Micro-kinetic modeling of temporal analysis of products data using kinetics-informed neural networks
Kinetics-informed neural networks improve fit quality for multi-pulse and noisy temporal analysis of products datasets.
Nai, Dingqi↗
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Plasma-assisted catalysis for ammonia synthesis in a dielectric barrier discharge reactor: key surface reaction steps and potential causes of low energy yield
Ammonia synthesis experiments were carried out in a coaxial dielectric barrier discharge (DBD) reactor packed with several different supports and metal catalysts. There was a marked increase in the reaction rate, over that obtained in an empty DBD plasma reactor, upon introduction of a packed bed of γ-Al 2 O 3 , Ru/γ-Al 2 O 3 , or SiO 2 particles. The difference in the reaction rates over γ-Al 2 O 3 and Ru/γ-Al 2 O 3 was minimal. Complementary zero-dimensional plasma kinetic model analysis was also performed using inputs from experimental data. This kinetic analysis allowed for gas phase reactions, Eley–Rideal (E–R) reactions, and direct adsorption of radical species on the γ-Al 2 O 3 surface. On the metal surface, dissociative adsorption of N 2 and H 2 , and Langmuir–Hinshelwood reactions were also included. This analysis revealed that, under the conditions of our experiments, ammonia synthesis proceeds principally by the formation of reactive radicals in the gas phase, which then adsorb and participate in E–R reactions on both the metal and support material surfaces. Furthermore, this finding illustrates a challenge for substantially increasing the energy yield for plasma-assisted ammonia synthesis in typical DBD reactors containing packed catalyst beads.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Kinetic Measurements in Heterogeneous Catalysis
This contribution is about the experimental determination of the rate of a heterogeneous catalytic reaction and the analysis of kinetic data. In this case, the reaction rate can be defined as the frequency at which the closed sequence of elementary steps transforming reactants into products is occurring. However, the rate of chemical reaction is not directly observed; rather, one records the rate of substance change. The rate of chemical reaction is calculated based on the rate of substance change and assumed stoichiometry of the reaction. In order to make this an intensive quantity (i.e., independent of the volume in which the reaction is performed, this frequency is divided by the latter or by a quantity proportional to the latter). As the focus of what follows is on heterogeneously catalyzed reactions this can be the catalyst mass, the catalyst surface area or the total number of active sites present in the reaction volume confined by the walls of a chemical reactor. For example, the rate may be reported as one of the following: Net rate of consumption/production of component i mol m-3 s-1 Net specific rate of consumption/production of component i mol kg-1 s-1 Rate of substance change (per unit volume of catalyst) mol m-3 s-1 Specific reaction rate (per unit mass of catalyst) mol kg-1 s-1 Net rate of consumption/production of component i mol m-2 s-1 The turnover frequency (TOF) is a characteristic originally introduced by Boudart [1] is expressed as TOF=R/G_tot , where R is the steady-state rate of reactant consumption or product generation and G_totis the total areal density of active sites [mol m-2]. The latter is typically taken from experimental chemisorption data obtained at low temperature. The advancement of a chemical reaction leads to changes in the amounts of reactants and products which, for a stoichiometric single reaction, are connected by the stoichiometric coefficients. In many cases, for the sake of simplicity, we will assume that the investigated reaction proceeds according to a single reaction pathway, so that the net production rate of a reaction component is directly proportional to the reaction rate and that the latter equals the net production rate of any of the involved components when divided by the appropriate stoichiometric coefficient. For complex stoichiometric reactions the net production rates of the involved components are linear combinations of the reaction rates. Different goals for kinetic measurements can be formulated: Catalyst testing, i.e., obtaining the kinetic dependences for the development and discrimination of efficient catalytic materials. Precise kinetic characterization of active materials via high-throughput screening is a part of such activity. Detailed kinetics, i.e., revealing the detailed mechanism of complex catalytic reaction via systematic kinetic studies, both steady-state and non-steady-state Industrial kinetics, i.e., obtaining data and relationships for describing and predicting the behaviour of catalytic reactors and processes at industrial scale Finally, mathematical modeling and analysis where kinetic measurements provide data for understanding complex kinetic phenomena, e.g., oscillations, non-linear self-organization, etc.
