Showing papers on "Mass action law published in 2022"
TL;DR: This work introduces a program, PolyODENet, which defines a generative model and predicts concentrations at arbitrary time and can predict reaction profiles of unknown species and even reveal hidden parts of reaction mechanisms.
Abstract: Kinetics of a reaction network that follows mass-action rate laws can be described with a system of ordinary differential equations (ODEs) with polynomial right-hand side. However, it is challenging to derive such kinetic differential equations from transient kinetic data without knowing the reaction network, especially when the data are incomplete due to experimental limitations. We introduce a program, PolyODENet, toward this goal. Based on the machine-learning method Neural ODE, PolyODENet defines a generative model and predicts concentrations at arbitrary time. As such, it is possible to include unmeasurable intermediate species in the kinetic equations. Importantly, we have implemented various measures to apply physical constraints and chemical knowledge in the training to regularize the solution space. Using simple catalytic reaction models, we demonstrate that PolyODENet can predict reaction profiles of unknown species and doing so even reveal hidden parts of reaction mechanisms.
1 citations
03 Oct 2022
TL;DR: In this paper , the authors provided a brief introduction to the entangled mass action law theory and demonstrated how the entanglement equations can be derived in the framework of the standard quasi steady state approximation in combination with the quasiequilibrium generalized mass action laws for an auxiliary reaction network including reactants and intermediates.
Abstract: Recently, Gorban (2021) analysed some kinetic paradoxes of the transition state theory and proposed its revision that gave the ``entangled mass action law'', in which new reactions were generated as an addition to the reaction mechanism under consideration. These paradoxes arose due to the assumption of quasiequilibrium between reactants and transition states. In this paper, we provided a brief introduction to this theory, demonstrating how the entangled mass action law equations can be derived in the framework of the standard quasi steady state approximation in combination with the quasiequilibrium generalized mass action law for an auxiliary reaction network including reactants and intermediates. We also proved the basic physical property (positivity) for these new equations, which was not obvious in the original approach.
22 Jul 2022
TL;DR: In this article , the authors used tri-n-octyl amine (TOA) with different diluents to extract succinic acid from its aqueous phase.
Abstract: Succinic acid recovery from the aqueous phase is an intrinsic downstreaming process because of its numerous applications in the pharmaceutical and food industries. Distillation, sorption, adsorption, membrane, dialysis, and electrodialysis these are the conventional methods used for acid separation. Apart from these methods, a novel technique developed called reactive extraction that delivers good selectivity and yield. Separation of succinic acid from its aqueous phase was investigated in a recent work using a tertiary amine, such as tri-n-octyl amine (TOA), with different diluents. The study based on the acid concentration ranged from 0.14 to 0.5 mol/kg, while the TOA concentration ranged from 0.11 to 0.57mol/kg to study the parameters like the distribution coefficient, loading ratio, equilibrium complexation constant, and degree of extraction. The physical extraction of succinic acid by using these three diluents was done. As compare to physical extraction, reactive extraction gives better separation efficiency. The comparison of results by using different diluents in TOA was studied. The best result was obtained at the initial concentration of acid 0.26 mol/kg and initial concentration of TOA 0.57 mol/kg in the benzyl alcohol that gives 99.44 % extraction efficiency. The order of extraction power for the diluents was found to be benzyl alcohol > 2-octanol > 1-decanol. Keywords: succinic acid; reactive extraction; distribution coefficient; degree of extraction
Proceedings Article•
14 Sep 2022TL;DR: In this paper , the authors introduce a notion of online analog computation for the CRNs that stabilize the concentration of their output species to the result of some function of the concentration values of their input species, whatever changes are operated on the inputs during the computation.
Abstract: The Turing completeness of continuous chemical reaction networks (CRNs) states that any computable real function can be computed by a continuous CRN on a finite set of molecular species, possibly restricted to elementary reactions, i.e. with at most two reactants and mass action law kinetics. In this paper, we introduce a notion of online analog computation for the CRNs that stabilize the concentration of their output species to the result of some function of the concentration values of their input species, whatever changes are operated on the inputs during the computation. We prove that the set of real functions stabilized by a CRN with mass action law kinetics is precisely the set of real algebraic functions.
TL;DR: Huang et al. as discussed by the authors proposed a data-driven method to discover multiscale chemical reactions governed by the law of mass action, where a single matrix is used to represent the stoichiometric coefficients for both the reactants and products without catalysis reactions.
TL;DR: In this paper , the mass action law combined with activity coefficient models for the solution (ideal, Debye-Hückel and Pitzer equations) and for the solid (Wilson equation) with absolute average relative deviations (AARDs) between 6.56% and 6.75%.
TL;DR: Huang et al. as mentioned in this paper proposed a data-driven method to discover multiscale chemical reactions governed by the law of mass action, where a single matrix is used to represent the stoichiometric coefficients for both the reactants and products without catalysis reactions.
01 Jan 2022
TL;DR: In this article , the authors introduce a notion of online analog computation for the CRNs that stabilize the concentration of their output species to the result of some function of the concentration values of their input species, whatever changes are operated on the inputs during the computation.
Abstract: The Turing completeness of continuous chemical reaction networks (CRNs) states that any computable real function can be computed by a continuous CRN on a finite set of molecular species, possibly restricted to elementary reactions, i.e. with at most two reactants and mass action law kinetics. In this paper, we introduce a notion of online analog computation for the CRNs that stabilize the concentration of their output species to the result of some function of the concentration values of their input species, whatever changes are operated on the inputs during the computation. We prove that the set of real functions stabilized by a CRN with mass action law kinetics is precisely the set of real algebraic functions.