Complex Reaction Mechanisms Research Articles

Two routes to hydrogen evolution for a Co-polypyridyl complex with two open sites

Cobalt polypyridyl complexes efficiently catalyze hydrogen evolution in aqueous media and exhibit high stability under reducing conditions. Their stability and activity can be tuned through electronic and steric considerations, but the rationalization of these effects requires detailed mechanistic understanding. As an example, tetradentate ligands with two non-permanently occupied coordination sites show higher activity with these sites in cis compared to trans configuration. Here reaction mechanisms of the Co-polypyridyl complex [CoII(bpma)Cl2] (bpma = bipyridinylmethyl-pyridinylmethyl-methyl-amine) have been studied using hybrid density-functional theory. This complex has two exchangeable cis sites, and provides a flexible ligand environment with both pyridyl and amine coordination. Two main pathways with low barriers are found. One pathway, which includes both open sites, is hydrogen evolution from a CoII-H intermediate with a water ligand as the proton donor. In the second pathway H–H bond formation occurs between the hydride and the protonated bpma ligand, with one open site acting as a spectator. The two pathways have similar barriers at higher pH, while the latter becomes more dominant at lower pH. The calculations consider a large number of interconnected variables; protonation sites, isomers, spin multiplicities, and the identities of the open binding sites, as well as their combinations, thus exploring many simultaneous dimensions within each pathway. The results highlight the effects of having two open cis-coordination sites and how their relative binding affinities change during the reaction pathway. They also illustrate why CoII-H intermediates are more active than CoIII-H ones, and why pyridyl protonation gives lower reaction barriers than amine protonation.

Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation.

Computational screening in heterogeneous catalysis relies increasingly on machine learning models for predicting key input parameters due to the high cost of computing these directly using first-principles methods. This becomes especially relevant when considering complex materials spaces such as alloys, or complex reaction mechanisms with adsorbates that may exhibit bi- or higher-dentate adsorption motifs. Here we present a data-efficient approach to the prediction of binding motifs and associated adsorption enthalpies of complex adsorbates at transition metals and their alloys based on a customized Wasserstein Weisfeiler-Lehman graph kernel and Gaussian process regression. The model shows good predictive performance, not only for the elemental transition metals on which it was trained, but also for an alloy based on these transition metals. Furthermore, incorporation of minimal new training data allows for predicting an out-of-domain transition metal. We believe the model may be useful in active learning approaches, for which we present an ensemble uncertainty estimation approach.

Online prediction based on the Copula function for the intrinsic viscosity of polyester fibre

AbstractThe polymerization process, as the starting point of polyester fibre production, directly affects the fibre properties. However, the polymerization process has complex reaction mechanisms and involves many variables, making online modelling more challenging. In this paper, we introduce the Copula function into the modelling of the polymerization process. First, a feature selection method based on kernel principal component analysis (KPCA)‐Copula is proposed, and then, the Copula function to obtain the nonlinear characteristics among the variables is used. On this basis, an online prediction model of intrinsic viscosity based on just‐in‐time learning (JITL) was established, that is, empirical Copula‐JITL. In order to improve the computational efficiency of the empirical Copula‐JITL, the t‐Copula function is introduced. Furthermore, by combining the t‐Copula function with the empirical Copula function, we get a JITL model based on the hybrid Copula function, that is, hybrid Copula‐JITL. Finally, actual polymerization process data collected from fibre production are provided to demonstrate the effectiveness of the proposed algorithms.

Exploring Complex Reaction Networks Using Neural Network-Based Molecular Dynamics Simulation.

Ab initio molecular dynamics (AIMD) is an established method for revealing the reactive dynamics of complex systems. However, the high computational cost of AIMD restricts the explorable length and time scales. Here, we develop a fundamentally different approach using molecular dynamics simulations powered by a neural network potential to investigate complex reaction networks. This potential is trained via a workflow combining AIMD and interactive molecular dynamics in virtual reality to accelerate the sampling of rare reactive processes. A panoramic visualization of the complex reaction networks for decomposition of a novel high explosive (ICM-102) is achieved without any predefined reaction coordinates. The study leads to the discovery of new pathways that would be difficult to uncover if established methods were employed. These results highlight the power of neural network-based molecular dynamics simulations in exploring complex reaction mechanisms under extreme conditions at the ab initio level, pushing the limit of theoretical and computational chemistry toward the realism and fidelity of experiments.