36 MATERIALS SCIENCE↗
A Markov chain Monte Carlo (MCMC) Bayesian inference approach to analyze apparent activation barriers and reaction orders from microreactor data
Statistical analysis of steady-state catalytic kinetic data is often limited by data sparsity due to the slow pace at which the data is collected. Data sparsity and limitations in statistical analysis make it difficult to differentiate between mechanistic models and catalytic sites. A Bayesian inference tool is reported for catalysis researchers to estimate error in the determination of reaction orders from steady state microreactor data. The benefits of a Bayesian inference approach are discussed, as an alternative to the more common frequentist approach. The approach incorporates prior knowledge of the system and the data collected to form an error estimate on reaction orders. We investigated the effects of three distinct data treatments—individual fitting of trials, pooled analysis, and constrained regression methods—on the precision and uncertainty of reaction order determinations. To assess the robustness of our findings, we conducted sensitivity analyses to evaluate the influence of Bayesian parameters on uncertainty estimation. Additionally, we utilized synthetic data to illustrate how data quality impacts the precision of uncertainty assessments. We show Bayesian analysis can obtain a more precise estimation of error with a sparse data set than a frequentist analysis. Finally, this work provides strong evidence that the adoption of Bayesian analysis of kinetic data may help researchers make more precise arguments as to the strength of their evidence for a particular mechanistic hypothesis, or in comparing across different catalysts.
42 ENGINEERING↗
Experimental and Theoretical Thermokinetic Analysis of the Na 3 VO 4 –CO 2 Reaction System under Different Physicochemical Conditions
Sodium vanadate (Na 3 VO 4 ) was synthesized, structural and microstructurally characterized as well as tested as possible carbon dioxide (CO 2 ) captor through thermodynamic calculations in addition to thermogravimetric dynamic and isothermal analyses. Ceramic structural characterization evidenced the formation of the Na 3 VO 4 crystal phase, while microstructural features evidenced dense agglomerates with a poor specific surface area, although synthesis temperature (600 °C) was not as high as that for other sodium ceramics. Na 3 VO 4 was investigated for the CO 2 capture process through dynamic and isothermal experiments, in the presence or absence of oxygen. All these experiments showed that Na 3 VO 4 possesses interesting CO 2 capture properties in a specific temperature range (520 and 600 °C). Isothermal products characterization allowed to elucidate the reaction pathway, implying the Na 4 V 2 O 7 formation, as possible reactive intermediate. In fact, reaction path evolution was supported with theoretical thermodynamic data. Moreover, the kinetic analysis, performed using the Avrami-Erofeev mathematical model and subsequent thermodynamic data obtention, probed the positive influence of the temperature, in both CO 2 superficial and bulk chemical sorption processes, while oxygen addition did not enhance the superficial process. Additionally, as it would be expected, kinetics were enhanced as a function of the CO 2 concentration.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Free Energy Dependencies for Interfacial Electron Transfer from Tin-Doped Indium Oxide (ITO) to Molecular Photoredox Catalysts
John B. Goodenough proposed that interfacial electron transfer kinetics from main group metal oxides should be fundamentally different from that of transition metal oxides, an expectation that has not been widely tested. Herein, the kinetics for interfacial electron transfer from mesoporous transparent conductive oxide Tin-doped Indium Oxide (ITO) to four photoredox catalysts (PCs) were characterized in acetonitrile electrolytes. The photocatalysts had the form: [Ru(4,4ʹ-R 2 -2,2′-bipyridine) 2 (4,4ʹ-(PO 3 H 2 ) 2 -2,2′-bipyridine)] 2+ , where R was H, methoxy, tert -butyl, and Br. The impact of the surface binding group was characterized with [Ru(2,2′-bipyridine) 2 (4,4ʹ-(CO 2 H) 2 -bpy)] 2+ . The interfacial electron transfer reaction ITO(e − )∣PC + → ITO∣PC was quantified by nanosecond absorption spectroscopy as a function of the applied potential (and hence ‒Δ G °). Specific conditions of applied potential were identified where the kinetics were sensitive to the incident irradiance. A layer-by-layer method was used to insert ionic methylene bridge(s) between the PC and the oxide surface. Marcus-Gerischer analysis of the kinetic data indicates non-adiabatic interfacial electron transfer with total reorganization energies that increase when bridges were placed between the photocatalyst and the ITO surface.
