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Chemical Reaction Engineering

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Chemical reaction engineering (CRE) emerged as a methodology that quantifies the interplay between transport phenomena and kinetics on a variety of scales and allows formulation of quantitative models for various measures of reactor performance

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Recently a branch of CRE that is mainly focusing on transport phenomena and fluid flow analysis, rather than reaction kinetics, has emerged. By these groups the abbreviation CRE is frequently interpreted as chemical reactor engineering .

In Chap.  1 it was shown that when the total mole balance is used instead, the reaction term does not always vanish on the RHS because the number of moles may change in a chemical process. This total molar balance can be obtained starting out from the species mass balance ( 6.4 ), after dividing by the molecular mass for each species to obtain a species mole balance and finally sum these equations for all species in the mixture.

In classical thermodynamics a simple system is defined as a system that is macroscopically homogeneous, isotropic, and uncharged, that are large enough so that surface effects can be neglected, and that are not acted on by electric, magnetic, or gravitational fields [ 8 ]. A thermal reservoir is defined as a reversible heat source that is so large that any heat transfer of interest does not alter the temperature of the thermal reservoir. Such a thermal reservoir is a system enclosed by rigid impermeable walls and characterized by relaxation times sufficiently short so that all processes of interest therein are essentially quasi-static. Given two or more simple systems, they may be considered as constituting a single composite system. The composite system is termed closed if it is surrounded by a wall that is restrictive with respect to the total energy, the total volume, and the total mole numbers of each species of the composite system. In the present outline the universe is considered a composite system, the system represents a simple subsystem and the surroundings a thermal reservoir.

The Helmholtz free energy is sometimes denoted by \(\hat{A}\) ( \(J\) ).

The American chemist G. N. Lewis (1875–1946) introduced the fugacity function (Latin fugare , to fly) as a measure of the pressure adjusted for the lack of ideality [ 9 ]. For an ideal gas the fugacity is equal to the pressure. For a non-ideal gas we normally define the standard state to correspond to unit fugacity, \(f_i^0 = 1\) ( \(bar\) ).

This approach can also be adopted for liquid mixtures provided that an appropriate EOS is available.

The elements of Lagrangian mechanics are explained in Chap.  2 .

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Hugo A. Jakobsen

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Jakobsen, H.A. (2014). Chemical Reaction Engineering. In: Chemical Reactor Modeling. Springer, Cham. https://doi.org/10.1007/978-3-319-05092-8_6

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• Perspective
• Published: 26 January 2023

Exploring catalytic reaction networks with machine learning

• Johannes T. Margraf   ORCID: orcid.org/0000-0002-0862-5289 1 ,
• Hyunwook Jung 1 ,
• Christoph Scheurer 1 , 2 &
• Karsten Reuter   ORCID: orcid.org/0000-0001-8473-8659 1  

Nature Catalysis volume  6 ,  pages 112–121 ( 2023 ) Cite this article

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• Computational chemistry
• Heterogeneous catalysis

Chemical reaction networks form the heart of microkinetic models, which are one of the key tools available for gaining detailed mechanistic insight into heterogeneous catalytic processes. The exploration of complex chemical reaction networks is therefore a central task in current catalysis research. Unfortunately, microscopic experimental information about which elementary reaction steps are relevant to a given process is almost always sparse, making the inference of networks from experiments alone almost impossible. While computational approaches provide important complementary insights to this end, their predictions also come with substantial uncertainties related to the underlying approximations and, crucially, the use of idealized structure models. In this Perspective, we aim to shine a light on recent applications of machine learning in the context of catalytic reaction networks, aiding both the inference of effective kinetic rate laws from experiment and the computational exploration of chemical reaction networks.

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Chemical reaction networks and opportunities for machine learning

Bridging the complexity gap in computational heterogeneous catalysis with machine learning

Machine learning in chemical reaction space

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H.J. gratefully acknowledges support from the Alexander-von-Humboldt (AvH) Foundation.

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Johannes T. Margraf, Hyunwook Jung, Christoph Scheurer & Karsten Reuter

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Margraf, J.T., Jung, H., Scheurer, C. et al. Exploring catalytic reaction networks with machine learning. Nat Catal 6 , 112–121 (2023). https://doi.org/10.1038/s41929-022-00896-y

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• 1 Relay Therapeutics, Cambridge, Massachusetts 02139, United States.
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• 3 Chemistry Capabilities Accelerating Therapeutics, Merck & Co., Inc., Kenilworth, New Jersey 07033, United States.
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• PMID: 34727496
• DOI: 10.1021/jacs.1c09820

Chemical reaction data in journal articles, patents, and even electronic laboratory notebooks are currently stored in various formats, often unstructured, which presents a significant barrier to downstream applications, including the training of machine-learning models. We present the Open Reaction Database (ORD), an open-access schema and infrastructure for structuring and sharing organic reaction data, including a centralized data repository. The ORD schema supports conventional and emerging technologies, from benchtop reactions to automated high-throughput experiments and flow chemistry. The data, schema, supporting code, and web-based user interfaces are all publicly available on GitHub. Our vision is that a consistent data representation and infrastructure to support data sharing will enable downstream applications that will greatly improve the state of the art with respect to computer-aided synthesis planning, reaction prediction, and other predictive chemistry tasks.

Publication types

• Research Support, Non-U.S. Gov't

Chemical Communications

In-situ synergistic reduced graphene oxide-boron carbon nitride nanosheets heterostructure for high-performance fabric-based supercapacitors.

We develop a new type of heterostructure nanocomposite made of reduced graphene oxide-boron carbon nitride nanosheets (rGO-BCN) by B-C covalent bonds. rGO-BCN nanocomposite delivers a large specific surface and excellent electrochemical properties, which are then constructed into flexible fabric-based high-performance supercapacitor electrodes based on the microfluidic electrospinning technology.

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• Supplementary information PDF (1180K)

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Y. Zhang, Q. Huang, L. Zhou, H. Liu, C. Wang, L. Zhu and S. Chen, Chem. Commun. , 2024, Accepted Manuscript , DOI: 10.1039/D4CC01370K

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