Estimation Of Kinetic Parameters Research Articles

Calculation of Thermochemical Kinetic Parameters of Several Types of Biomass Using Friedman Isoconversional Method

In the previous study, the activation energy of waste coffee grounds using the Kissinger model was 42.57 kJ/mol. In the case of the Friedman model, the activation energy value ranged from 44.01 to 350.20 kJ/mol at each conversion rate, while the average activation energy value recorded was 127.73 kJ/mol. In the case of the Kissinger model, the calculation method was simple and the activation energy value could be obtained in a short time, but the activation energy value was found to be low in the case of different pyrolysis characteristics as influenced by temperature. Since the Friedman model was able to obtain the activation energy value at each conversion rate change, it was suitable for biomass kinetic analysis. In the case of seven types of biomass from Philippines in Southeast Asia, the Friedman model used after several stages of data processing was applied to calculate the effective activation energy value from TGA data. In this study, processing data of biomass thermogravimetric analysis can help the estimation of kinetic parameters by reducing the error caused by the differential values of the reaction conversion rate at each operating temperature.

Mixing Matrix-corrected Whole-body Pharmacokinetic Modeling Using Longitudinal Micro-computed Tomography and Fluorescence-mediated Tomography

PurposePharmacokinetic modeling can be applied to quantify the kinetics of fluorescently labeled compounds using longitudinal micro-computed tomography and fluorescence-mediated tomography (μCT-FMT). However, fluorescence blurring from neighboring organs or tissues and the vasculature within tissues impede the accuracy in the estimation of kinetic parameters. Contributions of elimination and retention activities of fluorescent probes inside the kidneys and liver can be hard to distinguish by a kinetic model. This study proposes a deconvolution approach using a mixing matrix to model fluorescence contributions to improve whole-body pharmacokinetic modeling.ProceduresIn the kinetic model, a mixing matrix was applied to unmix the fluorescence blurring from neighboring tissues and blood vessels and unmix the fluorescence contributions of elimination and retention in the kidney and liver compartments. Accordingly, the kinetic parameters of the hepatobiliary and renal elimination routes and five major retention sites (the kidneys, liver, bone, spleen, and lung) were investigated in simulations and in an in vivo study. In the latter, the pharmacokinetics of four fluorescently labeled compounds (indocyanine green (ICG), HITC-iodide-microbubbles (MB), Cy7-nanogels (NG), and OsteoSense 750 EX (OS)) were evaluated in BALB/c nude mice.ResultsIn the simulations, the corrected modeling resulted in lower relative errors and stronger linear relationships (slopes close to 1) between the estimated and simulated parameters, compared to the uncorrected modeling. For the in vivo study, MB and NG showed significantly higher hepatic retention rates (P<0.05 and P<0.05, respectively), while OS had smaller renal and hepatic retention rates (P<0.01 and P<0.01, respectively). Additionally, the bone retention rate of OS was significantly higher (P<0.01).ConclusionsThe mixing matrix correction improves pharmacokinetic modeling and thus enables a more accurate assessment of the biodistribution of fluorescently labeled pharmaceuticals by μCT-FMT.

Estimating Kinetic Rate Parameters for Enzymatic Degradation of Lyophilized Silk Fibroin Sponges

