Reaction Propensities Research Articles

Analysis of magnesia-carbon bricks erosion in vanadium extraction converters: Insights from dynamic experiments and interface characterization

This paper uses a rotary furnace to simulate the dynamic erosion behavior of various magnesia-carbon bricks within a vanadium extraction converter, focusing on the effects of carbon content and antioxidants on erosion. The results indicate that vanadium-rich slag primarily consists of oxide solid solutions, iron vanadium spinel, and metallic Fe. Adding aluminum and silicon powders to the bricks reduces apparent porosity, increases body density, and improves slag erosion resistance. However, an increase in carbon content (12.54 wt% to 18.59 wt%) within magnesia-carbon bricks correlates with decreased high-temperature strength, exacerbating the reaction propensity with slag and consequent structural damage. Analysis of the interface phase between magnesia-carbon bricks and vanadium slag reveals a predominance of metal phase, spinel phase, and liquid slag phase. Thermodynamic calculations suggest an initial reaction between MgO and C on the brick surface, yielding Mg vapor and CO gas, while C reacts with slag to produce Cr and V elements, leading to surface structure degradation. Subsequent slag encasement and dissolution of remaining MgO via decarburization channels further erode magnesia-carbon bricks. Cumulative erosion iterations ultimately culmi nate in performance failure of magnesia-carbon bricks.

Rational Design of Loop Dynamics for a Barrel-Shaped Enzyme by Introducing Disulfide Bonds.

Loop dynamics redesign is an important strategy to manipulate protein function. Cellobiose 2-epimerase (CE) and other members of its superfamily are widely used for diverse industrial applications. The structural feature of the loops connecting barrel helices contributes greatly to the differences in their functional characteristics. Inspired by the in-silico mutation with molecular dynamics (MD) simulation analysis, we propose a strategy for identifying disulfide bond mutation candidates based on the prediction of protein flexibility and residue-residue interaction. The most beneficial mutant with the newly introduced disulfide bond would simultaneously improve both its thermostability and its reaction propensity to the targeting isomerization product. The ratio of the isomerization/epimerization catalytic rate was improved from 4:103 to 9:22. MD simulation and binding free energy calculations were applied to provide insights into molecular recognition upon mutations. The comparative analysis of enzyme/substrate binding modes indicates that the altered catalytic reaction pathway is due to less efficient binding of the native product. The key residue responsible for the observed phenotype was identified by energy decomposition and was further confirmed by the mutation experiment. The rational design of the key loop region might be a promising strategy to alter the catalytic behavior of all (α/α)6-barrel-like proteins.

Effects of initial Cu grain size and impurity residues on the liquid phase reactions between molten solder and electroplated Cu

Copper (Cu) electroplating is a cost-effective and scalable technology used to fabricate interconnects/metallizations in microelectronic products. Liquid-phase soldering reactions between electroplated Cu and Sn-based alloys are essential for constructing heterogeneous joints for mechanical support and electrical conduction applications. In this study, three Cu electroplated layers with different grain sizes and impurity contents are fabricated by formulating additives in the plating solution. The Cu electroplated layers are reacted with molten Sn-3.5 wt% Ag alloy at 260 °C for 20–60 min to investigate the liquid phase reactions. Microstructural characterizations of the electroplated Cu layers and the Cu/solder/Cu joints are carried out using electron microscopy and X-ray diffraction processes. A time-of-flight secondary ion mass spectrometer is used to analyze the impurity contents in the Cu electroplated layers. The Cu/solder/Cu joints after liquid phase reactions are mechanically analyzed to measure the shear strength. The results show that the additive formulas affect the microstructural features and the impurity contents in the Cu electroplated layers. Electroplating of Cu with an ultrafine grain texture accompanies a high impurity content, and the solder joint constructed by the ultrafine-grained Cu exhibits a rapid liquid phase reaction and interfacial voiding propensity, leading to a low shear strength.

Improving the stability of thiol-maleimide bioconjugates via the formation of a thiazine structure.

