Advanced Kinetic Models Research Articles

Advancements, Challenges, and Future Prospects in Clinical Hyperpolarized Magnetic Resonance Imaging: A Comprehensive Review

Hyperpolarized (HP) magnetic resonance imaging (MRI) is a groundbreaking imaging platform advancing from research to clinical practice, offering new possibilities for real-time, non-invasive metabolic imaging. This review explores the latest advancements, challenges, and future directions of HP MRI, emphasizing its transformative impact on both translational research and clinical applications. By employing techniques such as dissolution Dynamic Nuclear Polarization (dDNP), Parahydrogen-Induced Polarization (PHIP), Signal Amplification by Reversible Exchange (SABRE), and Spin-Exchange Optical Pumping (SEOP), HP MRI achieves enhanced nuclear spin polarization, enabling in vivo visualization of metabolic pathways with exceptional sensitivity. Current challenges, such as limited imaging windows, complex pre-scan protocols, and data processing difficulties, are addressed through innovative solutions like advanced pulse sequences, bolus tracking, and kinetic modeling. We highlight the evolution of HP MRI technology, focusing on its potential to revolutionize disease diagnosis and monitoring by revealing metabolic processes beyond the reach of conventional MRI and positron emission tomography (PET). Key advancements include the development of novel tracers like [2-13C]pyruvate and [1-13C]-alpha-ketoglutarate and improved data analysis techniques, broadening the scope of clinical metabolic imaging. Future prospects emphasize integrating artificial intelligence, standardizing imaging protocols, and developing new hyperpolarized agents to enhance reproducibility and expand clinical capabilities particularly in oncology, cardiology, and neurology. Ultimately, we envisioned HP MRI as a standardized modality for dynamic metabolic imaging in clinical practice.

Synergistic ternary deep eutectic solvents: An archetype for sustainable and eco-conscious Li and Co recovery from spent batteries

The development of sustainable materials for energy is a critical challenge in the pursuit of a circular economy. This study presents an innovative approach for recycling lithium-ion batteries (LIBs) using a novel, sustainable deep eutectic solvent (DES) composed of glycine, ascorbic acid, and water. This natural, chloride-free ternary DES leverages the biocompatible and eco-friendly properties of its constituents, achieving exceptional leaching efficiency of 99.1 % for lithium and 97.9 % for cobalt under optimized conditions. A multifaceted methodology integrating advanced factorial design, kinetic modeling, and machine learning was employed to refine the process parameters and enhance leaching efficiency. The extraction kinetics were predominantly governed by endothermic surface reactions, characterized by activation energies of 81.5 kJ/mol for Co and 110.9 kJ/mol for Li. An ensemble neural network model accurately predicted the extraction with an R2 > 0.95, demonstrating the robustness of the computational approach. The DES exhibited excellent recyclability over five cycles, thereby emphasizing its potential for practical industrial applications in the field of sustainable materials management. This approach facilitated the selective recovery of high-purity lithium oxalate and cobalt oxalate, underscoring the efficiency and environmental benefits of the process. DFT calculations provided insights into the extraction mechanism, revealing the crucial role of hydrogen bonding and synergistic effects of the DES components. This pioneering study not only advances LIB recycling but also establishes a comprehensive, quantitatively validated framework, paving the way for future innovations in sustainable materials management and supporting global efforts towards a circular economy in energy storage.

Comprehensive thermal properties, kinetic, and thermodynamic analyses of biomass wastes pyrolysis via TGA and Coats-Redfern methodologies

This study comprehensively analyzes the thermal decomposition characteristics as well as the kinetic and thermodynamic parameters of five biomass wastes, including coffee husk, groundnut shell, macadamia nutshell, rice husk, and tea waste, using Thermogravimetric Analysis (TGA) and the Coats-Redfern method. The TGA experiments were conducted on a PerkinElmer STA 6000 instrument under an inert N2 atmosphere with a heating rate of 20 °C/min, spanning a temperature range from 25 °C to 950 °C. The results identified three distinct pyrolysis stages: drying, devolatilization, and char formation, with macadamia nutshell demonstrating the highest thermal reactivity and efficient devolatilization characteristics, reflected by its lowest initial devolatilization temperature (175 °C) and highest peak temperature (380 °C). Kinetic analysis revealed that coffee husk had the highest overall activation energy (Ea) of 60.59 kJ/mol, indicating complex thermal degradation behavior. The thermodynamic evaluation showed that coffee husk also exhibited the highest enthalpy change (ΔH=55.46 kJ/mol) but the lowest Gibbs free energy change (ΔG=148.34 kJ/mol), suggesting high energy requirements for decomposition but relatively more spontaneous reactions compared to other biomass types. Macadamia nutshell demonstrated high ΔG (163.24 kJ/mol) and moderate ΔH (32.44 kJ/mol), reflecting greater resistance to spontaneous decomposition. The comprehensive pyrolysis index (CPI) and devolatilization index (Ddev) confirmed macadamia nutshell as the most reactive biomass, while rice husk exhibited the lowest reactivity. The findings highlight the importance of multi-step kinetic analysis for accurately understanding pyrolysis processes, providing critical insights for optimizing biomass conversion for energy production. Future research should explore co-pyrolysis with varied biomass mixtures and advanced kinetic modeling to enhance energy yields.