Sponge-like biomaterials formed from silk fibroin are promising as degradable materials in clinical applications due to their controllable breakdown into simple amino acids or small peptides in vivo. Silk fibroin, isolated from Bombyx mori silkworm cocoons, can be used to form sponge-like materials with a variety of tunable parameters including the elastic modulus, porosity and pore size, and level of nanocrystalline domains. These parameters can be independently tuned during formulation resulting in a wide parameter space and set of final materials. Determining the mechanism and rate constants for biomaterial degradation of these tunable silk materials would allow scientists to evaluate and predict the biomaterial performance for the large array of tissue engineering applications and patient ailments a priori. We first measured in vitro degradation rates of silk sponges using common protein-degrading enzymes such as Proteinase K and Protease XIV. The concentration of the enzyme in solution was varied (1, 0.1, 0.01 U/mL) along with one silk sponge formulation parameter: the level of crystallinity within the sponge. Additionally, two experimental degradation methods were evaluated, termed continuous and discrete degradation methods. Silk concentration, polymer chain length and scaffold pore size were held constant during experimentation and kinetic parameter estimation. Experimentally, we observed that the enzyme itself, enzyme concentration within the bulk solution, and the sponge fabrication water annealing time were the major experimental parameters dictating silk sponge degradation in our experimental design. We fit the experimental data to two models, a Michaelis-Menten kinetic model and a modified first order kinetic model. Weighted, non-linear least squares analysis was used to determine the parameters from the data sets and Monte-Carlo simulations were utilized to obtain estimates of the error. We found that modified first order reaction kinetics fit the time-dependent degradation of lyophilized silk sponges and we obtained first order-like rate constants. These results represent the first investigations into determining kinetic parameters to predict lyophilized silk sponge degradation rates and can be a tool for future mathematical representations of silk biomaterial degradation.

Operator dependency of arterial input function in dynamic contrast-enhanced MRI

ObjectiveTo investigate the effect of inter-operator variability in arterial input function (AIF) definition on kinetic parameter estimates (KPEs) from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas.MethodsThe study included 118 DCE series from 23 patients. AIFs were measured by three domain experts (DEs), and a population AIF (pop-AIF) was constructed from the measured AIFs. The DE-AIFs, pop-AIF and AUC-normalized DE-AIFs were used for pharmacokinetic analysis with the extended Tofts model. AIF-dependence of KPEs was assessed by intraclass correlation coefficient (ICC) analysis, and the impact on relative longitudinal change in Ktrans was assessed by Fleiss’ kappa (κ).ResultsThere was a moderate to substantial agreement (ICC 0.51–0.76) between KPEs when using DE-AIFs, while AUC-normalized AIFs yielded ICC 0.77–0.95 for Ktrans, kep and ve and ICC 0.70 for vp. Inclusion of the pop-AIF did not reduce agreement. Agreement in relative longitudinal change in Ktrans was moderate (κ = 0.591) using DE-AIFs, while AUC-normalized AIFs gave substantial (κ = 0.809) agreement.DiscussionAUC-normalized AIFs can reduce the variation in kinetic parameter results originating from operator input. The pop-AIF presented in this work may be applied in absence of a satisfactory measurement.

Bridging principal component analysis and method of moments based parameter estimation for grafting of polybutadiene with styrene

A challenge for the design of nonlinear polymerization is the full appreciation of the impact of side reactions, demanding the development of modeling techniques to determine the associated kinetic parameters while using the most important experimental responses. Here the combination of computationally inexpensive method of moments (MoM) kinetic simulations and dedicated principal component analysis (PCA) is put forward as a promising strategy to be successful in this respect. Focus is on (radical) vinyl grafting of chains containing unsaturations, selecting styrene (St) as monomer and polybutadiene (PB) as backbone, and low St conversions accounting for diffusional limitations on termination. It is highlighted that the less studied macropropagation cannot be directly ignored and a combined set of experimental responses related to free polystyrene and grafted copolymer (GC) average product properties is recommended for kinetic parameter estimation. This is supported by regression analysis considering in silico generated experimental data compensated for random noise and considering a validated end-chain approximation.