Linker stability is critically important for the efficacy and safety of peptide and protein conjugates used for biological applications. One common conjugation strategy, thiol-maleimide coupling, generates a succinimidyl thioether linker with limited stability under physiological conditions. We have shown in previous work that when a peptide with an N-terminal cysteine is conjugated to a maleimide reagent, a thiazine structure is formed via a chemical rearrangement. Our preliminary work indicated that the thiazine linker has favorable stability. Here, we report the evaluation of a thiazine linker as an alternative to the widely used succinimidyl thioether linker for thiol-maleimide bioconjugation. The stability of the thiazine conjugate in comparison to the thioether conjugate was assessed across a broad pH range. Additionally, the propensity for retro-Michael reaction and cross-reactivity with other thiols was evaluated by treating conjugates in the presence of glutathione. The studies indicated that the thiazine linker degrades markedly slower than the thioether conjugate. In addition, the thiazine linker is over 20 times less susceptible to glutathione adduct formation. The NMR study of the thiazine structure confirmed that the formation of the thiazine linker is a stereoselective process that yields a single diastereomer. In summary, we propose the use of the thiazine linker obtained by conjugation of maleimide-containing reagents with peptides or proteins presenting an N-terminal cysteine as a novel approach for bioconjugation. The advantages of this approach are the formation of a linker with a well-defined stereochemical configuration, increased stability at physiological pH, and a strongly reduced propensity for thiol exchange.

Accelerating likelihood calculations for biochemical network discovery

Biochemical networks are a widely used framework to quantitatively model gene expression, metabolic pathways, allosteric enzyme regulation, and other processes. These networks represent biochemical phenomena as a system of nodes (metabolites) and their connecting edges (the chemical reactions between them), with each edge weighted by a real number (rate) related to the reaction's propensity. Inferring rates from supplied data, usually in the form of reaction product counts across time, requires solving the chemical master equation (CME), which in turn requires calculating a matrix exponential — known to scale poorly with the number of states.

Magneto-Rotational Augmentation of Bioconvective Transport in Plasma-Nanofluid Flowing through a Penetrable Spinning Disc

The phenomenon of bioconvective transport through the manipulation of motile microorganisms is considered a promising process control technique in several biological processes and microdevices. Inducing convective transport in self-propelling microbes could be tailored to improve mixing, reaction propensity, and concentration transport within the media. This paper examined the combined effect of magnetic and rotational fields on the argumentation of bioconvective transport in the nanofluid-mediated plasma flow. A detailed analysis of the transport and dynamics of reactive forces during bioconvection in a rotary disc-like microchannel is presented. The physics of the problem was described by coupled nonlinear ordinary differential equations, which were numerically computed using the spectral relaxation scheme of the spectral homotopy analysis method. It was observed that the imposition of a magnetic field constituted viscous drag in the plasma-nanofluid media, which consequently increases the thermophoretic parameter in the bioconvective flow. It was ascertained that coupled magnetic and rotational effects significantly augmented the motility of microorganisms and translated to growth in momentum and concentration fields which is noticeable in the generation of stretching effect on the bacterium-containing plasma-nanofluid flow. The findings of this study could provide an essential basis for the design of bioreactors, centrifugal microfluidics technologies, and microdevices for use in a broad spectrum of biotechnology.

Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning.

Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemical reactions. A collection of key information that is most relevant to chemical reactions is integrated in CIMG:NMR chemical shifts as vertex features, bond dissociation energies as edge features, and solvent/catalyst information as global features. For any given compound as a target, a product CIMG is generated and exploited by a graph neural network (GNN) model to choose reaction template(s) leading to this product. A reactant CIMG is then inferred and used in two GNN models to select appropriate catalyst and solvent, respectively. Finally, a fourth GNN model compares the two CIMG descriptors to check the plausibility of the proposed reaction. A reaction vector is obtained for every molecule in training these models. The chemical wisdom of reaction propensity contained in the pretrained reaction vectors is exploited to autocategorize molecules/reactions and to accelerate Monte Carlo tree search (MCTS) for multistep retrosynthesis planning. Full synthetic routes with recommended catalysts/solvents are predicted efficiently using this CIMG-based approach.

