Complex Reaction Mechanisms Research Articles

Simultaneous self-optimisation of yield and purity through successive combination of inline FT-IR spectroscopy and online mass spectrometry in flow reactions

Self-optimisation constitutes a very helpful tool for chemical process development, both in lab and in industrial applications. However, research on the application of model-free autonomous optimisation strategies (based on experimental investigation) for complex reactions of high industrial significance, which involve considerable intermediate and by-product formation, is still in an early stage. This article describes the development of an enhanced autonomous microfluidic reactor platform for organolithium and epoxide reactions that incorporates a successive combination of inline FT-IR spectrometer and online mass spectrometer. Experimental data is collected in real-time and used as feedback for the optimisation algorithms (modified Simplex algorithm and Design of Experiments) without time delay. An efficient approach to handle intricate optimisation problems is presented, where the inline FT-IR measurements are used to monitor the reaction’s main components, whereas the mass spectrometer’s high sensitivity permits insights into the formation of by-products. To demonstrate the platform’s flexibility, optimal reaction conditions of two organic syntheses are identified. Both pose several challenges, as complex reaction mechanisms are involved, leading to a large number of variable parameters, and a considerable amount of by-products is generated under non-ideal process conditions. Through multidimensional real-time optimisation, the platform supersedes labor- and cost-intensive work-up procedures, while diminishing waste generation, too. Thus, it renders production processes more efficient and contributes to their overall sustainability.Graphical abstract

The influence of coupled kinetic-equilibrium in breaking the correlation between kinetic parameters

The most important requirement in nonlinear model fitting is an approach to solve the problem concerning the determination of full correlated model parameters. Since nonlinear model-based analysis is used in the chemical process, the importance of this issue is further increased. For instance, it is challenging to uniquely measure the rate constants in first-order reverse reactions from incomplete concentration data. This is due to the correlation between these constants. Therefore, breaking this correlation is necessary to determine the rate constants. In this work, the coupled kinetic-equilibrium process is suggested as a new strategy to overcome this problem. Also, this fact allows for determining kinetic parameters in multi-step kinetic reactions. The model-based global analysis is a complete analysis of the complex reaction mechanisms with the possibility of calculating and inserting the equilibrium and kinetic parameters into the fitting algorithm. After the fitting process, the optimal equilibrium and kinetic parameters are obtained, along with an estimate of their standard deviations. Therefore, simulated data sets for a range of different chemical cases, as well as the pH-dependent keto-enol tautomerization are used to exemplify the methods.

CuFeS2 as a Very Stable High-Capacity Anode Material for Sodium-Ion Batteries: A Multimethod Approach for Elucidation of the Complex Reaction Mechanisms during Discharge and Charge Processes.

Highly crystalline CuFeS2 containing earth-abundant and environmentally friendly elements prepared via a high-temperature synthesis exhibits an excellent electrochemical performance as an anode material in sodium-ion batteries. The initial specific capacity of 460 mAh g-1 increases to 512 mAh g-1 in the 150th cycle and then decreases to a still very high value of 444 mAh g-1 at 0.5 A g-1 in the remaining 550 cycles. Even for a large current density, a pronounced cycling stability is observed. Here, we demonstrate that combining the results of X-ray powder diffraction experiments, pair distribution function analysis, and 23Na NMR and Mössbauer spectroscopy investigations performed at different stages of discharging and charging processes allows elucidation of very complex reaction mechanisms. In the first step after uptake of 1 Na/CuFeS2, nanocrystalline NaCuFeS2 is formed as an intermediate phase, which surprisingly could be recovered during charging. On increasing the Na content, Cu+ is reduced to nanocrystalline Cu, while nanocrystalline Na2S and nanosized elemental Fe are formed in the discharged state. After charging, the main crystalline phase is NaCuFeS2. At the 150th cycle, the mechanisms clearly changed, and in the charged state, nanocrystalline CuxS phases are observed. At later stages of cycling, the mechanisms are altered again: NaF, Cu2S, and Cu7.2S4 appeared in the discharged state, while NaF and Cu5FeS4 are observed in the charged state. In contrast to a typical conversion reaction, nanocrystalline phases play the dominant role, which are responsible for the high reversible capacity and long-term stability.