Bangle, Rachel E.↗
Data for “Tree root nutrient uptake kinetics vary with nutrient availability, environmental conditions, and root traits: A global analysis”
This data package contains data and code used in the paper “Tree root nutrient uptake kinetics vary with nutrient availability, environmental conditions, and root traits: A global analysis”. The central product is a global dataset of root inorganic nutrient uptake rates and kinetics parameters covering temperate, boreal, and sub/tropical tree species, representing a collection of nutrient uptake data from published studies. This dataset enables tree investigation of root nutrient uptake rates across species, space, and experimental conditions. The data can also be combined with supplementary data on root and soil traits or with external datasets (e.g. R scripts contained within use data from FRED 3.0; (Iversen et al., 2021)). Contained within is the main nutrient data “uptake_data.csv” as well as 4 additional .csv files that link uptake data to supplementary measurements, source references, taxonomic information, and additional nutrient uptake measurements across nutrient gradients, and 1 .csv file that records meta-analysis results for plotting with the R scripts. There are seven R scripts that support data analysis and creation of the figures in the related publication.
54 ENVIRONMENTAL SCIENCES↗
Reorganization Energies for Interfacial Proton-Coupled Electron Transfer to a Water Oxidation Catalyst
The reorganization energy (λ) for interfacial electron transfer (ET) and proton-coupled electron transfer (PCET) from a conductive metal oxide (In 2 O 3 :Sn, ITO) to a surface-bound water oxidation catalyst were extracted from kinetic data measured as a function of the thermodynamic driving force. Visible light excitation resulted in rapid excited-state injection (k inj > 10 8 s -1 ) to the ITO, which photo-initiated the two interfacial reactions of interest. The rate constants for both reactions increased with the driving force, −ΔG°, to a saturating limit, k max , with rate constants consistently larger for ET than for PCET. Marcus-Gerischer analysis of the kinetic data provided the reorganization energy for interfacial PCET (0.90 ± 0.02 eV) and ET (0.40 ± 0.02 eV), respectively. The magnitude of k max for PCET was found to decrease with pH, behavior that was absent for ET. Both the decrease in k max and the larger reorganization energy for an unwanted competing PCET reaction from the ITO to the oxidized catalyst showcases a significant kinetic advantage for driving solar water oxidation at high pH. Computational analysis revealed a larger inner-sphere reorganization energy contribution for PCET than for ET arising from a more significant change in the Ru–O bond length for PCET. Extending Marcus-Gerischer theory to PCET by including the excited electron-proton vibronic states and the proton donor-acceptor motion provided an apparent reorganization energy of 1.01 eV. Furthermore, this study demonstrates that the Marcus-Gerischer theory initially developed for ET can be reliably extended to PCET for quantifying and interpreting reorganization energies observed experimentally.
Catalysts Kinetic parameters↗
Internal calibration of transient kinetic data via machine learning
The temporal analysis of products (TAP) reactor provides a vast amount of transient kinetic information that may be used to describe a variety of chemical features including residence time distributions, kinetic coefficients, number of active sites, reaction mechanism, etc. However, as with any measurement device, the TAP reactor signal is convoluted with noise and drift is common. In order to reduce the uncertainty of the kinetic measurement and any derived parameters or mechanisms, proper preprocessing must be performed prior to any advanced type of analysis. This preprocessing includes baseline correction, i.e., a shift in the voltage response, and calibration, i.e., a scaling of the flux response based on prior experiments. The traditional methodology of preprocessing requires significant user discretion and reliance on separate calibration experiments that may drift over time. Herein we use machine learning techniques combined with physical constraints to understand the noise and drift that is being generated within and between experiments for enhancement of the chemical kinetic signal. As such, the proposed methodology demonstrates clear benefits over the traditional preprocessing approach by eliminating the need for separate calibration experiments or heuristic input from the user.