PET-ABC: fully Bayesian likelihood-free inference for kinetic models

Aims. We describe an intuitive, easy to use method called PET-ABC that enables full Bayesian statistical inference from single subject dynamic PET data. The performance of PET-ABC was compared with weighted non-linear least squares (WNLS) in terms of reliability of kinetic parameter estimation and statistical power for model selection. Methods. Dynamic PET data based on 1-tissue and 2-tissue compartmental models were simulated with 2 noise models and 3 noise levels. PET-ABC was used to evaluate the reliability of parameter estimates under each condition. It was also used to perform model selection for a simulated noisy dataset composed of a mixture of 1- and 2-tissue compartment kinetics. Finally, PET-ABC was used to analyze a non-steady state dynamic [11C] raclopride study performed on a fully conscious rat administered either 2 mg.kg−1 amphetamine or saline 20 min after tracer injection. Results. PET-ABC yielded posterior point estimates for model parameters with smaller variance than WNLS, as well as probability density functions indicating confidence intervals for those estimates. It successfully identified the superiority of a 2-tissue compartment model to fit the simulated mixed model data. For the drug challenge study, the post observation probability of striatal displacement of the PET signal was 0.9 for amphetamine and approximately 0 for saline, indicating a high probability of amphetamine-induced endogenous dopamine release in the striatum. PET-ABC also demonstrated superior statistical power to WNLS (0.87 versus 0.09) for selecting the correct model in a simulated ligand displacement study. Conclusions. PET-ABC is a simple and intuitive method that provides complete Bayesian statistical analysis of single subject dynamic PET data, including the extent to which model parameter estimates and model choice are supported by the data. Software for PET-ABC is freely available as part of the PETabc package https://github.com/cgrazian/PETabc.

Reaction kinetics of coal char in oxy-fuel combustion environment and characterization of reacted char

ABSTRACT The work primarily focuses on combustion kinetics of devolatilized coal char fines (75–120 µm), including the estimation of kinetic parameters and char morphological transformation in oxy-fuel environment (O2-CO2 and O2-N2). Experimental work has been carried out to study the effect of higher CO2 concentration and elevated O2 concentrations on the ignition, burnout behavior of pulverized coal char both non-isothermally and isothermally. The coal chars obtained from pyrolyzing three different types of coal are used in experiments; a high ash (HA) coal, a medium ash (MA) coal, and a low ash (LA) coal. Three different kinetic models, namely VM, GM and RPM, have been employed to determine the kinetic parameters. The kinetic parameters are observed to be a strong function of oxygen concentration for all the three models. The estimated activation energy employing non-isothermal analysis for LA coal char using VM lies in the range of 203–212 kJ/mol. In comparison, the activation energy predicted by GM for HA coal char lies in the range 137–144 kJ/mol. Moreover, the activation energy estimated for MA coal char by RPM varies in the range of 134–142 kJ/mol. It is also explored during the non-isothermal analysis that an increase in char particle size from 75–120 µm to 250–300 µm shows a small increase in the values of apparent activation energy and pre-exponential factor. The activation energy values calculated employing the isothermal method are 106–126 kJ mol−1 (using RPM) for coal char MA and 96–135 kJ mol−1 (using GM) for coal char HA. Both the isothermal and non-isothermal kinetic analysis reveals that the combustion of coal char LA follows VM while coal char MA and HA follows RPM and GM in order. SEM images and BET surface area analysis for partially converted coal chars are further analyzed to justify the suitability of different reaction mechanism followed by various coal chars.

CO Preferential Oxidation in a Microchannel Reactor Using a Ru-Cs/Al2O3 Catalyst: Experimentation and CFD Modelling

This work presents an experimental and modelling evaluation of the preferential oxidation of CO (CO PROX) from a H2-rich gas stream typically produced from fossil fuels and ultimately intended for hydrogen fuel cell applications. A microchannel reactor containing a washcoated 8.5 wt.% Ru/Al2O3 catalyst was used to preferentially oxidise CO to form CO2 in a gas stream containing (by vol.%): 1.4% CO, 10% CO2, 18% N2, 68.6% H2, and 2% added O2. CO concentrations in the product gas were as low as 42 ppm (99.7% CO conversion) at reaction temperatures in the range 120–140 °C and space velocities in the range 65.2–97.8 NL gcat−1 h−1. For these conditions, less than 4% of the H2 feed was consumed via its oxidation and reverse water-gas shift. Furthermore, a computational fluid dynamic (CFD) model describing the microchannel reactor for CO PROX was developed. With kinetic parameter estimation and goodness of fit calculations, it was determined that the model described the reactor with a confidence interval far greater than 95%. In the temperature range 100–200 °C, the model yielded CO PROX reaction rate profiles, with associated mass transport properties, within the axial dimension of the microchannels––not quantifiable during the experimental investigation. This work demonstrates that microchannel reactor technology, supporting an active catalyst for CO PROX, is well suited for CO abatement in a H2-rich gas stream at moderate reaction temperatures and high space velocities.