Exploration of Resveratrol as a Potent Modulator of α-Synuclein Fibril Formation.

The molecular determinants of amyloid protein misfolding and aggregation are key for the development of therapeutic interventions in neurodegenerative disease. Although small synthetic molecules, bifunctional molecules, and natural products offer a potentially advantageous approach to therapeutics to remodel aggregation, their evaluation requires new platforms that are informed at the molecular level. To that end, we chose pulsed hydrogen/deuterium exchange mass spectrometry (HDX-MS) to discern the phenomena of aggregation modulation for a model system of alpha synuclein (αS) and resveratrol, an antiamyloid compound. We invoked, as a complement to HDX, advanced kinetic modeling described here to illuminate the details of aggregation and to determine the number of oligomeric populations by kinetically fitting the experimental data under conditions of limited proteolysis. The misfolding of αS is most evident within and nearby the nonamyloid-β component region, and resveratrol significantly remodels that aggregation. HDX distinguishes readily a less solvent-accessible, more structured oligomer that coexists with a solvent-accessible, more disordered oligomer during aggregation. A view of the misfolding emerges from time-dependent changes in the fractional species across the protein with or without resveratrol, while details were determined through kinetic modeling of the protected species. A detailed picture of the inhibitory action of resveratrol with time and regional specificity emerges, a picture that can be obtained for other inhibitors and amyloid proteins. Moreover, the model reveals that new states of aggregation are sampled, providing new insights on amyloid formation. The findings were corroborated by circular dichroism and transmission electron microscopy.

Imaging glymphatic response to glioblastoma

BackgroundThe glymphatic system actively exchanges cerebrospinal fluid (CSF) and interstitial fluid (ISF) to eliminate toxic interstitial waste solutes from the brain parenchyma. Impairment of the glymphatic system has been linked to several neurological conditions. Glioblastoma, also known as Glioblastoma multiforme (GBM) is a highly aggressive form of malignant brain cancer within the glioma category. However, the impact of GBM on the functioning of the glymphatic system has not been investigated. Using dynamic contrast-enhanced magnetic resonance imaging (CE-MRI) and advanced kinetic modeling, we examined the changes in the glymphatic system in rats with GBM.MethodsDynamic 3D contrast-enhanced T1-weighted imaging (T1WI) with intra-cisterna magna (ICM) infusion of paramagnetic Gd-DTPA contrast agent was used for MRI glymphatic measurements in both GBM-induced and control rats. Glymphatic flow in the whole brain and the olfactory bulb was analyzed using model-derived parameters of arrival time, infusion rate, clearance rate, and residual that describe the dynamics of CSF tracer over time.Results3D dynamic T1WI data identified reduced glymphatic influx and clearance, indicating an impaired glymphatic system due to GBM. Kinetic modeling and quantitative analyses consistently indicated significantly reduced infusion rate, clearance rate, and increased residual of CSF tracer in GBM rats compared to control rats, suggesting restricted glymphatic flow in the brain with GBM. In addition, our results identified compromised perineural pathway along the optic nerves in GBM rats.ConclusionsOur study demonstrates the presence of GBM-impaired glymphatic response in the rat brain and impaired perineural pathway along the optic nerves. Reduced glymphatic waste clearance may lead to the accumulation of toxic waste solutes and pro-inflammatory signaling molecules which may affect the progression of the GBM.