36 MATERIALS SCIENCE↗
Hydrothermal corrosion behavior of CVD SiC in high temperature water
Here, the hydrothermal corrosion of polished and as-cut high purity chemical vapor deposited (CVD) SiC was studied in a constantly refreshing water loop. Light water reactor (LWR) conditions were simulated at 288, 320, and 350 °C with dissolved gas concentrations between 0.15 and 3 ppm H 2 or between 1 and 4 ppm O 2 . In hydrogenated water, the rate of material loss was low, calculated to be ~1.3 μm of recession after 5 years of service in 320 °C water. Moreover, there was no observed localized attack at any temperature. In oxygenated conditions, the corrosion rate was higher, with a calculated material loss >10 μm after 5 years of service in 1 ppm O 2 , 320 °C water. Mass loss significantly increased when grain fallout became significant (as early as 200h with 4 ppm O 2 at 350 °C or after 1000–2000h with 2 ppm O 2 at 288 °C). Grain fallout more than doubled the corrosion rate and a steady state corrosion rate in the grain fallout regime was not observed but expected to eventually occur once large grains begin to be removed. Polished specimens had lower mass loss than unpolished coupons. A kinetic analysis of the data in this work suggests that the corrosion rates are controlled by a single activation step in both oxygenated and deoxygenated conditions, with the reaction order with respect to oxygen being 1. A resulting reaction rate equation to predict corrosion of SiC (in mg/cm 2 s) in high purity water from 288 to 350 °C and up to 4 ppm O 2 was constructed: Rate = $\frac{0.1458}{1+SA}$ T (1.09(1–10 -3 T)[O 2 ]e– $\frac{1.275x10^4}{T}$ + 7.91x10 -6 e– $\frac{7.39x10^3}{T}$).
36 MATERIALS SCIENCE↗
Different Kinetic Reactivities of Electrons in Distinct TiO 2 Nanoparticle Trap States
Electrons added to TiO 2 and other semiconductors often occupy trap states, whose reactivity can determine the catalytic and stoichiometric chemistry of the material. We previously showed that reduced aqueous colloidal TiO 2 nanoparticles have two distinct classes of thermally equilibrated trapped electrons, termed Red/e – and Blue/e – . Presented here are parallel optical and electron paramagnetic resonance (EPR) kinetic studies of the reactivity of these electrons with solution-based oxidants. Optical stopped-flow measurements monitoring the reactions of TiO 2 /e – with substoichiometric oxidants showed a surprising pattern: an initial fast (seconds) decrease in TiO 2 /e – absorbance followed by a secondary, slow (minutes) increase in the broad TiO 2 /e – optical feature. The analysis revealed that the fast decrease is due to the preferential oxidation of the Red/e – trap states and the slow increase results from the re-equilibration of electrons from Blue/e - to Red/e - states. This kinetic model was confirmed by freeze-quench EPR measurements. Quantitative analysis of the kinetic data demonstrated that Red/e – react ~5 times faster than Blue/e – with the nitroxyl radical oxidant 4-methoxy-2,2,6,6-tetramethyl-1-piperidinyloxyl (4-MeO-TEMPO). Similar reactivity patterns were also observed in oxidations of TiO 2 /e – by O 2 , which like 4-MeO-TEMPO is a proton-coupled electron transfer (PCET) oxidant, and by the pure electron transfer (ET) oxidant potassium triiodide (KI 3 ). Furthermore, this suggests that the faster intrinsic reactivity of one trap state over another on the seconds–minutes time scale is likely a general feature of reduced TiO 2 reactivity. This differential trap-state reactivity is likely to influence the performance of TiO 2 in photochemical/electrochemical devices, and it suggests an opportunity for tuning catalysis.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Catalytic reforming of methane with H 2 S via dynamically stabilized sulfur on transition metal oxides and sulfides