Direct Observation of Adsorption of Ceria Particles on the Silicon Dioxide Surfaces and Their Removal

We demonstrated that the movement of the single ceria particles located at the glass interface could be imaged in situ using evanescent wave (EW) microscopy. (1,2) Here, the flat glass surface of the lens became our model for the SiO2 film surface of interest. 3D trajectories, diffusion coefficients, and interactions of ceria particles with a glass surface at pH 3, 5, and 7 were investigated using EW microscopy. We also defined the types and characteristics of particle motions by analyzing the mean-square displacement (MSD) versus time curves. (1)The removal of single ceria particles from the glass films in the cleaning solution was monitored in situ using EW microscopy coupled with a brush set-up. In situ measurement data collected during cleaning are likely to provide more important insights into the underlying mechanisms of various complex reactions that occur at the particles/brush–wafer interface. (3)This talk will also introduce lateral force microscopy (LFM) mode in the atomic force microscopy (AFM) to obtain the removal force of single ceria particles from the SiO2 film. LFM is a powerful tool to monitor the torsion bending (or twisting) of the cantilever with contact mode, which is corresponding to the removal force. Types of particle removal mechanism depending on the characterization of single particles (size, shape, and surface) will be discussed. Our results will provide a better understanding of how the abrasive particles, the additives, and the film surfaces interact together during cleaning.(1) Seo, J., Gowda, A., Khajornrungruang, P., Hamada, S., Song, T., & Babu, S. (2020). Trajectories, diffusion, and interactions of single ceria particles on a glass surface observed by evanescent wave microscopy. Journal of Materials Research, 35(3), 321-331.(2) Seo, J., Gowda, A., Khajornrungruang, P., Hamada, S., & Babu, S. (2020). 3D trajectories and diffusion of single ceria particles near a glass surface and their removal. Journal of Materials Research, 1-10.(3) Seo, J. (2020). A review on chemical and mechanical phenomena at the wafer interface during chemical mechanical planarization. Journal of Materials Research, 1-23.

(Invited) Advances in Pyrolyzed Polymeric Sulfur Cathodes for Low-Cost Lithium Metal Batteries

Sulfur is a highly desirable cathode material for lithium batteries but its complex reaction mechanisms involving soluble species have limited its cycle life. When sulfur exists as a very short chain or substituted function groups on aromatic rings, the dissolution process appears to be mitigated, leading to a long cycle life. A good example is Sulfurized-poly(acrylonitrile) (SPAN), which is synthesized by pyrolyzing a mixture of PAN and elemental S at 300-500oC. SPAN is essentially a nitrogen doped carbon host for sulfur and has shown exceptional cycling stabilities in common carbonate electrolytes.We have recently launched an intensive effort to advance the Li/SPAN battery technology to an energy density of > 300 Wh/kg. Its exceptional cycle life and low cost will make the technology a promising choice for electric vehicle applications. In this talk, we will review several interrelated efforts to realize this goal: 1) searching for SPAN-like materials with higher S loading and higher capacities by tuning N:C:S ratios with different polymer and sulfur precursors; 2) improving the packing density of SPAN particles in the cathode by controlling particle shape and size using new synthesis approaches. A highlight is a um-sized, platelet-like morphology that greatly increase packing densities; 3) developing a solvent-free homogenous chemical lithiation method for the synthesis of lithium containing SPAN. The lithiated SPAN can greatly reduce the irreversible capacity loss during the first discharge cycle and minimize the volume change associated with the initial lithiation process; and 4) continuing the development of new electrolyte compositions that can further improve the lithium metal anode efficiency without sacrificing SPAN cycle life. These studies hold the promise to enable a practical, low-cost and long-life Li/S battery.

A Multi‐Parametric Catalyst Screening for CO2 Hydrogenation to Ethanol

AbstractThe direct hydrogenation of CO2 to higher alcohols has the potential to turn the main contributor of global warming into a valuable feedstock. However, for this technology to become attractive, more efficient and, especially, selective catalysts are required. Here we present a high throughput study on the influence of different promoters on the CO2 hydrogenation performance of Rh−SiO2 catalysts. Fe and K promoters were found to improve ethanol selectivity at the expense of undesired CH4. The best‐performing catalyst, with a composition 2 wt.% K, 20 wt.% Fe, and 5 wt.% Rh, displays an EtOH selectivity of 16 % at CO2 conversion level of 18.4 % and CH4 selectivity of 46 %. The combination of different characterization techniques and catalyst screening allowed us to unravel the role of each catalyst component in this complex reaction mechanism.