A universal tool for stability predictions of biotherapeutics, vaccines and in vitro diagnostic products

It is of particular interest for biopharmaceutical companies developing and distributing fragile biomolecules to warrant the stability and activity of their products during long-term storage and shipment. In accordance with quality by design principles, advanced kinetic modeling (AKM) has been successfully used to predict long-term product shelf-life and relies on data from short-term accelerated stability studies that are used to generate Arrhenius-based kinetic models that can, in turn, be exploited for stability forecasts. The AKM methodology was evaluated through a cross-company perspective on stability modeling for key stability indicating attributes of different types of biotherapeutics, vaccines and biomolecules combined in in vitro diagnostic kits. It is demonstrated that stability predictions up to 3 years for products maintained under recommended storage conditions (2–8 °C) or for products that have experienced temperature excursions outside the cold-chain show excellent agreement with experimental real-time data, thus confirming AKM as a universal and reliable tool for stability predictions for a wide range of product types.

Rapid removal of PFOA and PFOS via modified industrial solid waste: Mechanisms and influences of water matrices.

Emerging perfluoroalkyl and polyfluoroalkyl substances contaminate waters at trace concentrations, thus rapid and selective adsorbents are pivotal to mitigate the consequent energy-intensive and time-consuming issues in remediation. In this study, coal combustion residuals-fly ash was modified (FA-SCA) to overcome the universal trade-off between high adsorption capacity and fast kinetics. FA-SCA presented rapid adsorption (teq = 2 min) of PFOX (perfluorooctanoic acid and perfluorooctanesulfonic acid, collectively), where the dynamic adsorption capacity (qdyn = qm/teq) was 2-3 orders of magnitude higher than that of benchmark activated carbons and anion-exchange resins. Investigated by advanced characterization and kinetic models, the fast kinetics and superior qdyn are attributed to (1) elevated external diffusion driven by the submicron particle size; (2) enhanced intraparticle diffusion caused by the developed mesoporous structure (Vmeso/Vmicro = 8.1); (3) numerous quaternary ammonium anion-exchange sites (840 μmol/g), and (4) appropriate adsorption affinity (0.031 L/μmol for PFOS, and 0.023 L/μmol for PFOA). Since the adsorption was proven to be a synergistic process of electrostatic and hydrophobic interactions, effective adsorption ([PFOX]ini = 1.21 μM, concentration levels of highly-contaminant-sites) was obtained at conventional natural water chemistries. High selectivity (>85.4% removal) was also achieved with organic/inorganic competitors, especially compounds with partly similar molecular structures to PFOX. In addition, >90% PFOX was removed consistently during five cycles in mild regeneration conditions (pH 12 and 50 °C). Overall, FA-SCA showed no leaching issues of toxic metals and exhibits great potential in both single-adsorption processes and treatment train systems.

Aging-Related Alterations of Glymphatic Transport in Rat: In vivo Magnetic Resonance Imaging and Kinetic Study.

ObjectiveImpaired glymphatic waste clearance function during brain aging leads to the accumulation of metabolic waste and neurotoxic proteins (e.g., amyloid-β, tau) which contribute to neurological disorders. However, how the age-related glymphatic dysfunction exerts its effects on different cerebral regions and affects brain waste clearance remain unclear.MethodsWe investigated alterations of glymphatic transport in the aged rat brain using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and advanced kinetic modeling. Healthy young (3–4 months) and aged (18–20 months) male rats (n = 12/group) underwent the identical MRI protocol, including T2-weighted imaging and 3D T1-weighted imaging with intracisternal administration of contrast agent (Gd-DTPA). Model-derived parameters of infusion rate and clearance rate, characterizing the kinetics of cerebrospinal fluid (CSF) tracer transport via the glymphatic system, were evaluated in multiple representative brain regions. Changes in the CSF-filled cerebral ventricles were measured using contrast-induced time signal curves (TSCs) in conjunction with structural imaging.ResultsCompared to the young brain, an overall impairment of glymphatic transport function was detected in the aged brain, evidenced by the decrease in both infusion and clearance rates throughout the brain. Enlarged ventricles in parallel with reduced efficiency in CSF transport through the ventricular regions were present in the aged brain. While the age-related glymphatic dysfunction was widespread, our kinetic quantification demonstrated that its impact differed considerably among cerebral regions with the most severe effect found in olfactory bulb, indicating the heterogeneous and regional preferential alterations of glymphatic function.ConclusionThe robust suppression of glymphatic activity in the olfactory bulb, which serves as one of major efflux routes for brain waste clearance, may underlie, in part, age-related neurodegenerative diseases associated with neurotoxic substance accumulation. Our data provide new insight into the cerebral regional vulnerability to brain functional change with aging.

Long-Term Stability Prediction for Developability Assessment of Biopharmaceutics Using Advanced Kinetic Modeling.