Reforming of methane with H 2 S is a promising path to directly utilize sour natural gas reserves, although some aspects of the mechanism and structure–function relations remain elusive. In this report we show that metal oxides of group 4–6 elements, which are inert for steam and dry methane reforming reactions, are active and stable (pre)catalysts for the H 2 S reforming of methane. The key active sites are sulfur species (S*) that are dynamically bound to metal cations during catalysis. Similar H/D isotope exchange patterns and universal rate inhibition by H 2 on representative catalysts indicate that H 2 S decomposition and recombination of surface hydrogen atoms are quasi-equilibrated, whereas CH 4 dissociation steps are reversible, yet not quasi-equilibrated. An in-depth analysis of the kinetic data and isotopic substitution effects identifies S*-mediated C–H bond cleavage as the most plausible rate-limiting step common for all catalysts, with subtle yet essential differences between 3d and 4d/5d catalysts in the thermodynamic stability of S*.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Data of kinetic equilibrium reconstruction and transport and stability analysis of NSTX and NSTX-U plasmas
Explore the source record for details and available documents.
NSTX↗
Data Assimilation using Non-invasive Monte Carlo Sensitivity Analysis of Reactor Kinetics Parameters [Slides]
Sensitivity coefficients of prompt neutron decay constant (α) and effective delayed neutron fraction (β eff ) to Pu-239 nuclear data were calculated using a newly available tool (ACEtk). Uncertainty calculations showed the following trends: (1) The prompt neutron decay constant can be used to reduce nuclear data-induced uncertainty in Pu-239(n,f) and (2) the effective delayed neutron fraction can be used to effectively reduce nuclear data-induced uncertainty in Pu-239 total fission $\tilde{v}$. Sensitivity/uncertainty analysis can be used to determine optimal experiment/detector setup to identify compensating errors in nuclear data. Upcoming work includes investigating other nuclide-reaction pairs and response sensitivities, such as Δ$\rho$, neutron leakage spectra, and reaction rate measurements to constrain nuclear data of interest.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Doppler Backscattering Data Analysis and Integrated Modeling with OMFIT
One Modeling Framework for Integrated Tasks (OMFIT) is a widely used software tool in the magnetic fusion research community. OMFIT provides magnetic fusion energy researchers with a framework for the development of special-purpose physics modules. This paper describes an OMFIT physics module pertaining to the Doppler Backscattering (DBS) fusion plasma diagnostic. DBS measures density fluctuations and flow velocity through plasma scattering of electromagnetic waves. The OMFIT DBS module was developed to analyze experimental DBS data and facilitate modeling of DBS systems installed on multiple tokamak devices. The OMFIT DBS module is designed to support several analysis workflows: detailed analysis of experimental data, experimental planning, and theory-based synthetic diagnostic modeling. The DBS module uses integrated modeling by leveraging other OMFIT physics modules to perform tasks related to DBS, e.g. ray/beam–tracing simulations, edge-localized mode–synchronized data analysis, magnetic equilibrium reconstruction, and fitting kinetic profile data. Furthermore, this paper describes several supported workflows and serves a reference for the OMFIT DBS module.