Burning Characteristics of Pyrotechnic Time-Delay Composition Subjected to Moisture and Heat

The combustion of pyrotechnic delay composition based on metallic fuel and perchlorate oxidizer involves complex reaction mechanisms and condensed phase heat release. Studies on the effect of aging on ignition/burning properties, and understanding of such multicomponent pyrotechnic delay mixtures are quite limited. The present work focuses on investigating the reaction kinetics, ignition, and combustion behavior of a zirconium/nickel (Zr/Ni)-alloy-based pyrotechnic delay mixture. Comparisons are presented between pristine and hygrothermally aged samples subjected to accelerated aging conditions under a constant temperature of 71°C and 95% relative humidity for 2 weeks. Activation energy versus reaction progress, ignition delay time as a function of maximum wire temperature, reaction zone temperature profile, and burning rate with respect to pressure are obtained from various experimental procedures. Results indicate that aging increases the ignition delay time and temperature rise time significantly, and a substantial decrement in the heat of reaction and burning rates along with incomplete combustion are evident.

Data driven reaction mechanism estimation via transient kinetics and machine learning

• Reaction mechanism characterization via experimental transient kinetic measurements. • Integration of well-defined reactor physics and machine learning. • Data-driven kinetic coefficients estimation via machine learning. • Reaction mechanism classification through rate and gas concentration correlation. • Validation of reaction mechanism applied to CO oxidation on platinum. Understanding the set of elementary steps and kinetics in each reaction is extremely valuable to make informed decisions about creating the next generation of catalytic materials. With structural and mechanistic complexities of industrial catalysts, it is critical to obtain kinetic information through experimental methods. As such, this work details a methodology based on the combination of transient rate/concentration dependencies and machine learning to measure the number of active sites, the individual rate constants, and gain insight into the mechanism under a complex set of elementary steps. This new methodology was applied to simulated transient responses to verify its ability to obtain correct estimates of the micro-kinetic coefficients. Furthermore, data from an experimental CO oxidation on a platinum catalyst was analyzed to reveal that Langmuir-Hinshelwood mechanism drives the reaction. As oxygen accumulated on the catalyst, a transition in the apparent kinetics was clearly defined in the machine learning analysis due to the large amount of kinetic information available from transient reaction techniques. This methodology is proposed as a new data driven approach to characterize how materials control complex reaction mechanisms relying exclusively on experimental data.

Parameter estimation in kinetic models of complex heterogeneous catalytic reactions using Bayesian statistics

Kinetic analysis of the gas-phase and liquid phase hydrogenation of toluene to methylcyclohexane on Ni catalysts was performed. For complex reaction mechanisms comprising several kinetically significant steps, nonlinear regression, while giving an adequate description of experimental data, leads to kinetic parameters which are poorly defined. The approach based on Bayesian statistics allows identification of the values of such parameters which are the most statistically probable.

Determining parametric functions kinetic constants using the decomposition method

The purpose of this work is to automation of the informativity content analysis of kinetic parameters based onde composing a chemical reaction independent route. The software for determining the basis of nonlinear parametric functions of kinetic measurements for the mechanisms of complex chemical reactions is developed and described.

Computational analysis on transition time-period in complex reaction mechanism

To study the impact of reaction rate coefficients, a two-step mechanism is considered in this article. The computational results are calculated using the different values of the rate coefficients. To inspect the behavior of the species for different values of the rate coefficients, their activation energy and transition period have been measured before attaining equilibrium. Comparison between the varying values of rate coefficients are graphically compared through MATLAB programing.

