Reaction Network of Layer-to-Tunnel Transition of MnO2

Ye-Fei Li; Sheng-Cai Zhu; Zhi-Pan Liu

doi:10.1021/jacs.6b01768

Elucidating the Weakly Reversible Cs-Pb-Br Perovskite Nanocrystal Reaction Network with High-Throughput Maps and Transformations

10.26434/chemrxiv.12253277.v2 ◽

2020 ◽

Author(s):

Jakob Dahl ◽

Xingzhi Wang ◽

Xiao Huang ◽

Emory Chan ◽

Paul Alivisatos

Keyword(s):

High Throughput ◽

Dynamic Equilibrium ◽

Reaction Network ◽

Substantial Impact ◽

Equilibrium Behavior ◽

Automated Method ◽

Lead Bromide ◽

Different Shapes ◽

Complex Chemistry ◽

Lead Halide

Advances in automation and data analytics can aid exploration of the complex chemistry of nanoparticles. Lead halide perovskite colloidal nanocrystals provide an interesting proving ground: there are reports of many different phases and transformations, which has made it hard to form a coherent conceptual framework for their controlled formation through traditional methods. In this work, we systematically explore the portion of Cs-Pb-Br synthesis space in which many optically distinguishable species are formed using high-throughput robotic synthesis to understand their formation reactions. We deploy an automated method that allows us to determine the relative amount of absorbance that can be attributed to each species in order to create maps of the synthetic space. These in turn facilitate improved understanding of the interplay between kinetic and thermodynamic factors that underlie which combination of species are likely to be prevalent under a given set of conditions. Based on these maps, we test potential transformation routes between perovskite nanocrystals of different shapes and phases. We find that shape is determined kinetically, but many reactions between different phases show equilibrium behavior. We demonstrate a dynamic equilibrium between complexes, monolayers and nanocrystals of lead bromide, with substantial impact on the reaction outcomes. This allows us to construct a chemical reaction network that qualitatively explains our results as well as previous reports and can serve as a guide for those seeking to prepare a particular composition and shape.

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Synthesis and Breakdown of the Universal Precursors to Biological Metabolism Promoted by Ferrous Iron

10.26434/chemrxiv.7178930.v1 ◽

2018 ◽

Cited By ~ 1

Author(s):

Kamila B. Muchowska ◽

Sreejith Jayasree VARMA ◽

Joseph Moran

Keyword(s):

Amino Acids ◽

Chemical Reaction ◽

Metabolic Pathways ◽

Ferrous Iron ◽

Reaction Network ◽

Tca Cycle ◽

Chemical Reaction Network ◽

Metabolic Precursors ◽

Tca Cycle Intermediates

How core biological metabolism initiated and why it uses the intermediates, reactions and pathways that it does remains unclear. Life builds its molecules from CO2 and breaks them down to CO2 again through the intermediacy of just five metabolites that act as the hubs of biochemistry. Here, we describe a purely chemical reaction network promoted by Fe2+ in which aqueous pyruvate and glyoxylate, two products of abiotic CO2 reduction, build up nine of the eleven TCA cycle intermediates, including all five universal metabolic precursors. The intermediates simultaneously break down to CO2 in a life-like regime resembling biological anabolism and catabolism. Introduction of hydroxylamine and Fe0 produces four biological amino acids. The network significantly overlaps the TCA/rTCA and glyoxylate cycles and may represent a prebiotic precursor to these core metabolic pathways.

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A flux-based stoichiometric model of enhanced biological phosphorus removal metabolism

Water Science & Technology ◽

10.2166/wst.1998.0727 ◽

1998 ◽

Vol 37 (4-5) ◽

pp. 609-613

Author(s):

J. Pramanik ◽

P. L. Trelstad ◽

J. D. Keasling

Keyword(s):

Phosphorus Removal ◽

Reaction Network ◽

Bacterial Biomass ◽

Enhanced Biological Phosphorus Removal ◽

Biological Phosphorus Removal ◽

Stoichiometric Model ◽

Aerobic Reactor ◽

Complete Set ◽

Biological Phosphorus

Enhanced biological phosphorus removal (EBPR) in wastewater treatment involves metabolic cycling through the biopolymers polyphosphate (polyP), polyhydroxybutyrate (PHB), and glycogen. This cycling is induced through treatment systems that alternate between carbon-rich anaerobic and carbon-poor aerobic reactor basins. While the appearance and disappearance of these biopolymers has been documented, the intracellular pressures that regulate their synthesis and degradation are not well understood. Current models of the EBPR process have examined a limited number of metabolic pathways that are frequently lumped into an even smaller number of “reactions.” This work, on the other hand, uses a stoichiometric model that contains a complete set of the pathways involved in bacterial biomass synthesis and energy production to examine EBPR metabolism. Using the stoichiometric model we were able to analyze the role of EBPR metabolism within the larger context of total cellular metabolism, as well as predict the flux distribution of carbon and energy fluxes throughout the total reaction network. The model was able to predict the consumption of PHB, the degradation of polyP, the uptake of acetate and the release of Pi. It demonstrated the relationship between acetate uptake and Pi release, and the effect of pH on this relationship. The model also allowed analysis of growth metabolism with respect to EBPR.