Functionalization of poly(butylene adipate‐co‐terephthalate) with itaconic anhydride through graft copolymerization

AbstractPoly(butylene adipate‐co‐terephthalate) (PBAT) was functionalised with itaconic anhydride (IA) via melt grafting in a batch mixer, initiated by dicumyl peroxide (DCP). It was found that the ratio of IA to DCP is critical to avoid undesired side reactions, such as crosslinking and chain scission. PBAT and other polyesters with low aliphatic hydrocarbon content are relatively unreactive toward radical graft modification, providing relatively low degree of grafting. Using 2 wt% IA and 1.2 wt% DCP gave the highest degree of grafting (1%), with little indication of crosslinking or chain scission. The complex reaction mechanism of grafting presents a clear competition between grafting, homopolymerization, crosslinking, and chain scission. Using non‐isothermal kinetic modeling, it was shown that the reaction is not first order, and that models making this assumption leads to incorrect results. Using the Vyazovkin model‐free method, the effective rate constant was predicted over a typical melt processing temperature range. Low reagent concentration resulted in the lowest effective rate constant, and it was shown that the activation energy changes with conversion and is also affected by reagent concentration.

Shepherding reaction intermediates to optimize H2 yield using composite-doped TiO2-based photocatalysts

Optimization of Pt-promoted TiO2-based is key to promote the photocatalytic production of hydrogen using sacrificial alcohol molecules. Combination of doping and surface decoration of the mentioned base photoactive material is here exploited to maximize hydrogen yield. Using the quantum efficiency parameter, it is shown that the resulting composite system can boost activity up to 7.3 times within the whole methanol:water mixture ratio, yielding quantum efficiencies in the ca. 13–16 % range. The key role of the different components in generating charge carrier species and their use to trigger the sacrificial molecule evolution and control reaction kinetics are examined through an in-situ spectroscopic study. The study unveils the complex reaction mechanism, with generation of C1 to C3 molecules from different carbon-containing radicals, and interprets the physical origin of the huge H2 production enhancement occurring in doped-composite titania-based catalysts.

Synchronized Two-Color Time-Resolved Dual-Comb Spectroscopy for Quantitative Detection of HOx Radicals Formed from Criegee Intermediates.

Criegee intermediates, derived from ozonolysis of alkenes and recognized as key species in the production of nonphotolytic free radicals, play a crucial role in atmospheric chemistry. Here, we present a spectrometer based on synchronized two-color time-resolved dual-comb spectroscopy, enabling simultaneous spectral acquisitions in two molecular fingerprint regions near 2.9 and 7.8 μm. Upon flash photolysis of CH2I2/O2/N2 gas mixtures, multiple reaction species, involving the simplest Criegee intermediates (CH2OO), formaldehyde (CH2O), hydroxyl (OH) and hydroperoxy (HO2) radicals are simultaneously detected with microsecond time resolution. The concentration of each molecule can be determined based on high-resolution rovibrational absorption spectroscopy. With quantitative detection and simulation of temporal concentration profiles of the targeted molecules at various conditions, the underlying reaction mechanisms and pathways related to the formation of the HOx radicals, which can be generated from decomposition of initially energized and vibrationally excited Criegee intermediates, are explored. This approach capable of achieving multispectral measurements with simultaneously high spectral and temporal resolutions opens up the opportunities for quantification of transient intermediates and products, thus, enabling elucidation of complex reaction mechanisms.

Deep neural network-based hybrid modeling and experimental validation for an industry-scale fermentation process: Identification of time-varying dependencies among parameters

Kinetic modeling of fermentation processes is difficult due to the use of micro-organisms that follow complex reaction mechanisms. Kinetic models are usually not perfect owing to incomplete knowledge of the system. Recently, there is a lot of interest towards data-driven modeling as the amount of data collected, stored, and utilized is growing tremendously due to the advent of super-computing power and data storage devices. Additionally, data-driven models are simple and easy to build but their utility is restricted by the amount and quality of data required. Therefore, hybrid modeling is an attractive alternative to purely data-based modeling, wherein it combines a kinetic model with a data-based model resulting in improved accuracy and robustness. In this work, a hybrid model is developed for an industry-scale fermentation process (>100,000 gallons) using a three-step process. The accuracy of the kinetic model is first improved utilizing process knowledge obtained from the literature. Sensitivity analyses are then utilized to identify sensitive parameters in the kinetic model that have considerable influence on its prediction capability. Finally, a deep neural network (DNN)-based hybrid model is developed by integrating the kinetic model with a DNN trained with time-series process data to predict sensitive and uncertain model parameters. The hybrid model is shown to be more accurate and robust than the kinetic model, providing a novel capability to capture unknown time-varying dependencies among parameters.