Kinetic analysis and reaction mechanism of p-alkoxybenzyl alcohol ([4-(hydroxymethyl)phenoxymethyl]polystyrene) resin pyrolysis: Revealing new information on thermal stability

This study provides new information on the thermal stability and kinetics of pyrolysis of commonly used resins in solid-phase peptide synthesis. Pyrolysis process of 4-benzyloxybenzyl alcohol resin was monitored by simultaneous TGA-DTG measurements in a nitrogen atmosphere, at four different heating rates (5, 10, 20 and 30 K min−1). Kinetic computations included the model-free (isoconversional) and model-based methods for reliable estimation of kinetic parameters and the mechanism of investigated process. Based on the findings from applied methods, the pyrolysis process can be described by three reaction steps, where one of them encompasses the consecutive reactions. The first step (n-th order reaction, referred to Fn) is attributed to the random scission of the main chain in PS (polystyrene) solid support, where polymer radicals were formed. The second step proceeds through homolytic cleavage path of ether linkage of the resin (by the first-order reaction, referred to F1) and then via phenoxy radical (semiquinone) formation which is converted into the benzoquinone through a dehydrogenation (by autocatalysis reaction, referred to Cnm). Finally, the third step (referred to Fn reaction) was characterized by depolymerization mechanism, which takes place at higher temperatures (T > 385 °C). With determined kinetic parameters and the most probable mechanism functions, the model agrees excellently with experimental data. By applying the obtained kinetic results, isothermal life-time prediction was also performed.

Critical aspects in exploring time analysis for the voltammetric estimation of kinetic parameters of surface electrode mechanisms coupled with chemical reactions

The accurate determination of the rate constant related to the electron transfer step of so-called “surface redox active compounds” by voltammetric measurements is very important because it is linked to the reactivity and stability of many biological and chemical systems such as redox enzymes, vitamins, hormones, and many more. Evaluation of the kinetics of the electron transfer is often challenging, especially when chemical equilibria are coupled to the electron transfer step. In this work, we theoretically consider some critical aspects of the time-related methodologies in square-wave voltammetry (SWV), which is designed to analyze the kinetics of the electron transfer step of surface mechanisms coupled with chemical reactions. We demonstrate with a series of simulated scenarios that caution must be taken when exploring the time-related analysis for kinetic characterizations for both surface CE and EC mechanisms. The main concern stems from the fact that the SW frequency simultaneously affects both the kinetics of electron transfer and that of chemical reactions as well. Under defined conditions, the SW frequency variation in the case of surface EC and CE mechanisms may produce unexpected features of the voltammetric patterns. In many scenarios, time-independent analysis, such as those related to the square-wave amplitude and potential increment, are seen as alternative tools to evaluate the rate parameter of electrode reactions.

Kinetic parameters estimation and reaction model modification for thermal degradation of Beizao oil shale based on thermogravimetric analysis coupled with deconvolution procedure

Pyrolysis characteristics of Beizao oil shale in nitrogen were investigated by thermogravimetric analysis coupled with deconvolution procedure. A new method was proposed to separate multistep thermal decomposition process. Kissinger-Kai method was employed to determine the peak positions of main components involved in the complex reaction, and the overlapping peaks were separated by bi-Gaussian function according to the thermal knowledge of sub-reaction. The results showed that four parallel reactions were observed in the whole process, including the decomposition of kerogen, pyrite, carbonate, as well as removal of crystal water from clay minerals. Furthermore, the kinetic parameters of the separated reactions were estimated by traditional model-free and model-fitting methods on the basis of deconvolution analysis, wherein the optimal reaction models were modified with the accommodation function. Eventually, these parameters were optimized to predict the pyrolysis behavior. It was found that the obtained kinetic parameters and four-component reaction models can well characterize the entire pyrolysis process of Beizao oil shale.