A crucial aspect of pharmaceutical development is the demonstration of long-term stability of the drug product. Biopharmaceuticals, such as proteins or peptides in liquid formulation, are typically administered via parental routes and should be stable over the shelf life, which generally includes a storing period (e.g., two years at 5 °C) and optionally an in-use period (e.g., 28 days at 30 °C). Herein, we present a case study where chemical degradation of SAR441255, a therapeutic peptide, in different formulations in combination with primary packaging materials was analyzed under accelerated conditions to derive long-term stability predictions for the recommended storing conditions (two years at 5 °C plus 28 days at 30 °C) using advanced kinetic modeling. These predictions served as a crucial decision parameter for the entry into clinical development. Comparison with analytical data measured under long-term conditions during the subsequent development phase demonstrated a high prediction accuracy. These predictions provided stability insights within weeks that would otherwise take years using measurements under long-term stability conditions only. To our knowledge, such in silico studies on stability predictions of a therapeutic peptide using accelerated chemical degradation data and advanced kinetic modeling with comparisons to subsequently measured real-life long-term stability data have not been described in literature before.

Autocatalytic decomposition of energetic materials: interplay of theory and thermal analysis in the study of 5-amino-3,4-dinitropyrazole thermolysis.

A reliable kinetic description of the thermal stability of energetic materials (EM) is very important for safety and storage-related problems. Among other pertinent issues, autocatalysis very often complicates the decomposition kinetics of EM. In the present study, the kinetics and decomposition mechanism of a promising energetic compound, 5-amino-3,4-dinitro-1H-pyrazole (5-ADP) were studied using a set of complementary experimental (e.g., differential scanning calorimetry in the solid state, melt, and solution along with advanced thermokinetic models, accelerating rate calorimetry, and evolved gas analysis) and theoretical techniques (CCSD(T)-F12 and DLPNO-CCSD(T) predictive quantum chemical calculations). The experimental study revealed that the strong acceleration of the decomposition rate of 5-ADP is caused by two factors: the progressive liquefaction of the sample directly observed using in situ optical microscopy, and the autocatalysis by reaction products. For the first time, the processing of the non-isothermal data was performed with a formal Manelis-Dubovitsky kinetic model that accounts for both factors. With the aid of quantum chemical calculations, we have rationalized the autocatalysis present in the formal kinetic models at the molecular level. Theory revealed an unusual primary decomposition channel of 5-ADP, viz., the two subsequent sigmatropic H-shifts in the pyrazole ring followed by the C-NO2 bond scission yielding a pyrazolyl and nitrogen dioxide radicals as simple primary products. Moreover, we found the secondary reactions of the latter radical with the 5-ADP to be kinetically unimportant. On the contrary, the substituted pyrazolyl radical turned out to undergo a facile addition to 5-ADP, followed by a fast exothermic elimination of another ˙NO2 species. We believe the latter process to contribute remarkably to the observed autocatalytic behavior of 5-ADP. Most importantly, the calculations provide detailed mechanistic evidence complementing the thermoanalytical experiment and formal kinetic models.

Advanced Kinetic Modeling of Bio-co-polymer Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) Production Using Fructose and Propionate as Carbon Sources

Biopolymers are a promising alternative to petroleum-based plastic raw materials. They are bio-based, non-toxic and degradable under environmental conditions. In addition to the homopolymer poly(3-hydroxybutyrate) (PHB), there are a number of co-polymers that have a broad range of applications and are easier to process in comparison to PHB. The most prominent representative from this group of bio-copolymers is poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV). In this article, we show a new kinetic model that describes the PHBV production from fructose and propionic acid in Cupriavidus necator (C. necator). The developed model is used to analyze the effects of process parameter variations such as the CO2 amount in the exhaust gas and the feed rate. The presented model is a valuable tool to improve the microbial PHBV production process. Due to the coupling of CO2 online measurements in the exhaust gas to the biomass production, the model has the potential to predict the composition and the current yield of PHBV in the ongoing process.

Molecular Reconstruction of Hydrocarbons and Sulfur-Containing Compounds in Atmospheric and Vacuum Gas Oils

The inherent complexity of petroleum fractions makes molecular reconstruction an essential element to make use of advanced kinetic models in the petrochemical industry, in particular when sulfur co...

Kinetic modeling of microbial growth, enzyme activity, and gene deletions: An integrated model of β-glucosidase function in Cellvibrio japonicus.