Process monitoring of abnormal working conditions in the zinc roasting process with an ALD-based LOF-PCA method

Timely and accurate detection of abnormal working conditions can ensure stability, improve production efficiency and reduce pollution of an industrial process. However, the production data of an industrial process has non-Gaussian and time-varying characteristics due to the diverse feed composition and complex reaction mechanisms. To address the above issue, an improved online principal component analysis (PCA) algorithm based on the selective model update is proposed in this study. First, considering the non-Gaussian nature of the process data, a local outlier factor-based (LOF) abnormality detection logic is used to replace the T2 and squared prediction error (SPE) statistics in traditional PCA algorithms. Then, to adapt to the time-varying characteristics of the process data, an approximate linear dependence (ALD) algorithm is used to evaluate the independent degree between the new sample and training samples. Only those samples containing new information are used to update the monitoring model, which can improve model performance and reduce the frequency of online updates. The zinc roasting process (ZRP) is used as an example to illustrate the proposed approach. Industrial data collected from a ZRP is used to demonstrate the performance of the ALD-based LOF-PCA method in the early detection of two typical abnormal working conditions in the ZRP.

Spectroscopic and microscopic studies of Co, Ce, and Pd containing gamma-alumina as catalysts for cyclohexene oxidation

The oxidation of cyclohexene belongs to the most important reactions in organic chemistry and leads to the production of the precursors for surfactants, polymers, agrochemicals, and drugs. So far, however, due to a complex reaction mechanism, it was hard to find the catalyst of satisfactory properties.Attractive catalysts in this reaction could be sonochemically prepared Co, Ce, and Pd containing γ-Al2O3, which is the object of the undertaken research. What is more, this combination of metals in the oxide system has not been studied. The analysis of catalytic performance indicated that all studied catalysts were active in the catalytic oxidation of cyclohexene. For metal-containing alumina (Me=Co, Ce, Pd), the conversion of cyclohexene was 61–84%. The best catalyst was Co5Ce5/γ-Al2O3s, prepared via the sonochemically-assisted procedure.It was also shown that the application of ultrasonic irradiation during the preparation of samples enhanced metal distribution over alumina support, resulted in smaller crystallites of prepared materials, and caused higher surface area with simultaneous smaller both pore size and pore volume in comparison with counterparts prepared via the standard procedure. XRD, Ra, and DR..-UV–vis spectroscopies indicated the coexistence of Co as Co3O4 and CoAl2O4 spinel, however, Ce was found in the form of CeO2. The addition of Pd to Co/γ-Al2O3 did not influence the status of cobalt.

The lithium storage mechanism of a new Li3Ti0.75(MoO4)3 high-performance anode material and its applications for both half-cell and full-cell

Anode materials with a low operating voltage, high capacity and excellent cycling stability are of great significance in the practical application of lithium-ion batteries (LIBs). In this work, a new anode material Li3Ti0.75(MoO4)3 is successfully synthesized. Due to the realization of the multielectron reversible reaction from Mo6+ to Mo0, the Li3Ti0.75(MoO4)3 electrode shows excellent electrochemical properties with a high reversible capacity of approximately 950 mAh g−1. Moreover, because the uniform distribution of titanium produced in the initial discharge process increases the conductivity of the electrode material, even at a high current density of 1 A g−1, a stable and high specific capacity of 644.6 mAh g−1 can be obtained after 350 cycles, which is close to twice that of a commercial graphite anode. Using in-situ X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and high-resolution transmission electron microscopy (HRTEM), we analyze the complex reaction mechanism of the electrode in detail for the first time. Moreover, the full-cell test exhibits a stable and reversible practical specific capacity of ∼120 mAh g−1 and excellent cycling stability. These results show that Li3Ti0.75(MoO4)3 promising for practical application and may be a potential alternative to commercial graphite anode materials.

Multichannel experimental and theoretical constraints for the Cd116(Ne20,F20)In116 charge exchange reaction at 306 MeV

Charge exchange (CE) reactions offer a major opportunity to excite nuclear isovector modes, providing clues about the nuclear interaction in the medium. Moreover, double charge exchange (DCE) reactions are proving to be a tempting tool to access nuclear transition matrix elements (NME) related to double beta-decay processes. Through a multi-channel experimental analysis and a consistent theoretical approach of the $^{116}$Cd($^{20}$Ne,$^{20}$F)$^{116}$In single charge exchange (SCE) reaction at 306 MeV, we aim at disentangling from the experimental cross section the contribution of the competing mechanisms, associated with second or higher order sequential transfer and inelastic processes. We measured excitation energy spectra and absolute cross sections for elastic + inelastic, one-proton transfer and SCE channels, using the MAGNEX large acceptance magnetic spectrometer to detect the ejectiles. For the first two channels, we also extracted the experimental cross section angular distributions. The experimental data are compared with theoretical predictions obtained by performing two-step distorted wave Born approximation and coupled reaction channel calculations. We employ spectroscopic amplitudes for single-particle transitions derived within a large-scale shell model approach and different optical potentials for modeling the initial and the final state interactions. The present study significantly mitigates the possible model dependence existing in the description of these complex reaction mechanisms, thanks to the reproduction of several channels at once. In particular, our work demonstrates that the two-step transfer mechanisms produce a non negligible contribution to the total cross section of the $^{116}$Cd($^{20}$Ne,$^{20}$F)$^{116}$In reaction channel, although a relevant fraction is still missing, being ascribable to the direct SCE mechanism, which is not addressed here.