Chemical Kinetics Bayesian Inference Toolbox (CKBIT)

The robust estimation of chemical kinetic parameters and their associated uncertainty is essential in the field of chemistry and catalysis. The Chemical Kinetics Bayesian Inference Toolbox (CKBIT) is a Python software library introduced to enable users to implement advanced Bayesian inference techniques for kinetic parameter estimation and uncertainty quantification. Leveraging functionalities of other open source Python packages and offering simplified implementation through minimal user-required coding and straightforward Excel input files, CKBIT aspires to make the inference method easily accessible for chemical kinetics. CKBIT provides maximum a posteriori, Markov chain Monte Carlo, and variational inference estimation options. Users may apply these functionalities to estimate activation energies, reaction orders, and pre-exponential terms from chemical reaction data from batch reactors, continuous stirred-tank reactors, and plug flow reactors. The availability of prior distribution specification and the implementation of hierarchical modeling in CKBIT provide a heightened level of accuracy in estimates of kinetic parameters and their uncertainties. Program summaryProgram title: CKBITCPC Library link to program files:https://doi.org/10.17632/tnzk2jvffs.1Developer's repository link:https://github.com/VlachosGroup/ckbitCode Ocean capsule:https://codeocean.com/capsule/8389927Licensing provisions: MIT licenseProgramming language: PythonNature of problem: Rigorous estimation and uncertainty quantification of kinetic rate parameters are necessary for chemical researchers to create physical models with explicit levels of certainty. Advanced statistical treatments of Bayesian inference meet this need with a high level of rigor. However, current software available for Bayesian inference is complex and nuanced in its implementation, preventing widespread adoption among researchers.Solution method: We present a Python package with a modular approach to applying different Bayesian inference techniques for kinetic rate parameter estimation and uncertainty quantification. Optimal point estimates can be obtained through maximum a posteriori estimation, while full probability distributions of parameters can be generated through Markov chain Monte Carlo estimation or variational inference. The code is straightforward to use in contrast to current Bayesian inference software, and it interfaces with Excel for ease of data entry.

Kinetic and Static Analysis of Poly-(Adenosine Diphosphate-Ribose) Polymerase-1-Targeted 18F-Fluorthanatrace PET Images of Ovarian Cancer.

The poly-(adenosine diphosphate-ribose) polymerase (PARP) family of proteins participates in numerous functions, most notably the DNA damage response. Cancer vulnerability to DNA damage has led to development of several PARP inhibitors (PARPi). This class of drugs has demonstrated therapeutic efficacy in ovarian, breast, and prostate cancers, but with variable response. Consequently, clinics need to select patients likely to benefit from these targeted therapies. In vivo imaging of 18F-fluorthanatrace uptake has been shown to correspond to PARP-1 expression in tissue. This study characterized the pharmacokinetics of 18F-fluorthanatrace and tested kinetic and static models to guide metric selection in future studies assessing 18F-fluorthanatrace as a biomarker of response to PARPi therapy. Methods: Fourteen prospectively enrolled ovarian cancer patients were injected with 18F-fluorthanatrace and imaged dynamically for 60 min after injection followed by up to 2 whole-body scans, with venous blood activity and metabolite measurements. SUVmax and SUVpeak were extracted from dynamic images and whole-body scans. Kinetic parameter estimates and SUVs were assessed for correlations with tissue PARP-1 immunofluorescence (n = 7). Simulations of population kinetic parameters enabled estimation of measurement bias and precision in parameter estimates. Results:18F-fluorthanatrace blood clearance was variable, but labeled metabolite profiles were similar across patients, supporting use of a population parent fraction curve. The total distribution volume from a reversible 2-tissue-compartment model and Logan reference tissue distribution volume ratio (DVR) from the first hour of PET acquisition correlated with tumor PARP-1 expression by immunofluorescence (r = 0.76 and 0.83, respectively; P < 0.05). DVR bias and precision estimates were 6.4% and 29.1%, respectively. SUVmax and SUVpeak acquired from images with midpoints of 57.5, 110 ± 3, and 199 ± 4 min highly correlated with PARP-1 expression (mean ± SD, r ≥ 0.79; P < 0.05). Conclusion: Tumor SUVmax and SUVpeak at 55-60 min after injection and later and DVR from at least 60 min appear to be robust noninvasive measures of PARP-1 binding. 18F-fluorthanatrace uptake in ovarian cancer was best described by models of reversible binding. However, pharmacokinetic patterns of tracer uptake were somewhat variable, especially at later time points.