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Hidden in its color: A molecular-level analysis of the beer’s Maillard reaction network

Food Chemistry ◽

10.1016/j.foodchem.2021.130112 ◽

2021 ◽

pp. 130112

Author(s):

Stefan A. Pieczonka ◽

Daniel Hemmler ◽

Franco Moritz ◽

Marianna Lucio ◽

Martin Zarnkow ◽

...

Keyword(s):

Maillard Reaction ◽

Molecular Level ◽

Reaction Network ◽

Level Analysis

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Tandem synthesis of tetrahydroquinolines and identification of reaction network by operando NMR

Catalysis Science & Technology ◽

10.1039/d1cy00418b ◽

2021 ◽

Author(s):

Jingwen Chen ◽

Long Qi ◽

Biying Zhang ◽

Minda Chen ◽

Takeshi Kobayashi ◽

...

Keyword(s):

Reaction Mechanism ◽

Complex Network ◽

Reaction Network ◽

Tandem Reactions ◽

Organic P ◽

Design And Optimization ◽

Metal Organic ◽

Tandem Synthesis

The study of the reaction mechanism and complex network for heterogeneously catalyzed tandem reactions is challenging but can guide reaction design and optimization. Here, we describe using a bifunctional metal-organic...

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A CRISPR-Cas autocatalysis-driven feedback amplification network for supersensitive DNA diagnostics

Science Advances ◽

10.1126/sciadv.abc7802 ◽

2021 ◽

Vol 7 (5) ◽

pp. eabc7802

Author(s):

Kai Shi ◽

Shiyi Xie ◽

Renyun Tian ◽

Shuo Wang ◽

Qin Lu ◽

...

Keyword(s):

Nucleic Acid ◽

Genomic Dna ◽

Reaction Network ◽

Clinical Samples ◽

Great Promise ◽

Base Specificity ◽

Nucleotide Mutation ◽

Positive Feedback Circuit ◽

Stringent Target ◽

And Function

Artificial nucleic acid circuits with precisely controllable dynamic and function have shown great promise in biosensing, but their utility in molecular diagnostics is still restrained by the inability to process genomic DNA directly and moderate sensitivity. To address this limitation, we present a CRISPR-Cas–powered catalytic nucleic acid circuit, namely, CRISPR-Cas–only amplification network (CONAN), for isothermally amplified detection of genomic DNA. By integrating the stringent target recognition, helicase activity, and trans-cleavage activity of Cas12a, a Cas12a autocatalysis-driven artificial reaction network is programmed to construct a positive feedback circuit with exponential dynamic in CONAN. Consequently, CONAN achieves one-enzyme, one-step, real-time detection of genomic DNA with attomolar sensitivity. Moreover, CONAN increases the intrinsic single-base specificity of Cas12a, and enables the effective detection of hepatitis B virus infection and human bladder cancer–associated single-nucleotide mutation in clinical samples, highlighting its potential as a powerful tool for disease diagnostics.

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Correction to “Automated Generation and Analysis of the Complex Catalytic Reaction Network of Ethanol Synthesis from Syngas on Rh(111)”

ACS Catalysis ◽

10.1021/acscatal.1c00606 ◽

2021 ◽

pp. 3351-3351

Author(s):

Tangjie Gu ◽

Baochuan Wang ◽

Shuyue Chen ◽

Bo Yang

Keyword(s):

Catalytic Reaction ◽

Reaction Network ◽

Automated Generation ◽

Ethanol Synthesis

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An In-Depth Process Model for Fuel Production via Hydrothermal Liquefaction and Catalytic Hydrotreating

Processes ◽

10.3390/pr9071172 ◽

2021 ◽

Vol 9 (7) ◽

pp. 1172

Author(s):

Leonard Moser ◽

Christina Penke ◽

Valentin Batteiger

Keyword(s):

Process Model ◽

Reaction Network ◽

Hydrothermal Liquefaction ◽

Process Chain ◽

Fuel Production ◽

Model Compounds ◽

Point Distribution ◽

Process Step ◽

Elemental Analyses ◽

Made In

One of the more promising technologies for future renewable fuel production from biomass is hydrothermal liquefaction (HTL). Although enormous progress in the context of continuous experiments on demonstration plants has been made in the last years, still many research questions concerning the understanding of the HTL reaction network remain unanswered. In this study, a unique process model of an HTL process chain has been developed in Aspen Plus® for three feedstock, microalgae, sewage sludge and wheat straw. A process chain consisting of HTL, hydrotreatment (HT) and catalytic hydrothermal gasification (cHTG) build the core process steps of the model, which uses 51 model compounds representing the hydrolysis products of the different biochemical groups lipids, proteins, carbohydrates, lignin, extractives and ash for modeling the biomass. Two extensive reaction networks of 272 and 290 reactions for the HTL and HT process step, respectively, lead to the intermediate biocrude (~200 model compounds) and the final upgraded biocrude product (~130 model compounds). The model can reproduce important characteristics, such as yields, elemental analyses, boiling point distribution, product fractions, density and higher heating values of experimental results from continuous experiments as well as literature values. The model can be applied as basis for techno-economic and environmental assessments of HTL fuel production, and may be further developed into a predictive yield modeling tool.

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