Doppler backscattering↗
Lifetime of actin-dependent protein nanoclusters
Protein nanoclusters (PNCs) are dynamic collections of a few proteins that spatially organize in nanometer-length clusters. PNCs are one of the principal forms of spatial organization of membrane proteins, and they have been shown or hypothesized to be important in various cellular processes, including cell signaling. PNCs show remarkable diversity in size, shape, and lifetime. In particular, the lifetime of PNCs can vary over a wide range of timescales. The diversity in size and shape can be explained by the interaction of the clustering proteins with the actin cytoskeleton or the lipid membrane, but very little is known about the processes that determine the lifetime of the nanoclusters. In this paper, using mathematical modeling of the cluster dynamics, we model the biophysical processes that determine the lifetime of actin-dependent PNCs. In particular, we investigated the role of actin aster fragmentation, which had been suggested to be a key determinant of the PNC lifetime, and we found that it is important only for a small class of PNCs. A simple extension of our model allowed us to investigate the kinetics of protein-ligand interaction near PNCs. We found an anomalous increase in the lifetime of ligands near PNCs, which agrees remarkably well with experimental data on RAS-RAF kinetics. In particular, analysis of the RAS-RAF data through our model provides falsifiable predictions and novel hypotheses that will not only shed light on the role of RAS-RAF kinetics in various cancers, but also will be useful in studying membrane protein clustering in general.
59 BASIC BIOLOGICAL SCIENCES↗
Intrinsic Kinetics of Polyethylene Terephthalate Pyrolysis via Micropyrolysis and Multivariate Chromatographic Analysis
This study provides an in-depth investigation of the primary decomposition of polyethylene terephthalate (PET) via pyrolysis, employing an experimental-analytic workflow that integrates design of experiments (DoE), micropyrolysis coupled with comprehensive two-dimensional gas chromatography (GC×GC), and multivariate data analysis to verify intrinsic kinetic conditions and elucidate evolving product distributions for mapping key reaction pathways. Peaks that could not be identified using commercial spectral libraries were assigned using Mass Frontier simulations, enabling the identification of divinyl terephthalate, ethyl vinyl terephthalate, and 2-(benzoyloxy)ethyl vinyl terephthalate. A polar×polar (non-orthogonal) column set tailored for the detection of carboxylic acids enhanced the quantification of benzoic acid, 4-vinylbenzoic acid, 4-ethylbenzoic acid, and methylbenzoic acid by up to 6-fold relative to an orthogonal column combination (non-polar×mid-polar). Moreover, pyrolysis variables were systematically evaluated using a Box- Behnken design (BBD), encompassing pyrolysis temperature (500−600 °C), sample weight (50−150 μg), and carrier gas flow rate (100−300 mL min −1 ). Among these, pyrolysis temperature was the only statistically significant factor influencing product yields, ranging from 58.78 to 84.26 wt %. In contrast, neither the sample weight nor the carrier gas flow rate had a significant effect on product yields within the evaluated experimental space. At 600 °C, the major pyrolysis products were benzoic acid (up to 20.20 ± 1.46 wt %) and CO 2 (up to 21.28 ± 1.46 wt %), which can be produced through decarboxylation reactions. These findings underscore the critical importance of selecting appropriate analytical columns for the accurate quantification of heteroatomcontaining products such as carboxylic acids, which may otherwise be underestimated or undetected due to their reactivity with the stationary phase of non-polar and mid-polar columns, as well as other GC components. They also highlight the importance of selecting pyrolysis conditions for investigating the primary decomposition of PET under an isothermal kinetically limited regime.
aromatic compounds↗
Data Assimilation using Non-invasive Monte Carlo Sensitivity Analysis of Reactor Kinetics Parameters
Accurately predicting the criticality of an experiment before interacting with the experimental components is very important for criticality safety. Radiation transport software can be utilized to calculate the effective neutron multiplication factor of a nuclear system. Because of the integral nature of the effective neutron multiplication factor, the value calculated contains various sources of nuclear-data induced uncertainty. The sensitivity analysis and data assimilation technique presented in this paper exhibit one possible method of identifying and reducing the effective neutron multiplication factor nuclear-data induced uncertainty. The results presented in this work show that it is possible to use relative sensitivity coefficients of the prompt neutron decay constant and the effective delayed neutron fraction to 239 Pu nuclear data to reduce nuclear-data induced uncertainties in the effective neutron multiplication factor. This work has been utilized by members of the Los Alamos National Laboratory project EUCLID (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) for optimally designing a new experiment, which will be used to reduce compensating errors in 239 Pu nuclear data.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