Evaluation and comparison of thermodynamic and kinetic parameters for oxidation and pyrolysis of Yarega heavy crude oil asphaltenes

The processes of pyrolysis in atmosphere of nitrogen and oxidation in air were compared for asphaltenes extracted from Yarega heavy crude oil and its atmospheric and vacuum residues. Thermogravimetry, differential scanning calorimetry and kinetic studies were applied to estimate kinetic parameters of the asphaltenes samples. Different stages of pyrolysis and oxidation were determined and their temperatures were estimated. The steps of pyrolysis and oxidation obtained through different methods were compared. The asphaltenes from atmospheric residue and heavy parent oil showed two pyrolysis stages. Only one stage was distinguished for vacuum residue asphaltenes. Three stages were described for oxidation process. The values of rate of mass loss, heat flow, heat consumption and activation energy were calculated for conversion stages. Data from conversion studies was interpreted through physical processes. The obtained results can contribute to the development of methods for asphaltenes processing in petroleum refineries.

Inhibition of Glucose-6-Phosphate Dehydrogenase Activity Attenuates Right Ventricle Pressure and Hypertrophy Elicited by VEGFR Inhibitor + Hypoxia.

Pulmonary hypertension (PH) is a disease of hyperplasia of pulmonary vascular cells. The pentose phosphate pathway (PPP)-a fundamental glucose metabolism pathway-is vital for cell growth. Because treatment of PH is inadequate, our goal was to determine whether inhibition of glucose-6-phosphate dehydrogenase (G6PD), the rate-limiting enzyme of the PPP, prevents maladaptive gene expression that promotes smooth muscle cell (SMC) growth, reduces pulmonary artery remodeling, and normalizes hemodynamics in experimental models of PH. PH was induced in mice by exposure to 10% oxygen (Hx) or weekly injection of vascular endothelial growth factor receptor blocker [Sugen5416 (SU); 20 mg kg-1] during exposure to hypoxia (Hx + SU). A novel G6PD inhibitor (N-[(3β,5α)-17-oxoandrostan-3-yl]sulfamide; 1.5 mg kg-1) was injected daily during exposure to Hx. We measured right ventricle (RV) pressure and left ventricle pressure-volume relationships and gene expression in lungs of normoxic, Hx, and Hx + SU and G6PD inhibitor-treated mice. RV systolic and end-diastolic pressures were higher in Hx and Hx + SU than normoxic control mice. Hx and Hx + SU decreased expression of epigenetic modifiers (writers and erasers), increased hypomethylation of the DNA, and induced aberrant gene expression in lungs. G6PD inhibition decreased maladaptive expression of genes and SMC growth, reduced pulmonary vascular remodeling, and decreased right ventricle pressures compared with untreated PH groups. Pharmacologic inhibition of G6PD activity, by normalizing activity of epigenetic modifiers and DNA methylation, efficaciously reduces RV pressure overload in Hx and Hx + SU mice and preclinical models of PH and appears to be a safe pharmacotherapeutic strategy. SIGNIFICANCE STATEMENT: The results of this study demonstrated that inhibition of a metabolic enzyme efficaciously reduces pulmonary hypertension. For the first time, this study shows that a novel inhibitor of glucose-6-phosphate dehydrogenase, the rate-limiting enzyme in the fundamental pentose phosphate pathway, modulates DNA methylation and alleviates pulmonary artery remodeling and dilates pulmonary artery to reduce pulmonary hypertension.