Understanding the complex growth and metabolic dynamics in microorganisms requires advanced kinetic models containing both metabolic reactions and enzymatic regulation to predict phenotypic behaviors under different conditions and perturbations. Most current kinetic models lack gene expression dynamics and are separately calibrated to distinct media, which consequently makes them unable to account for genetic perturbations or multiple substrates. This challenge limits our ability to gain a comprehensive understanding of microbial processes towards advanced metabolic optimizations that are desired for many biotechnology applications. Here, we present an integrated computational and experimental approach for the development and optimization of mechanistic kinetic models for microbial growth and metabolic and enzymatic dynamics. Our approach integrates growth dynamics, gene expression, protein secretion, and gene-deletion phenotypes. We applied this methodology to build a dynamic model of the growth kinetics in batch culture of the bacterium Cellvibrio japonicus grown using either cellobiose or glucose media. The model parameters were inferred from an experimental data set using an evolutionary computation method. The resulting model was able to explain the growth dynamics of C. japonicus using either cellobiose or glucose media and was also able to accurately predict the metabolite concentrations in the wild-type strain as well as in β-glucosidase gene deletion mutant strains. We validated the model by correctly predicting the non-diauxic growth and metabolite consumptions of the wild-type strain in a mixed medium containing both cellobiose and glucose, made further predictions of mutant strains growth phenotypes when using cellobiose and glucose media, and demonstrated the utility of the model for designing industrially-useful strains. Importantly, the model is able to explain the role of the different β-glucosidases and their behavior under genetic perturbations. This integrated approach can be extended to other metabolic pathways to produce mechanistic models for the comprehensive understanding of enzymatic functions in multiple substrates.

High Temporal-Resolution Dynamic PET Image Reconstruction Using a New Spatiotemporal Kernel Method

Current clinical dynamic PET has an effective temporal resolution of 5-10 seconds, which can be adequate for traditional compartmental modeling but is inadequate for exploiting the benefit of more advanced tracer kinetic modeling for characterization of diseases (e.g., cancer and heart disease). There is a need to improve dynamic PET to allow fine temporal sampling of 1-2 seconds. However, the reconstruction of these short-time frames from tomographic data is extremely challenging as the count level of each frame is very low and high noise presents in both spatial and temporal domains. Previously, the kernel framework has been developed and demonstrated as a statistically efficient approach to utilizing image prior for low-count PET image reconstruction. Nevertheless, the existing kernel methods mainly explore spatial correlations in the data and only have a limited ability in suppressing temporal noise. In this paper, we propose a new kernel method which extends the previous spatial kernel method to the general spatiotemporal domain. The new kernelized model encodes both spatial and temporal correlations obtained from image prior information and are incorporated into the PET forward projection model to improve themaximumlikelihood(ML) image reconstruction. Computer simulations and an application to real patient scan have shown that the proposed approach can achieve effective noise reduction in both spatial and temporal domains and outperform the spatial kernel method and conventional ML reconstruction method for improving the high temporal-resolution dynamic PET imaging.

Optical actinometry of O-atoms in pulsed nanosecond capillary discharge: peculiarities of kinetics at high specific deposited energy

The density of O-atoms was studied in a nanosecond capillary discharge in air with 5.3% additions of Ar at 28.5 mbar. Time-resolved electrical current, longitudinal electric field, optical emission of O(3p3P) at λO = 844.6 nm, Ar(2p1) λAr = 750.4 nm and their ratio, and emission of N2(C3Πu) at were measured. A kinetic scheme describing consistent behavior of the set of the experimental data was developed. The main processes responsible for population and decay of the species of interest were selected on the basis of sensitivity and rate analysis. The electric field was taken as input data; all other experimentally obtained signals were modeled; experimental data and results of calculations were in good agreement. The role of the reactions between excited, charged species and electrons in the early afterglow for pulsed discharges at high reduced electric fields and high specific deposited energy was discussed. The density of O-atoms in the ground state was calculated. It was concluded that Ar-based traditional actinometry demands advanced kinetic modeling with regards to the nanosecond discharge with a high specific energy deposition and high reduced electric field.

Analytical and advanced kinetic models for characterization of chain-growth copolymerization: the state-of-the-art

A detailed overview is given on the currently developed analytical and advanced kinetic models to calculate the main bulk/solution chain-growth copolymerization characteristics.

