Energy E (eV), Frequency Factor s (s−1), value of kinetic order b, Temperature at which the intensity maximum TMax(oC), Intensity Maximum IMax (a.u.) and Distribution Width σ (eV) of every peak. FOM function is subject to degeneracy under certain conditions, this means that different sets of kinetic parameters can lead to similar FOM values. Furthermore, as the obtained kinetic parameters are deemed acceptable when the FOM value is below 5%, then the physical degeneracy is greater than the mathematical ones. This contribution provides a rigorous analysis of how the FOM values can be impacted by deviations between experimental and estimated data of kinetic parameters from the analysis process. This studio was carried out using a mathematical method based on Artificial Intelligence with a high capability to manage a large volume of data. Another advantage of this method is that the analysis to be extended to a wide range of experimental data.

Transcriptional activators in the early Drosophila embryo perform different kinetic roles.

Combinatorial regulation of gene expression by transcription factors (TFs) may in part arise from kinetic synergy-wherein TFs regulate different steps in the transcription cycle. Kinetic synergy requires that TFs play distinguishable kinetic roles. Here, we used live imaging to determine the kinetic roles of three TFs that activate transcription in the Drosophila embryo-Zelda, Bicoid, and Stat92E-by introducing their binding sites into the even-skipped stripe 2 enhancer. These TFs influence different sets of kinetic parameters, and their influence can change over time. All three TFs increased the fraction of transcriptionally active nuclei; Zelda also shortened the first-passage time into transcription and regulated the interval between transcription events. Stat92E also increased the lifetimes of active transcription. Different TFs can therefore play distinct kinetic roles in activating the transcription. This has consequences for understanding the composition and flexibility of regulatory DNA sequences and the biochemical function of TFs. A record of this paper's transparent peer review process is included in the supplemental information.

Kinetic analysis of complex chemical reactions by coupling model-free and model-fitting analysis

Three different complex multi-step reaction mechanisms of parallel-competing, consecutive and parallel-independent reactions were simulated for two different sets of kinetic parameters. The two sets were chosen in such a way to represent two very different situations. An advanced non-linear isoconversional method (NLN) and a multiple heating rates model-fitting method using nonlinear optimization (NLO) were applied to the data. The errors in kinetic parameters were evaluated for each set of data and for each method. It is shown that analysis of the thermoanalytical curves and of the Eα-dependencies can be used in complementary with the model-fitting approach for the elucidation of the reaction mechanisms and estimation of the kinetic parameters of each step. In most cases, NLO improved the accuracy of the kinetic parameters obtained from NLN method, but only if the mechanism was known. However, selecting the right mechanism is an essential problem of NLO. The study showed that it was possible to accurately fit the mechanism of two consecutive steps with a model of two independent steps that resulted in evaluating invalid kinetic parameters. This example also highlights an important limitation of the so-called deconvolution kinetic analysis which consists in fitting of various multi-step reaction mechanisms with a model of independent steps.

Defining Kinetic Roles of Transcriptional Activators in the Early Drosophila Embryo

Most animal transcription factors are categorized as activators or repressors without specifying their mechanisms of action. Defining their specific roles is critical for deciphering the logic of transcriptional regulation and predicting the function of regulatory sequences. Here, we define the kinetic roles of three activating transcription factors in the Drosophila embryo—Zelda, Bicoid and Stat92E—by introducing their binding sites into the even skipped stripe 2 enhancer and measuring transcriptional output with live imaging. We find that these transcription factors act on different sets of kinetic parameters, and these sets can change over the course of nuclear cycle (NC) 14. These transcription factors all increase the fraction of transcriptionally active nuclei. Zelda dramatically shortens the time interval between the start of NC 14 and initial activation, and Stat92E increases the duration of active transcription intervals throughout NC 14. Zelda also decreases the time intervals between instances of active transcription early in NC 14, while Stat92E does so later. Different transcription factors therefore play distinct kinetic roles in activating transcription; this has consequences for understanding both regulatory DNA sequences as well as the biochemical function of transcription factors.