Coupling mesoscale transport to catalytic surface reactions in a hybrid model.

In heterogeneous catalysis, reactivity and selectivity are not only influenced by chemical processes occurring on catalytic surfaces but also by physical transport phenomena in the bulk fluid and fluid near the reactive surfaces. Because these processes take place at a large range of time and length scales, it is a challenge to model catalytic reactors, especially when dealing with complex surface reactions that cannot be reduced to simple mean-field boundary conditions. As a particle-based mesoscale method, Stochastic Rotation Dynamics (SRD) is well suited for studying problems that include both microscale effects on surfaces and transport phenomena in fluids. In this work, we demonstrate how to simulate heterogeneous catalytic reactors by coupling an SRD fluid with a catalytic surface on which complex surface reactions are explicitly modeled. We provide a theoretical background for modeling different stages of heterogeneous surface reactions. After validating the simulation method for surface reactions with mean-field assumptions, we apply the method to non-mean-field reactions in which surface species interact with each other through a Monte Carlo scheme, leading to island formation on the catalytic surface. We show the potential of the method by simulating a more complex three-step reaction mechanism with reactant dissociation.

A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor Bias

Accurate estimation of the state of charge (SOC) of zinc–nickel single-flow batteries (ZNBs) is an important problem in battery management systems (BMSs). A nonideal electromagnetic environment will usually cause the measured signal to contain nonnegligible noise and bias. At the same time, due to the influence of battery ageing, environmental temperature changes, and a complex reaction mechanism, it is difficult to establish a very accurate system model that can be applied to various complex working conditions. The unscented Kalman filter (UKF) is a widely used SOC estimation algorithm, but the UKF will reduce the estimation accuracy and divergence under the influence of inaccurate model and sensor errors. To improve the performance of the UKF, a robust desensitized unscented Kalman filter (RDUKF) is proposed to realize an accurate SOC estimation of batteries in the context of different disturbances. Then, the proposed method is applied to cases of error interference, such as Gaussian noise, voltage sensor drift, an unknown initial state, and inaccurate model parameters. The simulation and experimental results show that compared with the standard UKF algorithm, the proposed estimation algorithm can effectively suppress the influence of various errors and disturbances and achieve higher accuracy and robustness.

Valorization of Wet Oily Petrochemical Sludge via Slow Pyrolysis: Thermo-Kinetics Assessment and Artificial Neural Network Modeling

Oily sludge is a hazardous waste stream of oil refineries that requires an effective process and an environment-friendly route to convert and recover valuable products. In this study, the pyrolytic conversion of the wet waste oil sludge was implemented in an autoclave pyrolyzer and a thermogravimetric analyzer (TGA) at 5°C/min, 20°C/min, and 40°C/min, respectively. The kinetic analysis was performed using model-free methods, such as Friedman, Kissenger–Akahira–Sunose (KAS), and Ozawa–Flynn–Wall (OFW) to examine the complex reaction mechanism. The average activation energy of wet waste oil sludge (WWOS) estimated from Friedman, KAS, and OFW methods was 198.68 ± 66.27 kJ/mol, 194.60 ± 56.99 kJ/mol, and 193.28 ± 54.88 kJ/mol, respectively. The activation energy increased with the conversion, indicating that complex multi-step processes are involved in the thermal degradation of WWOS. An artificial neural network (ANN) was employed to predict the conversion during heating at various heating rates. ANN allows complex non-linear relationships between the response variable and its predictors. nH, ΔG, and ΔS were found to be 191.26 ± 2.82 kJ/mol, 240.79 ± 2.82 kJ/mol, and −9.67 J/mol K, respectively. The positive values of ΔH and ΔG and the slightly negative value of ΔS indicate the endothermic nature of the conversion process, which is non-spontaneous without the supply of energy.