Sub-nanosecond resolution electric field measurements during ns pulse breakdown in ambient air

Electric field during ns pulse discharge breakdown in ambient air has been measured by ps four-wave mixing, with temporal resolution of 0.2 ns. The measurements have been performed in a diffuse plasma generated in a dielectric barrier discharge, in plane-to-plane geometry. Absolute calibration of the electric field in the plasma is provided by the Laplacian field measured before breakdown. Sub-nanosecond time resolution is obtained by using a 150 ps duration laser pulse, as well as by monitoring the timing of individual laser shots relative to the voltage pulse, and post-processing four-wave mixing signal waveforms saved for each laser shot, placing them in the appropriate ‘time bins’. The experimental data are compared with the analytic solution for time-resolved electric field in the plasma during pulse breakdown, showing good agreement on ns time scale. Qualitative interpretation of the data illustrates the effects of charge separation, charge accumulation/neutralization on the dielectric surfaces, electron attachment, and secondary breakdown. Comparison of the present data with more advanced kinetic modeling is expected to provide additional quantitative insight into air plasma kinetics on ~ 0.1–100 ns scales.

Advanced kinetic models through mechanistic understanding: Population balances for methylenedianiline synthesis

The recent development of amorphous silica-alumina catalysts for the heterogeneously-catalyzed production of methylenedianiline (MDA) has resulted in an economically and environmentally attractive alternative to the industrially employed HCl catalyst. However, the quantitative kinetic description of the complex reaction network, a key prerequisite for reactor choice and process assessment, is unfeasible when using classical power-law based models due to the wealth of components and interactions involved. Based on high throughput batch experiments coupled with a detailed product analysis, we herein reveal that all possible reactions in an MDA mixture can be represented in terms of interactions between a small number of reoccurring functional groups. A corresponding categorization of all mixture components enables the description of the reaction network using population balance equations, an approach previously applied to polymerization, aggregation, and cell-growth processes. Thereby, every possible binary interaction between two molecules in the system can be accounted for, resulting in an unprecedented resolution regarding oligomeric species and improved predictive capabilities based on a small number of parameters. The translation of the deepened mechanistic understanding into advanced kinetic models enables to circumvent previous limitations, and is expected to accelerate the industrial realization of the sustainable process.

A Review of Kinetic Modeling Methodologies for Complex Processes

In this paper, kinetic modeling techniques for complex chemical processes are reviewed. After a brief historical overview of chemical kinetics, an overview is given of the theoretical background of kinetic modeling of elementary steps and of multistep reactions. Classic lumping techniques are introduced and analyzed. Two examples of lumped kinetic models (atmospheric gasoil hydrotreating and residue hydroprocessing) developed at IFP Energies nouvelles (IFPEN ) are presented. The largest part of this review describes advanced kinetic modeling strategies, in which the molecular detail is retained, i.e. the reactions are represented between molecules or even subdivided into elementary steps. To be able to retain this molecular level throughout the kinetic model and the reactor simulations, several hurdles have to be cleared first: (i) the feedstock needs to be described in terms of molecules, (ii) large reaction networks need to be automatically generated, and (iii) a large number of rate equations with their rate parameters need to be derived. For these three obstacles, molecular reconstruction techniques, deterministic or stochastic network generation programs, and single-event micro-kinetics and/or linear free energy relationships have been applied at IFPEN , as illustrated by several examples of kinetic models for industrial refining processes.

Development of a Nonequilibrium Finite-Rate Ablation Model for Radiating Earth Reentry Flows

Thermal protection system design for atmospheric reentry vehicles remains a challenging and complex problem. Recent advances in computational modeling of air–carbon interactions consider competing finite-rate reactions on a limited number of available surface sites. One of the most advanced kinetic models is due to Zhluktov and Abe. However, the Zhluktov and Abe model only describes the oxidation and sublimation of carbon and has no nitridation mechanism. The following study develops several modifications to the Zhluktov and Abe air–carbon model that account for all three reaction mechanisms with the goal of improving cyanogen shock-layer radiation predictions to recent experimental results. First, the study examines two possible paths for carbon nitridation and then assesses the augmented surface reaction model in a representative blunt-body reentry flow. Second, a sensitivity analysis is performed to determine which surface reactions have the most impact on altering cyanogen radiance predictions. It was found that modifications to the oxidation mechanisms significantly improved the comparisons and the nitridation mechanism had a minor effect. Specifically, it was determined that carbon monoxide must be the principal oxidation product at high surface temperatures instead of carbon dioxide, as the Zhluktov and Abe model had originally predicted. More detailed measurements of both the carbon oxidation and nitridation reactions at higher surface temperatures are required to further validate and improve the rate parameters derived in this study.