Acetylation of Glycerol over Highly Stable and Active Sulfated Alumina Catalyst: Reaction Mechanism, Kinetic Modeling and Estimation of Kinetic Parameters

ABSTRACTOptimization of Rubisco kinetics could improve photosynthetic efficiency, ultimatly resulting in increased crop yield. However, imprecise knowledge of the reaction mechanism and the individual rate constants limit our ability to optimize the enzyme. Membrane inlet mass spectrometery (MIMS) may offer benefits over traditional methods for determining individual rate constants of the Rubisco reaction mechanism, as it can directly monitor concentration changes in CO2, O2, and their isotopologs during assays. However, a direct comparsion of MIMS to the traditional Radiolabel method of determining Rubisco kinetic parameters has not been made. Here, the temperature responses of Rubisco kinetic parameters from Arabidopsis thaliana were measured using the Radiolabel and MIMS methods. The two methods provided comparable parameters above 25 °C, but temperature responses deviated at low temperature as MIMS derived catalytic rates of carboxylation, oxygenation, and CO2/O2 specificity showed thermal breakpoints. Here we discuss the variability and uncertainty surrounding breakpoints in the Rubisco temperature response and relavance of individual rate constants of the reaction mechanisms to potential breakpoints.

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Mechanism of NO–CO reaction over highly dispersed cuprous oxide on γ-alumina catalyst using a metal–support interfacial site in the presence of oxygen: similarities to and differences from biological systems

Catalysis Science & Technology ◽

10.1039/c8cy00080h ◽

2018 ◽

Vol 8 (15) ◽

pp. 3833-3845 ◽

Cited By ~ 7

Author(s):

Ryoichi Fukuda ◽

Shogo Sakai ◽

Nozomi Takagi ◽

Masafuyu Matsui ◽

Masahiro Ehara ◽

...

Keyword(s):

Reaction Mechanism ◽

Cuprous Oxide ◽

Cluster Model ◽

Biological Systems ◽

Alumina Catalyst ◽

Highly Dispersed ◽

Metal Support

The NO–CO reaction mechanism over the Cu/γ-Al2O3 catalyst was elucidated using DFT and a cluster model.

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Polarographic and Cyclic Voltammetric Behaviour of Some Azo Compounds Derived from Sulfonamide in DMF-Aqueous Solutions

Collection of Czechoslovak Chemical Communications ◽

10.1135/cccc19920268 ◽

1992 ◽

Vol 57 (2) ◽

pp. 268-275 ◽

Cited By ~ 2

Author(s):

El-Sayed M. Mabrouk ◽

Hamada M. Killa ◽

Abdel Fattah A. Abdel Fattah ◽

Shalaby A. Yasen

Keyword(s):

Reaction Mechanism ◽

Aqueous Solutions ◽

Kinetic Parameters ◽

Alkaline Solution ◽

Electrode Reaction ◽

Azo Compounds ◽

Cyclic Voltammetric ◽

Ph Range

The polarographic and cyclic voltammetric behaviour of (2-hydroxyphenylazo)-4-benzenesulfonamide and some of its derivatives have been studied in Britton-Robinson buffer series containing 30 vol.% of DMF. Over the entire pH range (2-12), the reduction pathway occurs through an irreversible 4-electron step corresponding to the reduction of N=N center to the amine stage. The voltammograms recorded in acidic and alkaline solution at different scan rates exhibit one or two cathodic peaks depending on the substituent and the pH of the medium. The electrode reaction mechanism was suggested, also the kinetic parameters were calculated.

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Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation

10.26434/chemrxiv.13489656 ◽

2020 ◽

Author(s):

Mengjie Liu ◽

Alon Grinberg Dana ◽

Matthew Johnson ◽

Mark Goldman ◽

Agnes Jocher ◽

...

Keyword(s):

Reaction Mechanism ◽

Kinetic Parameters ◽

Important Change ◽

Elementary Reactions ◽

Chemical Systems ◽

Chemical Mechanisms ◽

Computational Performance ◽

Reaction Mechanism Generator ◽

Automatic Mechanism ◽

Global Uncertainty

In chemical kinetics research, kinetic models containing hundreds of species and tens of thousands of elementary reactions are commonly used to understand and predict the behavior of reactive chemical systems. Reaction Mechanism Generator (RMG) is a software suite developed to automatically generate such models by incorporating and extrapolating from a database of known thermochemical and kinetic parameters. Here, we present the recent version 3 release of RMG and highlight improvements since the previously published description of RMG v1.0. One important change is that RMG v3.0 is now Python 3 compatible, which supports the most up-to-date versions of cheminformatics and machine learning packages that RMG depends on. Additionally, RMG can now generate heterogeneous catalysis models, in addition to the previously available gas- and liquid-phase capabilities. For model analysis, new methods for local and global uncertainty analysis have been implemented to supplement first-order sensitivity analysis. The RMG database of thermochemical and kinetic parameters has been significantly expanded to cover more types of chemistry. The present release also includes parallelization for reaction generation and on-the-fly quantum calculations, and a new molecule isomorphism approach to improve computational performance. Overall, RMG v3.0 includes many changes which improve the accuracy of the generated chemical mechanisms and allow for exploration of a wider range of chemical systems.

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