Bayesian Network modelling for safety management of electric vehicles transported in RoPax ships

The safety management of electric vehicles when being transported by RoPax ships is addressed, as this form of maritime transportation is gaining relevance owing to the increased production of this type of vehicles, which develop a complex chemical reaction mechanism and hazard characteristics (e.g. initial exothermic temperature, self-heating rate, pressure rise rate). This paper develops a data-driven Bayesian Network to analyse the effects of the influencing factors on the consequences, and to propose appropriate countermeasures. The kernel of this model is built from the analysis of a sample of 132 accidents of fire accidents in electric vehicles, to derive the quantitative and qualitative relationships amongst influencing factors by using mutual information and Expectation-maximization algorithm, respectively, and to further analysis the marginal probability to discover the effects of influencing factors on the consequences. Afterwards, the findings from sensitivity analysis are used to discover the key failure patterns, and the results demonstrate that it would better not to allow the electric cars to charge when being transported by RoPax ships because the occurrence probability of explosion will increase. Moreover, adjustment on the external temperature is also an effective measure for consequence reduction.

Investigating the effect of carbon support on palladium-based catalyst towards electro-oxidation of ethylene glycol

Palladium-based catalysts serve as promising electrocatalysts for the oxidation of ethylene glycol to produce electrical energy that can be used to address the continuous worldwide energy demand increments along with the depletion of fossil fuels which serve as the main energy source. For optimal catalysts performance, carbon nanotubes and carbon nanodots were investigated as palladium catalyst support materials to address difficulties in oxidizing and breaking the C–C bonds in ethylene glycol, cost of electrocatalyst, and complex reaction mechanism that is restraining rapid development and applications of direct ethylene glycol fuel cells (DEGFC). Utilization of palladium catalysts supported on carbon nanotubes (CNT) and carbon nanodots (CND) as support materials resulted in spontaneous ethylene glycol oxidation. The Pd/CNT catalyst showed greater stability compared to Pd/CND during the oxidation of ethylene glycol, and it is not easily poisoned by carbon monoxide intermediates formed during ethylene glycol oxidation as shown by a slow current decay on chronoamperometry.

Numerical investigation on combustion and NOx emissions of a CH4 / kerosene afterburner

Cogeneration groups equipped with gas turbines usually operate with natural gas. The new requirements regarding the flexibility of functioning both with conventional and alternative fuels have led to the development of new solutions. An afterburning installation facilities, on the one hand, the combustion of fuels which can not normally be used in gas turbines, and on the other hand, allows a rapid adaptation of the cogeneration group to the user's variable thermal energy requirements. In some cases, an afterburning installation may contribute to the reduction of NOx emissions. It also increases the group's adaptability to changing environmental conditions, especially during the summer. Having in mind these tendencies and starting from the data collected from the 2xST 18 – Suplacu de Barcau cogeneration plant, this paper presents a numerical analysis of an afterburner in order to switch from natural gas to natural gas / kerosene functioning, with minimal modifications to the existing solution. A 3D steady RANS numerical integration of the Navier-Stokes equations has been carried out using the commercial software ANSYS CFX. For future research work, it will be taken into consideration the use of more complex reaction mechanisms and the variation of the spray angle will be considered. Also, in order to validate the CFD results a combustion experiments campaign will be conducted.

Model-free cure kinetics of tetra-functional epoxy reinforced with GO and p-Phenylenediamine modified GO

Tetra-functional epoxy was reinforced with graphene oxide (GO) and p-Phenylenediamine functionalized graphene oxide (fGO). FTIR and XPS analysis of fGO confirmed the attachment of amine groups on its surface. Data obtained from nonisothermal DSC scans of neat epoxy and epoxy modified with 0.1 and 0.5 wt.% GO and fGO was studied to evaluate the effect of filler content and chemistry on the curing behavior of epoxy. Cure analysis was performed quantitatively using the integral isoconversional method to identify the dependence of activation energy (Eα) on conversion. Eα for neat epoxy increased with increase in conversion, possibly because at higher conversions, reactions such as etherification and homo-polymerization controlled the reaction kinetics. The addition of GO and fGO both displayed a lower activation energy in the conversion range α = 0.5−0.9 as compared to neat epoxy. The activation energy of fGO reinforced epoxy remained almost constant with change in conversion. This behavior was attributable to the amine groups attached to the surface of the filler, which acted as a secondary hardener and triggered the epoxide ring opening reaction. This fact was supported by the comparative XPS analysis of fGO and curing agent which clearly indicated the compositional similarities between the two. Further the autocatalytic model was used to determine reaction parameters. The overall order of the curing reaction was more than unity throughout, indicative of a complex reaction mechanism.