Understanding the Impact of Precipitation Kinetics on the Electrochemical Performance of Lithium–Sulfur Batteries by Operando X-ray Diffraction

The complex reaction mechanism of the lithium–sulfur battery system consists of repetitive dissolution and precipitation of the sulfur-containing species in the positive electrode. In particular, the precipitation of lithium sulfide (Li2S) during discharge has been considered a crucial factor for obtaining a high degree of active material utilization. Here, the influence of electrolyte amount, electrode thickness, applied current, and electrolyte salt on the formation of Li2S is systematically investigated in a series of operando X-ray diffraction experiments. Through a combination of simultaneous diffraction and resistance measurements, the evolution of Li2S is directly correlated to the variation in internal resistance and transport properties inside the positive electrode. The correlation indicates that at different stages the Li2S precipitation both facilitates and impedes the discharge process. This information about the kinetics of Li2S formation offers mechanistic explanations for the strong impact of different electrochemical cell parameters on the cell performance and, thus, directions for holistic optimizations to achieve high sulfur utilization.

Kinetics of fast reactions analyzed with stopped flow spectroscopy

Many physiologically relevant processes occur on the timescales inaccessible by conventional spectrometers. Kinetics of those reactions, occurring in the millisecond to hundreds of seconds range can be studied using stopped-flow technology. These experiments allow to obtain kinetic rate constants and study mechanisms of complex reactions by detecting different types of optical changes (absorbance, fluorescence, etc). Despite basic principle of rapid mixing of reagents together in an observation cell, and stopping the flow that triggers signal acquisition, sample handling and instrumentation play a key role in data quality.

A review on the modelling of hydrothermal liquefaction of biomass and waste feedstocks

Hydrothermal liquefaction is a thermo-chemical conversion process which uses water as a reaction medium at elevated pressure and temperature. It converts biomass and waste feedstock to biofuel and recovers water and fertilizers present in the feedstock. Hydrothermal liquefaction overcomes many technical limitations of the existing technologies such as anaerobic digestion, incineration, gasification, and pyrolysis and it offers a solution to water-energy-climate nexus. Hydrothermal liquefaction has been widely investigated to understand the mechanism and kinetics of hydrothermal liquefaction at different batch scales of operation. Significant efforts have been made recently to develop the kinetic models for various type of feedstocks to predict product yields despite having complex reaction mechanism and pathways. This article critically reviews past work and discuss the different types of mathematical models used to predict products yields and composition for different biomass and waste feedstocks. It encompasses summary of important characteristics of representative biomass and waste feedstocks and quality of biocrude product, experimental studies conducted for various predictive and kinetic model development. A few limitations on existing models were identified to focus for further study. Finally, suggestions have been incorporated with focus on improving hydrothermal liquefaction kinetic model for optimal product yields and composition under varying operating conditions for various feedstocks.

Modeling and simulation of wood pyrolysis process using COMSOL Multiphysics

The current study presents the modeling and simulation of wood pyrolysis process of American Red oak using COMSOL Multiphysics to identify the effect of process parameters on product yield, and predict the heat and mass transfer profiles of cylindrical geometry samples of two dimensions ((2 × 10) cm and (5 × 10) cm). The non-isothermal and isothermal pyrolysis kinetics were simulated at heating rates of 10, 20, 30, 40, and 100 °C/min, and temperature range of 250 to 600 °C, respectively to study the effect of heating rate, pyrolysis temperature. A complex reaction mechanism was included to predict the product distribution. During non-isothermal pyrolysis, samples of smaller geometry showed higher heat transfer, leading to occurrence of higher flux of produced vapors. The developed model maybe extended for different biomass samples to optimize the operating conditions for desired product yields. Further, this study may help scaling up the pyrolysis process.

Toward stable lithium-ion batteries: Accelerating the transfer and alloying reactions of Sn-based anodes via coordination atom regulation and carbon hybridization

Although anodes based on Sn alloys (SnX) such as SnO2, SnS, and Sn4P3 may be used to increase the gravimetric and volumetric energy densities of existing lithium-ion batteries, the further development of these anodes is hindered by their complex reaction mechanisms, sluggish conversion, and the occurrence of undesired reactions. Herein, we probe the mechanisms of the incompletely reversible reactions of SnO2 and SnS using analytical and electrochemical techniques and demonstrate that the capacity retention of these species can be further increased using coordination atom regulation and hybridization with carbon. The superior cyclability (sustainable operation for >500 cycles) of the optimized SnS/C composite is attributed to its reinforced conversion and alloying reactions resulting from enhanced bulk structure, higher interface stability, and accelerated kinetics. This work also provides guidance for the development of other alloy-based anode materials, especially regarding the choice of coordination atoms and the understanding of the role of carbon coatings in property optimization.