Capsid destabilization and epitope alterations of human papillomavirus 18 in the presence of thimerosal

Thimerosal has been widely used as a preservative in drug and vaccine products for decades. Due to the strong propensity to modify thiols in proteins, conformational changes could occur due to covalent bond formation between ethylmercury (a degradant of thimerosal) and thiols. Such a conformational change could lead to partial or even complete loss of desirable protein function. This study aims to investigate the effects of thimerosal on the capsid stability and antigenicity of recombinant human papillomavirus (HPV) 18 virus-like particles (VLPs). Dramatic destabilization of the recombinant viral capsid upon thimerosal treatment was observed. Such a negative effect on the thermal stability of VLPs preserved with thimerosal was shown to be dependent on the thimerosal concentration. Two highly neutralizing antibodies, 13H12 and 3C3, were found to be the most sensitive to thimerosal treatment. The kinetics of antigenicity loss, when monitored with 13H12 or 3C3 as probes, yielded two distinctly different sets of kinetic parameters, while the data from both monoclonal antibodies (mAbs) followed a biphasic exponential decay model. The potential effect of thimerosal on protein function, particularly for thiol-containing proteinaceous active components, needs to be comprehensively characterized during formulation development when a preservative is necessary.

A systematic parametric study on the effect of a catalyst coating microstructure on its performance in methane steam reforming

A systematic approach is presented to study the effect of a catalyst coating microstructure on its performance in a wall-coated steam methane reformer using response surface methodology. Three-dimensional simulation of diffusion and reaction are performed in several catalyst microstructures represented by packing of overlapping spheres. A surrogate model is developed based on Latin hypercube design of experiment and response surface methodology that relates the rate of hydrogen production in the catalyst coating microstructure to inter-particle porosity and average particle size. Two sets of simulations are done based on a kinetic model with two different sets of kinetic parameters and the results are compared. The comparison shows that the maximum rate of hydrogen production occurs at higher inter-particle porosity and smaller particle size, when the kinetics is faster and diffusion limitation is more severe.

Is reactor light-off data sufficiently discriminating between kinetic parameters to be used for developing kinetic models of automotive exhaust aftertreatment catalysts? The effect of hysteresis induced by strong self inhibition

Mathematical models of automotive exhaust aftertreatment catalysts are often based on reaction kinetics developed using laboratory reactor light-off data. This paper investigates an effect observed while developing CO oxidation kinetics for a Diesel Oxidation Catalyst: it was found that while several different sets of kinetic parameters would give an equivalent prediction of the reactor data, they gave different predictions over a vehicle drive cycle. This represents a major problem when developing an aftertreatment catalyst model. This effect may be observed with any reaction which is strongly inhibited by one of the reactants and which can become external transport limited.The main cause of this effect is that the reaction exhibits hysteresis; kinetic parameters which predict the same behaviour when the temperature is increasing, give different predictions when the temperature is decreasing. The size of the hysteresis increases with the magnitude of the self-inhibition constant in the rate equation and with reactant concentration.A second cause for this effect is that if lower concentrations are encountered in the drive cycle than were used for kinetic development; kinetic parameters which give equivalent predictions at higher concentration may give different predictions at lower concentration.To avoid this problem, reactor data during both temperature ramp up and ramp down should be used for developing kinetics. However, it should be noted that it has been found that (i) not all experiments exhibit hysteresis and (ii) that discretisation of the catalyst into an exceptionally large number of elements is required for accurate prediction during ramp down.

Biological Nutrient Removal Model No. 2 (BNRM2): a general model for wastewater treatment plants

This paper presents the plant-wide model Biological Nutrient Removal Model No. 2 (BNRM2). Since nitrite was not considered in the BNRM1, and this previous model also failed to accurately simulate the anaerobic digestion because precipitation processes were not considered, an extension of BNRM1 has been developed. This extension comprises all the components and processes required to simulate nitrogen removal via nitrite and the formation of the solids most likely to precipitate in anaerobic digesters. The solids considered in BNRM2 are: struvite, amorphous calcium phosphate, hidroxyapatite, newberite, vivianite, strengite, variscite, and calcium carbonate. With regard to nitrogen removal via nitrite, apart from nitrite oxidizing bacteria two groups of ammonium oxidizing organisms (AOO) have been considered since different sets of kinetic parameters have been reported for the AOO present in activated sludge systems and SHARON (Single reactor system for High activity Ammonium Removal Over Nitrite) reactors. Due to the new processes considered, BNRM2 allows an accurate prediction of wastewater treatment plant performance in wider environmental and operating conditions.