Kinetic theory of hyaluronan cleavage by bovine testicular hyaluronidase in standard and crowded environments

In this paper, we introduce a comprehensive kinetic model describing the enzymatic cleavage of hyaluronan (HA) by bovine testicular hyaluronidase (BTH). Our theory focuses specifically on the late stage of the hydrolysis, where the concentrations of a limited number of oligomers may be determined experimentally with accuracy as functions of time.The present model was applied to fit different experimental sets of kinetic data collected by capillary electrophoresis at two HA concentrations and three concentrations of PEG crowder (0, 10, 17% w/w). Our theory seems to apply universally, irrespective of HA concentration and crowding conditions, reproducing to an excellent extent the time evolution of the individual molar fractions of oligomers. Remarkably, we found that the reaction mechanism in the late degradation stage essentially reduces to the cleavage or transfer of active dimers. While the recombination of dimers is the fastest reaction, the rate-limiting step turns out to be invariably the hydrolysis of hexamers. Crowding, HA itself or other inert, volume-excluding agents, clearly boosts recombination events and concomitantly slows down all fragmentation pathways.Overall, our results bring a novel and comprehensive quantitative insight into the complex reaction mechanism underlying enzymatic HA degradation. Importantly, rationalizing the effect of crowding not only brings the intricate conditions of in-vivo settings a little closer, but also emerges as a powerful tool to help pinpointing relevant kinetic pathways in complex systems.

What can we learn about urban air quality with regard to the first outbreak of the COVID-19 pandemic? A case study from central Europe

Abstract. Motor vehicle road traffic in central Budapest was reduced by approximately 50 % of its ordinary level for several weeks as a consequence of various limitation measures introduced to mitigate the first outbreak of the COVID-19 pandemic in 2020. The situation was utilised to assess the real potentials of urban traffic on air quality. Concentrations of NO, NO2, CO, O3, SO2 and particulate matter (PM) mass, which are ordinarily monitored in cities for air quality considerations, aerosol particle number size distributions, which are not rarely measured continuously on longer runs for research purposes, and meteorological properties usually available were collected and jointly evaluated in different pandemic phases. The largest changes occurred over the severest limitations (partial lockdown in the Restriction phase from 28 March to 17 May 2020). Concentrations of NO, NO2, CO, total particle number (N6–1000) and particles with a diameter < 100 nm declined by 68 %, 46 %, 27 %, 24 % and 28 %, respectively, in 2020 with respect to the average reference year comprising 2017–2019. Their quantification was based on both relative difference and standardised anomaly. The change rates expressed as relative concentration difference due to relative reduction in traffic intensity for NO, NO2, N6–1000 and CO were 0.63, 0.57, 0.40 and 0.22 (%/%), respectively. Of the pollutants which reacted in a sensitive manner to the change in vehicle circulation, it is the NO2 that shows the most frequent exceedance of the health limits. Intentional tranquillising of the vehicle flow has considerable potential for improving the air quality. At the same time, the concentration levels of PM10 mass, which is the most critical pollutant in many European cities including Budapest, did not seem to be largely affected by vehicles. Concentrations of O3 concurrently showed an increasing tendency with lower traffic, which was explained by its complex reaction mechanism. Modelling calculations indicated that spatial gradients of NO and NO2 within the city became further enhanced by reduced vehicle flow.

Revealing the complex sulfur reduction mechanism using cyclic voltammetry simulation