Li+ Transport in Single-Ion Conducting Side-Chain Polymer Electrolytes with Nanoscale Self-Assembly of Ordered Ionic Domains

Li-ion batteries based on organic liquid electrolytes have been commercialized for decades. However, the flammability of the liquid electrolyte and propensity for reaction with metallic lithium anodes warrants the study of alternative electrolyte materials to satisfy modern demands such as higher safety and energy density. Polymer electrolytes are less flammable than organic liquids, more easily processable than inorganic solids, and more inert toward lithium metal and electrochemical side reactions. But the ionic conductivity in common polymer electrolytes, such as poly(ethylene oxide)-based polymer electrolytes, is limited by the segmental relaxation of the polymer matrix that is solvating the lithium cation. Recently, metal ion-containing polymers with regulated, repeating chain architecture have drawn attention as ion conductors due to their ionic domain segregation. In this contribution, we investigate the ion transport mechanism in a series of metal ion-containing polymers with ionic groups located on the side chains to explore their potential as Li+ conductors. Four side-chain polymers having different numbers (n = 6, 10, 12, and 15) of methylene groups as side spacers between the polymer backbone and terminally bound anions, titrated with Li+ counterions, were synthesized and characterized. These polymers were found to have strong nanoscale phase segregation with predominantly 1-D ionic domains. Through dielectric spectroscopy analysis, their conductivity was found to be linearly scaled with the dielectric relaxation rate. Short side chains (n = 6) resulted in a slower dielectric relaxation rate and lower DC conductivity compared to polymers with longer side chains (n ≥ 10). The long-range Li+ transport in these polymers is found to be coupled to the ionic cluster relaxations.

Generalized fluctuation-dissipation theorem for non-Markovian reaction networks.

Intracellular biochemical networks often display large fluctuations in the molecule numbers or the concentrations of reactive species, making molecular approaches necessary for system descriptions. For Markovian reaction networks, the fluctuation-dissipation theorem (FDT) has been well established and extensively used in fast evaluation of fluctuations in reactive species. For non-Markovian reaction networks, however, the similar FDT has not been established so far. Here, we present a generalized FDT (gFDT) for a large class of non-Markovian reaction networks where general intrinsic-event waiting-time distributions account for the effect of intrinsic noise and general stochastic reaction delays represent the impact of extrinsic noise from environmental perturbations. The starting point is a generalized chemical master equation (gCME), which describes the probabilistic behavior of an equivalent Markovian reaction network and identifies the structure of the original non-Markovian reaction network in terms of stoichiometries and effective transition rates (extensions of common reaction propensity functions). From this formulation follows directly the solution of the linear noise approximation of the stationary gCME for all the components in the non-Markovian reaction network. While the gFDT can quickly trace noisy sources in non-Markovian reaction networks, example analysis verifies its effectiveness.

Frontispiece: Properties of the Reactants and Their Interactions within and with the Enzyme Binding Cavity Determine Reaction Selectivities. The Case of Fe(II)/2‐Oxoglutarate Dependent Enzymes

The binding cavities of ODDs fine-tune the selectivities of catalysed reactions by precise positioning of the substrate with respect to the iron cofactor and by specific cavity-substrate interactions. The latter may outweigh the steric effects and the inherent reaction propensity of the reactants and thus settle product selectivity. For more information, see the Review by Z. Wojdyla and T. Borowski (DOI: 10.1002/chem.202104106).

SiCaSMA: An Alternative Stochastic Description via Concatenation of Markov Processes for a Class of Catalytic Systems

The Chemical Master Equation is a standard approach to model biochemical reaction networks. It consists of a system of linear differential equations, in which each state corresponds to a possible configuration of the reaction system, and the solution describes a time-dependent probability distribution over all configurations. The Stochastic Simulation Algorithm (SSA) is a method to simulate sample paths from this stochastic process. Both approaches are only applicable for small systems, characterized by few reactions and small numbers of molecules. For larger systems, the CME is computationally intractable due to a large number of possible configurations, and the SSA suffers from large reaction propensities. In our study, we focus on catalytic reaction systems, in which substrates are converted by catalytic molecules. We present an alternative description of these systems, called SiCaSMA, in which the full system is subdivided into smaller subsystems with one catalyst molecule each. These single catalyst subsystems can be analyzed individually, and their solutions are concatenated to give the solution of the full system. We show the validity of our approach by applying it to two test-bed reaction systems, a reversible switch of a molecule and methyltransferase-mediated DNA methylation.