Growth of Listeria monocytogenes in Partially Cooked Battered Chicken Nuggets as a Function of Storage Temperature.

Battered poultry products may be wrongly regarded and treated by consumers as ready-to-eat and, as such, be implicated in foodborne disease outbreaks. This study aimed at the quantitative description of the growth behavior of Listeria monocytogenes in fresh, partially cooked (non-ready-to-eat) battered chicken nuggets as function of temperature. Commercially prepared chicken breast nuggets were inoculated with L. monocytogenes and stored at different isothermal conditions (4, 8, 12, and 16 °C). The pathogen’s growth behavior was characterized via a two-step predictive modelling approach: estimation of growth kinetic parameters using a primary model, and description of the effect of temperature on the estimated maximum specific growth rate (μmax) using a secondary model. Model evaluation was undertaken using independent growth data under both constant and dynamic temperature conditions. According to the findings of this study, L. monocytogenes may proliferate in battered chicken nuggets in the course of their shelf life to levels potentially hazardous for susceptible population groups, even under well-controlled refrigerated storage conditions. Model evaluation demonstrated a satisfactory performance, where the estimated bias factor (Bf) was 0.92 and 1.08 under constant and dynamic temperature conditions, respectively, while the accuracy factor (Af) value was 1.08, in both cases. The collected data should be useful in model development and quantitative microbiological risk assessment in battered poultry products.

Improving grain size analysis using computer vision techniques and implications for grain growth kinetics

Abstract Earth’s physical properties and mantle dynamics are strongly dependent on mantle grain size, shape, and orientation, but these characteristics are poorly constrained. Experimental studies provide an opportunity to simulate the grain growth kinetics of mantle aggregates. The experimentally determined grain sizes can be fit to the normal grain growth law (Gn – G0n) = k0t∙exp(–ΔH/RT) and then be used to determine grain size throughout the mantle and geological time. The grain growth dynamics of spinelorthopyroxene mixtures in the upper mantle are modeled here by experimentally producing small grain sizes in the range of 0.5 to 2 µm radius at pressures and temperatures equivalent to the spinel lherzolite stability field. To accurately measure the sizes of these small grains, we have developed a computer vision workflow; using a watershed transformation, which rapidly measures 68% more grains and produces a 20% improvement in the average grain size accuracy and repeatability when compared with manual methods. Using this automated approach, we have been able to identify a significant proportion of small grains, which have been overlooked when using manual methods. This additional population of grains, when fit to the normal grain growth law, highlights the influence of improved accuracy and sample size on the estimation of grain growth kinetic parameters. Our results demonstrate that automatic computer vision enables a systematic, fast, repeatable method of grain size analysis, across large data sets, improving the accuracy of experimentally determined grain growth kinetics.

A Review of Enzyme Induced Carbonate Precipitation (EICP): The Role of Enzyme Kinetics

Enzyme-induced carbonate precipitation (EICP) is a relatively new bio-cementation technique for ground improvement. In EICP, calcium carbonate (CaCO3) precipitation occurs via urea hydrolysis catalysed by the urease enzyme sourced from plants. EICP offers significant potential for innovative and sustainable engineering applications, including strengthening of soils, remediation of contaminants, enhancement of oil recovery through bio-plugging and other in situ field applications. Given the numerous potential applications of EICP, theoretical understanding of the rate and quantity of CaCO3 precipitation via the ureolytic chemical reaction is vital for optimising the process. For instance, in a typical EICP process, the rate and quantity of CaCO3 precipitation can depend significantly on the concentration, activity and kinetic properties of the enzyme used along with the reaction environment such as pH and temperature. This paper reviews the research and development of enzyme-catalysed reactions and its applications for enhancing CaCO3 precipitation in EICP. The paper also presents the assessment and estimation of kinetic parameters, such as the maximal reaction velocity (Vmax) and the Michaelis constant (Km), that are associated with applications in civil and geotechnical engineering. Various models for evaluating the kinetic reactions in EICP are presented and discussed, taking into account the influence of pH, temperature and inhibitors. It is shown that a good understanding of the kinetic properties of the urease enzyme can be useful in the development, optimisation and prediction of the rate of CaCO3 precipitation in EICP.