Calculation of reactivities using ionization chamber currents with different sets of kinetic parameters for reduced scram system efficiency in the VVER-1000 of the third unit of the Kalinin nuclear power plant at the stage of physical start-up

The effectiveness of the VVER-1000 reactor scram system is analyzed using ionization chamber currents with different sets of kinetic parameters with allowance for the isotopic composition in the calculation of these parameters. The most “correct, aesthetically acceptable” results are obtained using the eight-group constants of the ROSFOND (BNAB-RF) library. The difference between the maximum and minimum values of the scram system effectiveness calculated with different sets of kinetic parameters slightly exceeds 2β. The problems of introducing corrections due to spatial effects are not considered in this study.

Models for nitrification process design: one or two AOB populations?

Models for engineering design of nitrifying systems use one ammonia oxidizer biomass (AOB) state variable. A simple extension using two AOB populations allows a more accurate prediction of nitrification systems at switching process environments. These two AOB subpopulations are characterized by two different sets of kinetic parameters. Selection pressure and competition between the two functional AOB populations are determined by process conditions as demonstrated by three case studies: Case study I describes dynamics of two AOB populations showing different temperature sensitivities (modified Arrhenius term on growth and decay) when bioaugmented from the warm sidestream treatment environment to the cold mainstream and vice-versa. Case study II investigates competition between fast growing micro-strategists and k-strategists adjusted to low ammonia levels depending on the internal mixed liquor recycle rate (IMLR). Case study III shows that AOB transferred from the waste activated sludge of an SBR to the parallel continuous flow system with different decay kinetics can overgrow or coexist with the original population.

Models for Nitrification Process Design: One or Two AOB Populations?

Models for engineering design of nitrifying systems use one ammonia oxidizer biomass (AOB) state variable. A simple extension using two AOB populations allows a more accurate prediction of nitrification systems at switching process environments. These two AOB subpopulations are characterized by two different sets of kinetic parameters. Selection pressure and competition between the two functional AOB populations are determined by process conditions as demonstrated by three case studies: Case study I describes dynamics of two AOB populations showing different temperature sensitivities (modified Arrhenius term on growth and decay) when bioaugmented from the warm sidestream treatment environment to the cold mainstream and vice-versa. Case study II investigates competition between fast growing μ-strategists and k-strategists adjusted to low ammonia levels depending on the internal mixed liquor recycle rate (IMLR). Case study III shows that AOB transferred from the waste activated sludge of an SBR to the parallel continuous flow system with different decay kinetics can overgrow or coexist with the original population.

New Approach To Elucidate Compensation Effect between Kinetic Parameters in Thermogravimetric Data

The study of thermogravimetric curves is commonly used to elucidate the likely processes involved during pyrolysis as well as determine the corresponding kinetic parameters. One of the main problems reported in the determination of such parameters is their ability to couple each other in such way that different sets of kinetic parameters can describe similar conversion degree curves once a kinetic model has been selected. Least-squared methods can be used to calculate the optimum kinetic parameters that minimize the squared differences between experimental and calculated data. Although the calculation can be successfully done, there are still questions to be answered. In the present work, a procedure to register the compensation effect between kinetic parameters is suggested.

Thermal history reconstruction by inversion of down-hole S2 pyrolysis data: synthetic studies and case histories

Thermal history reconstruction, as seen by buried kerogen samples, relies upon both a kinetic scheme for kerogen break down in the sub-surface as well as paleoheat flux information. A numerical model to invert S2 data from routine Rock-Eval pyrolysis was developed and tested against synthetic data sets and real data from two wells. The synthetic tests show that the S2 inverse model is sensitive to the present day heat flux ( Q 0 , to parameters describing paleoheat flow, to the number of probability channels for activation energy, and to the range of activation energy. The synthetic tests also show that for a given S2 data set, there can be different sets of kinetic parameters which give not only the same degree of fit between the calculated and measured S2 values, but also similar S2 evolution patterns with burial paths, i.e. there is no unique kinetic solution for a given set of data. The applications of the S2 inverse model to two real well data sets show that the S2 data can be used as a thermal indicator to determine the paleoheat flow and that the results are as useful as those from other thermal indicators, provided high quality S2 data are available. For an S2 data set with a mixture of kerogen types, it is first necessary to perform an un-mixing procedure prior to using the inverse model in order to obtain equivalent single type kerogen contributions, or else results obtained will not be trustworthy.