Coverage Fluctuations and Correlations in Nanoparticle-Catalyzed Diffusion-Influenced Bimolecular Reactions

The kinetic processes in nanoparticle-based catalysis are dominated by large fluctuations and spatiotemporal heterogeneities, in particular for diffusion-influenced reactions which are far from equilibrium. Here, we report results from particle-resolved reaction-diffusion simulations of steady-state bimolecular reactions catalyzed on the surface of a single, perfectly spherical nanoparticle. We study various reactant adsorption and diffusion regimes, in particular considering the crowding effects of the reaction products. Our simulations reveal that fluctuations, significant coverage cross-correlations, transient self-poisoning, related domain formation, and excluded-volume effects on the nanoparticle surface lead to a complex kinetic behavior, sensitively tuned by the balance between adsorption affinity, mixed 2D and 3D diffusion, and chemical reaction propensity. The adsorbed products are found to influence the correlations and fluctuations, depending on overall reaction speed, thereby going beyond conventional steric (e.g., Langmuir-like) product inhibition mechanisms. We summarize our findings in a state diagram depicting the nonlinear kinetic regimes by an apparent surface reaction order in dependence of the intrinsic reaction propensity and adsorption strength. Our study using a simple, perfectly spherical, and inert nanocatalyst demonstrates that spatiotemporal heterogeneities are intrinsic to the reaction-diffusion problem and not necessarily caused by any dynamical surface effects from the catalyst (e.g., dynamical surface reconstruction), as often argued.

A Diffusion Based Embedding of the Stochastic Simulation Algorithm in Continuous Space

A variety of simulation methodologies have been used for modeling reaction-diffusion dynamics -- including approaches based on Differential Equations (DE), the Stochastic Simulation Algorithm (SSA), Brownian Dynamics (BD), Green's Function Reaction Dynamics (GFRD), and variations thereon -- each offering trade-offs with respect to the ranges of phenomena they can model, their computational tractability, and the difficulty of fitting them to experimental measurements. Here, we develop a multiscale approach combining efficient SSA-like sampling suitable for well-mixed systems with aspects of the slower but space-aware GFRD model, assuming as with GFRD that reactions occur in a spatially heterogeneous environment that must be explicitly modeled. Our method extends the SSA approach in two major ways. First, we sample bimolecular association reactions following diffusive motion with a time-dependent reaction propensity. Second, reaction locations are sampled from within overlapping diffusion spheres describing the spatial probability densities of individual reactants. We show the approach to provide efficient simulation of spatially heterogeneous biochemistry in comparison to alternative methods via application to a Michaelis-Menten model.

A comparison of the German and Russian literary intelligentsia in Arnold Hauser’s Social History of Art

To date, critical engagement with Arnold Hauser’s sociology of art has been confined to the field of art history. This perspective has ignored Hauser’s interest in literary history, which I argue is essential to his project. Hauser’s dialectical model, composed of conflicting realist and formalist tendencies, extends to the literary sphere. In The Social History of Art, these two traditions are epitomised by the Russian social novel and German idealism. Anti-enlightenment tendencies in German intellectual culture provide Hauser with evidence of idealism’s propensity for escapism and reaction. Conversely, he extols the Russian social novel as the naturalistic art form par excellence. Because the intelligentsia is central to Hauser’s understanding of the formation of literary culture, this paper provides an outline of his sociology of intellectuals. Through a comparison of the German and Russian literary intelligentsia, this paper shows that Hauser’s analysis of literature is often more complex than his sociological interpretations of the visual arts.

Chemical Kinetics Roots and Methods to Obtain the Probability Distribution Function Evolution of Reactants and Products in Chemical Networks Governed by a Master Equation.