Understanding the complex reaction mechanism and kinetics of sulfur reduction is prerequisite to design well performing lithium-sulfur batteries. Decoupling of individual polysulfide species is problematic when only using experimental methods. Model supported electrochemical analysis together with HPLC–MS analysis of the reacting species is used to compensate for this missing insight, as it enables to analyze the underlying species, transport, kinetic and thermodynamic processes. Concentration measurements confirm a strong prevalence of chemical equilibrium reactions that cause many species to be present over a wide range of SoC. An EEC-mechanism represents the top-down view and yields the simplest generic mechanism, that is able to reproduce the electrochemical behavior. Reduction of sulfur is performed by two consecutive electron transfer reactions while a chemical reaction accounts for the decreasing cathodic-to-anodic peak ratio with decreasing scan rate. A physically motivated E3C4-mechanism is shown to yield convincing results besides transport and kinetic parameters. Circular routes and chemical equilibrium are included to reproduce all characteristics of the experimental cyclic voltammogram. Influence of kinetic and transport parameters are elucidated with a global sensitivity analysis. Peak currents are almost exclusively influenced by electrochemical kinetics, while diffusion limited currents largely depend on transport parameters. In addition, the E3C4-mechanism reveales the prominent role of chemical equilibrium between polysulfides in the range of transport limitation. The revealed mechanistic complexity leads to complex, non-intuitive behavior of sulfur electrodes and Li-S batteries. Thus it is highly recommended to use the presented E3C4 kinetics and model for a more reliable interpretation of experimental behavior, including the dynamic relaxation behavior, and for improved electrode and cell design that takes chemical disproportionation into account.

Experimental and Theoretical Investigations of the Radical-Radical Reaction: N2H3 + NO2.

The N2H3 + NO2 reaction plays a key role during the early stages of hypergolic ignition between N2H4 and N2O4. Here for the first time, the reaction kinetics of N2H3 in excess NO2 was studied in 2.0 Torr of N2 and in the narrow temperature range 298-348 K in a pulsed photolysis flow-tube reactor coupled to a mass spectrometer. The temporal profile of the product, HONO, was determined by direct detection of the m/z +47 amu ion signal. For each chosen [NO2], the observed [HONO] trace was fitted to a biexponential kinetics expression, which yielded a value for the pseudo-first-order rate coefficient, k', for the reaction of N2H3 with NO2. The slope of the plot of k' versus [NO2] yielded a value for the observed bimolecular rate coefficient, kobs, which could be fitted to an Arrhenius expression of (2.36 ± 0.47) × 10-12 exp((520 ± 350)/T) cm3 molecule-1 s-1. The errors are 1σ and include estimated uncertainties in the NO2 concentration. The potential energy surface of N2H3 + NO2 was investigated by advanced ab initio quantum chemistry theories. It was found that the reaction occurs via a complex reaction mechanism, and all of the reaction channels have transition state energies below that of the entrance asymptote. The radical-radical addition forms the N2H3NO2 adducts, while roaming-mediated isomerization reactions yield the N2H3ONO isomers, which undergo rapid dissociation reactions to several sets of distinct products. The RRKM multiwell master equation simulations revealed that the major product channel involves the formation of trans-HONO and trans-N2H2 below 500 K and the formation of NO + NH2NHO above 500 K, which is nearly pressure independent. The pressure-dependent rate coefficients of the product channels were computed over a wide pressure-temperature range, which encompassed the experimental data.

High-Capacity Layered Magnesium Vanadate with Concentrated Gel Electrolyte toward High-Performance and Wide-Temperature Zinc-Ion Battery.

Aqueous zinc-ion batteries (ZIBs) have emerged as the most promising alternative energy storage system, but the development of a suitable cathode and the issues of Zn anodes have remained challenging. Herein, an effective strategy of high-capacity layered Mg0.1V2O5·H2O (MgVO) nanobelts together with a concentrated 3 M Zn(CF3SO3)2 polyacrylamide gel electrolyte was proposed to achieve a durable and practical ZIB system. By adopting the designed concentrated gel electrolyte which not only inherits the high-voltage window and wide operating temperature of the concentrated electrolyte but also addresses the Zn dendrite formation problem, the prepared cathode exhibits an ultrahigh capacity of 470 mAh g-1 and a high rate capability of 345 mAh g-1 at 5.0 A g-1, and the assembled quasi-solid-state ZIBs achieve 95% capacity retention over 3000 cycles as well as a wide operating temperature from -30 to 80 °C, demonstrating a promising prospect for large-scale energy storage. In situ X-ray diffraction, X-ray photoelectron spectroscopy, and thermogravimetric analysis (TGA) investigations also demonstrate a complex reaction mechanism for this cathode involving the (de)insertion of Zn2+, H+, and water molecules during cycling. The water molecules will reinsert into the interlayer and act as "pillars" to stabilize the host structure when Zn2+ is fully extracted.