Inhibitory mechanism of O-methylated quercetins, highly potent β-secretase inhibitors isolated from Caragana balchaschensis (Kom.) Pojark

Ethnopharmacological relevanceCaragana has a standing history of implementation in Traditional Chinese Medicine (TCM). Most species of this genus have been explored for multi-functional purposes, such as promoting blood circulation and curing neuralgia, fatigue, migraine, arthritis, and vascular hypertension (Meng et al., 2009). Among them, the well-known species C. sinica showed the most promising potential to increase the expression of ADAM10 among 313 tested medicinal plants, which is one of the promising approach for the treatment of Alzheimer's disease (AD). (Schuck et al., 2015). Aim of this studyThe aim of this work is to explore β-secretase inhibitory activity of compounds isolated from the aerial part of endemic Caragana balchaschensis (Kom.) Pojark. We provided a full characterization of their inhibitory mechanisms, binding affinities, and binding modes. Materials and methodsThe isolation of quercetin derivatives was accomplished by various chromatographical approaches and their structures were annotated by spectroscopic analysis. The detailed kinetic behavior of β-secretase inhibitors was determined by estimation of kinetic parameters (Km, Vmax, KI, and KIS). Binding affinities (KSV) and binding modes of inhibitors were elucidated by fluorescence quenching and molecular docking studies, respectively. ResultsO-methylated quercetins (2-7) were significantly effective in β-secretase inhibition with IC50 ranging from 1.2 to 6.5 μM. The most active one (6) was 20-fold effective than the mother skeleton, quercetin. The O-methyl motif was a critical factor in β-secretase inhibition: tri-O-methylated (1.2 μM) > di-O-methylated (3.5 μM) > mono-O-methylated (6.5 μM) > quercetin (25.2 μM). In the kinetic study, all quercetins (1–7) showed a noncompetitive inhibition, but glucoside ones (8 and 9) were mixed type I inhibitors. The binding affinities (KSV) were agreed with inhibitory potencies. The O-methylated quercetins were annotated as the most natural abundant metabolites in the aerial part by LC-ESI-TOF/MS. Binding modes of inhibitors to enzyme were elucidated by molecular docking experiments. ConclusionThis study disclosed that most of the major phenolic metabolites of the aerial part of C. balchaschensis are O-methylated quercetins, which have a significant inhibitory effect on β-secretase, which is a critical factor for AD.

Modelling the dynamic long-term performance of a full-scale digester treating sludge from an urban WRRF using an extended version of ADM1

The optimization of full-scale digesters requires the development of models able to describe the main process performances, due to the strategic role of anaerobic digestion in the sustainability of Water Resource Recovery Facilities (WRRFs). In this study, an extended version of the Anaerobic Digestion Model No. 1 (ADM1) was developed to simulate the long-term performance of a full-scale anaerobic digester treating primary and activated sludge of a large WWRF (300,000 p.e.). Monitored data were collected over 973 days and analyzed in order to provide reconciled datasets for modeling purposes. A comprehensive influent characterization in terms of biodegradability extent, COD and N content was carried out by combining measurements of several parameters and Biochemical Methane Potential (BMP) tests to provide a compatible ADM1 substrate fractionation. The model was calibrated by comparing simulation results against both routine plant measurements and data from a dedicated measuring campaign. Disintegration constants and particulate inert fractions were the sole estimated model parameters. Disintegration constants determined in the calibration step resulted in values 5-times greater than those initially estimated from BMP tests. The model was able to accurately predict digestate composition in terms of TSS, VSS, COD, N and P as well as biogas production and methane content. It was demonstrated that a precise description of the anaerobic co-digestion of sewage sludges relies on the estimation of solubilization kinetic parameters together with the use of two different composite variables representing each substrate. The developed model proved to be a useful tool that can be integrated in a plant-wide model to assess plant process interactions under different operating conditions