In this paper first, we review the physical root bases of chemical reaction networks as a Markov process in multidimensional vector space. Then we study the chemical reactions from a microscopic point of view, to obtain the expression for the propensities for the different reactions that can happen in the network. These chemical propensities, at a given time, depend on the system state at that time, and do not depend on the state at an earlier time indicating that we are dealing with Markov processes. Then the Chemical Master Equation (CME) is deduced for an arbitrary chemical network from a probability balance and it is expressed in terms of the reaction propensities. This CME governs the dynamics of the chemical system. Due to the difficulty to solve this equation two methods are studied, the first one is the probability generating function method or z-transform, which permits to obtain the evolution of the factorial moment of the system with time in an easiest way or after some manipulation the evolution of the polynomial moments. The second method studied is the expansion of the CME in terms of an order parameter (system volume). In this case we study first the expansion of the CME using the propensities obtained previously and splitting the molecular concentration into a deterministic part and a random part. An expression in terms of multinomial coefficients is obtained for the evolution of the probability of the random part. Then we study how to reconstruct the probability distribution from the moments using the maximum entropy principle. Finally, the previous methods are applied to simple chemical networks and the consistency of these methods is studied.

Beyond Mean-Field Microkinetics: Toward Accurate and Efficient Theoretical Modeling in Heterogeneous Catalysis

Kinetics as the link between atomic scale properties and macroscopic functionalities is indispensable in describing surface chemical reactions and computation-based rational design of catalysts. Kinetic Monte Carlo (KMC) on the explicit lattice can resolve events taking place on the catalytic surfaces at the atomic level. It can explicitly account for spatial correlations due to lateral interactions among adsorbates, which have been proved to significantly affect the surface chemical reactions. However, the disparity in time scales of various processes (e.g., adsorption/desorption, diffusion, and reaction) usually makes brute force KMC simulations impractical. Here, we propose a method, namely XPK, to extend the phenomenological kinetics (PK) for the accurate and efficient microkinetic modeling of heterogeneous catalysis. XPK is achieved through a hybrid between the diffusion-only KMC on the explicit lattice to evaluate the reaction propensities and later an implicit lattice KMC in the PK form to evolve t...

Efficient stochastic simulation of biochemical reactions with noise and delays

The stochastic simulation algorithm has been used to generate exact trajectories of biochemical reaction networks. For each simulation step, the simulation selects a reaction and its firing time according to a probability that is proportional to the reaction propensity. We investigate in this paper new efficient formulations of the stochastic simulation algorithm to improve its computational efficiency. We examine the selection of the next reaction firing and reduce its computational cost by reusing the computation in the previous step. For biochemical reactions with delays, we present a new method for computing the firing time of the next reaction. The principle for computing the firing time of our approach is based on recycling of random numbers. Our new approach for generating the firing time of the next reaction is not only computationally efficient but also easy to implement. We further analyze and reduce the number of propensity updates when a delayed reaction occurred. We demonstrate the applicability of our improvements by experimenting with concrete biological models.

Accelerating rejection-based simulation of biochemical reactions with bounded acceptance probability.

Stochastic simulation of large biochemical reaction networks is often computationally expensive due to the disparate reaction rates and high variability of population of chemical species. An approach to accelerate the simulation is to allow multiple reaction firings before performing update by assuming that reaction propensities are changing of a negligible amount during a time interval. Species with small population in the firings of fast reactions significantly affect both performance and accuracy of this simulation approach. It is even worse when these small population species are involved in a large number of reactions. We present in this paper a new approximate algorithm to cope with this problem. It is based on bounding the acceptance probability of a reaction selected by the exact rejection-based simulation algorithm, which employs propensity bounds of reactions and the rejection-based mechanism to select next reaction firings. The reaction is ensured to be selected to fire with an acceptance rate greater than a predefined probability in which the selection becomes exact if the probability is set to one. Our new algorithm improves the computational cost for selecting the next reaction firing and reduces the updating the propensities of reactions.

Stochastic Simulation of Biomolecular Networks in Dynamic Environments.

Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate—using decision-making by a large population of quorum sensing bacteria—that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits.

HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.